Reliability and Life Cycle
Cost/Profit Assessment of
Intelligent Well Systems
Diploma thesis for
stud. techn. Knut Eivind Borg
February 2001
Faculty of Marine Technology
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Preface
This project is carried out within a four month period of time between September 2000 and January 2001 by Stud. Techn. Knut Eivind Borg, student at the department of Marin Technology. Area
of specialization: Reliability/subsea engineering. This report is my final thesis for my engineering
graduation at Norwegian University of Science and Technology. The name of the study is: “Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems”.
This report marks the end of my studies in Trondheim. The four and a half years have been a great
experience both professionally and socially.
I would like to thank my supervisor professor Marvin Rausand. His keen interest for the subject
and for my work has been very inspiring. Through our weekly meetings he has challenged my
findings and thereby improved the quality of the report. He has helped me applying reliability theory for the given problem.
I would also like to thank my other two supervisors: Geir-Ove Strand at ExproSoft and Hans Peter
Jenssen at SINTEF. They have thougth me about the system and the challenges and benefits
involved with intelligent wells. They have given me the time I needed and answered all my questions in order for me to get a clear overview of intelligent wells.
I had the opportunity to use the MAROS Software at ABB in Oslo. My contact person in ABB,
Endre Willmann, helped me during one week to establish a MAROS model. This expanded the
context of the report. I am very grateful for his input.
In order to understand the possibilities of dynamic programming I needed to seek expert information. I would like to thank Stud. Techn. Petter Fornæss for the very constructive discussions about
dynamic programming and Cand. Scient. Fredrik Borg for helping me develop a small computer
program.
At last I would like to thank Kristin for all the support and for her endurance throughout the
project.
Trondheim, 18. January 2001
______________________________
Knut Eivind Borg
I
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Executive summary
The aim of the study was to develop a life cycle cost/profit (LCP/LCP) model to compare cost/
benefit of intelligent wells with traditional concepts. During the last few years it has been an
increased focus on advanced completion systems, so-called intelligent wells. The intelligent wells
have the potential for a more effective drainage of the reservoir than traditional wells, but this
potential may not be reached due to reliability problems.
The SCRAMS system supplied by PES/Halliburton was chosen to represent a typical intelligent
well system. Still, the techniques and methods used for this project are general and easily adaptable to other systems.
A part of the project was to identify potential technical and operational problems during the life
cycle of the SCRAMS system. The findings are documented in HAZID and FMECA work sheets.
These findings showed that the intelligent well consists of a relatively simple structure and is easy
to model. Today the main problem is to find adequate failure data due to the short track record.
The challenges with the LCC/LCP modelling are:
• Not all the components are required for the whole design life
• The actions taken and consequences are time dependent
• The possible repair and restoration options have distinct effects on the residual lifetime.
There are some main differences between a traditional concept and an intelligent well. The intelligent well may:
• Produce from different zones
• Increase the recovery factor
• Accelerate the production.
The challenges and differences listed must be incorporated in the cost breakdown structure and in
the production regularity measure used for an intelligent well system.
A cost breakdown structure was defined for a typical intelligent well system. It is based on the
NORSOK O-CR-002 standard. Some modifications were required for the operational (OPEX) and regularity (REGEX) expenditures. The REGEX must consist of one category for deferred production and one category for each producing zone. This is because a failure may have different
impact in different zones. The cost for unscheduled events should also be moved from the OPEX
to the REGEX. Scheduled intervention is removed from the OPEX because an intelligent well do
not have scheduled interventions.
When an intelligent well is compared with a traditional concept it is the LCP that must be compared. This is due to the differences in total production between the new and the traditional concepts. A new group, Production Income (PROIN) was therefore included in the CBS structure.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Different measures for production regularity were discussed. The suitable measure is defined to be
the summation of the actual production from each zone related to the maximum production:
Pr od AIntelligent Well =
Actual Pr oduction Z1 + Actual Pr oduction Z 2 + Actual Pr oduction Z3 + ..
Maximum theoretic production
This measure is found applicable both for oil and gas wells.
The real challenge is to quantify the production regularity. Three different approaches (analytical,
stochastic simulation and dynamic programming) were evaluated in order to quantify the production regularity. To assess the stochastic simulation the computer program MAROS was used. A
common feature for all the approaches is that a lot of pre work is required. The most important
inputs are:
• Definition of the production profiles for the different zones
• The impact upon production for the different types of failures
• Intervention strategies throughout the field life.
The ability of the approaches to be combined with decision trees was also evaluated and the main
findings are summarized in the table below.
Approach
Combination with decision tree
General comments
Analytical.
No.
Only applicable for very simple
scenarios.
Stochastic simulation
(MAROS).
Yes, but only possible to simulate
one scenario at a time.
Possible to model time dependent
failures and different maintenance
strategies.
A full model should be implemented and evaluated.
Dynamic programming.
Yes. When a pattern for the decision tree is defined it is easy to
implement complex structures.
Offers the required functionality
for a simulation.
A small computer program is written but a full program should be
developed and evaluated.
A detailed LCC/LCP model for an intelligent well was not developed during the project but a general layout and the required input data were defined.
III
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Preface ................................................................................................................................ I
Executive summary ......................................................................................................... II
1.
Introduction..............................................................................................................1
1.1
1.2
1.3
1.4
2.
Intelligent wells........................................................................................................4
2.1
3.
3.2
Identification of potential failures......................................................................................17
HAZID ...............................................................................................................................18
Procedure HAZOP.............................................................................................................18
Failure Mode and Effect Critical Analysis (FMECA).......................................................19
4.4.1
Assumptions for the FMECA analysis ................................................................20
4.4.2
Definition and delimitation of the system ..........................................................20
4.4.3
System Breakdown and complete list of components for each sub system .......21
4.4.4
The main function of the system .........................................................................21
4.4.5
Operational modes of the system ........................................................................22
4.4.6
Detection of failure.............................................................................................22
4.4.7
Review of system functional diagram and drawings...........................................23
4.4.8
Example of FMECA work sheet .........................................................................23
Cost Breakdown Structure (CBS)..........................................................................24
5.1
5.2
5.3
5.4
5.5
6.
SCRAMS overview ...........................................................................................................12
3.1.1
Horizontal vs. Conventional trees .......................................................................13
SCRAMS components......................................................................................................13
3.2.1
Dual flat pack umbilical ......................................................................................13
3.2.2
HF Packer ............................................................................................................14
3.2.3
Inflow device .......................................................................................................14
3.2.4
ICV Sliding Sleeve ..............................................................................................16
Failure and operational analysis ............................................................................17
4.1
4.2
4.3
4.4
5.
Typical Intelligent Well System ..........................................................................................5
2.1.1
Inflow Control Device...........................................................................................5
2.1.2
Sensor for permanent monitoring..........................................................................7
2.1.3
Packer (Zone isolation)..........................................................................................8
2.1.4
Umbilical ...............................................................................................................8
Typicall Intelligent Well System ...........................................................................11
3.1
4.
Background..........................................................................................................................1
1.1.1
Drivers for Intelligent Wells..................................................................................1
1.1.2
Use of Downhole Data ..........................................................................................1
Objective..............................................................................................................................2
Limitations ...........................................................................................................................2
Report Structure...................................................................................................................3
Capital Expenditures (CAPEX) ........................................................................................25
Regularity Expenditures (REGEX) ...................................................................................25
5.2.1
Deferred production............................................................................................26
5.2.2
Reduced production.............................................................................................26
5.2.3
Interventions for an intelligent well ....................................................................27
Operational Expenditure (OPEX)......................................................................................28
Life Cycle Profit (LCP) ....................................................................................................29
Summary of Cost Breakdown Structure (CBS) .................................................................30
How to measure Production regularity? ..............................................................31
6.1
Definition of production regularity....................................................................................31
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
6.2
6.3
7.
Production Regularity Assessment ........................................................................39
7.1
7.2
7.3
7.4
7.5
7.6
8.
Introduction........................................................................................................................39
Failure data for intelligent wells ........................................................................................39
7.2.1
Failure data for typical intelligent Well...............................................................39
7.2.2
Calculation of MTTF for a group of components ...............................................40
7.2.3
Problems associated with failure data .................................................................41
7.2.4
Calculation of cost...............................................................................................41
7.2.5
Cost of deferred production.................................................................................42
7.2.6
Cost of intervention .............................................................................................43
Failures which influence the safety of the Intelligent Well ...............................................43
7.3.1
Analytical solution and stochastic simulation of safety related failures .............43
Production related failures .................................................................................................46
7.4.1
Assumptions for production related failures .......................................................47
7.4.2
Analytical approach.............................................................................................49
7.4.3
Dynamic programming........................................................................................53
Simulation of both types of failures..................................................................................54
7.5.1
Dynamic programming........................................................................................54
7.5.2
Stochastic simulation (Monte Carlo simulation).................................................55
Validity of methods described to other systems ................................................................60
LCC model.............................................................................................................61
8.1
8.2
9.
Different measures for production regularity ....................................................................32
Production regularity for an intelligent well system..........................................................33
6.3.1
Item availability for an intelligent well system ...................................................34
6.3.2
System Availability for an intelligent well system .............................................34
6.3.3
Safety availability for an intelligent well system ................................................35
6.3.4
Production availability for an intelligent well system.........................................36
General features .................................................................................................................61
Layout of a LCC/LCP model.............................................................................................61
Conclusions and recommendations .......................................................................62
9.1
Recommendations for further work ...................................................................................63
Appendix A: HAZID and HAZOP results ...................................................................66
Appendix B: FMECA results .........................................................................................71
Appendix C: Calculation of MTTF ...............................................................................91
Appendix D: Calculation of expected costs for safety related failures ......................98
Appendix E: Dynamic programing and analytical approach for prod. failures ....104
Appendix F: Acronyms and Abbreviations ................................................................110
References ......................................................................................................................111
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
1. Introduction
An intelligent well completion system is a remotely operated downhole device that contains sensors for observing, and inflow valves for controlling, the well on real time basis. The main advantage of the intelligent well is that it enables the operator to reconfigure the well remotely, based on
simulation and early detection of undesired well condition.
1.1 Background
SINTEF Petroleum Research has initiated a strategic research program on technical and operational aspects of intelligent well systems.
The concept of smart-or intelligent wells is relatively old. It appeared first in the early 1990s but
the technology used for the intelligent well completion has been intensive developed. The goal is
to realize the potential of advanced well completion but the full benefit is still not achieved.
The first system offered to the market was the so called SCRAMS system [22]. It was available in
1996, and installed at Saga’s Snorre tention-leg platform in the North Sea, Norway [27].
Today, there are a range of systems available. In general they include remotely controlled valves
with zonal isolation and monitoring functionality.
The intelligent wells have the potential to give a fair more cost-effective drainage of the reservoir
than traditional wells, but might also cause significant reliability problems. At the current stage of
the development it is important to document the reliability of the new concept, and to develop life
cycle cost/profit (LCC/LCP) models to compare the cost/benefit of the new concept with traditional concepts.
1.1.1 Drivers for Intelligent Wells
There exists different drivers for intelligent wells. The most important driver is the potential
increase and optimizing of reservoir recovery, but there are also other potential benefits of an intelligent well system [27]:
• Minimizing or eliminating the need for well intervention
(This will lead to increased safety)
• Accelerating production
• Providing production trend data
• Providing quality reservoir data for support of total field development or field extension
scenario assessment.
A more detailed presentation of the drivers is given in [4].
1.1.2 Use of Downhole Data
The downhole data and temperature monitoring are both used to determine, in real time, information about the well flow. The data gathered is used for optimizing the recovery from the reservoir.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Today it is not possible to take full advantage of the data because proper analyzing tools do not
exist.
1.2 Objective
The main objective for this report is to develop an LCC/LCP model for a typical intelligent well
system. In order to design the model the following steps were carried out:
a) Description of a typical intelligent well system.
A description of a typical intelligent well system is required in order to understand the
system and how it should be modelled.
b) Identification of reliability problems for the chosen intelligent well system.
In order to design an LCC/LCP model an in depth understanding of the operational and
technical promblems is required. An FMECA and a HAZID analysis were conducted
in order to understand the system and the reliability issues involved.
c) Definition of a dedicated cost breakdown structure and a suitable production regularity
measure.
The configuration of the intelligent well differs from the traditional concepts and therefore it is necessary to define a dedicated cost breakdown structure and a production
regularity measure.
d) Evaluation of different approaches regarding their ability to quantify the measure
defined for production regularity (item c) with and without decisions involved.
Various approaches are assessed and evaluated regarding their ability to quantify the
measure defind for production regularity. The methods evaluated are analytical-, stochastic simulation-, and dynamic programming. During the life of an intelligent well, a
number of decisions may be required. Therefore the possibility for combining the different approaches with decision trees should also be evaluated.
1.3 Limitations
The report focus on one single well and it is assumed that the well is a subsea well. The boundary
is set above the wellhead. The surface based equipment is therefore left out in the report.
It is not the intention to fully describe the potential technical and operational problems. This will
be too comprehensive. The objective is to assess the components not presented in a traditional system.
An LCC model requires different types of input data. The data presented in this report only serves
as example values and may not reflect the real values.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
1.4 Report Structure
The structure of the report is defined in table 1.
TABLE 1. Structure of the report
Chapter
Description
1
Introduction and backgrund.
2
Presentation of the concept of intelligent well systems. The available concepts are presented together with the main component groups.
3
Definition of the example case for the intelligent well. The SCRAMS system is chosen
as an example and described in detail.
4
Evaluation of the technical and operational problem. The methods used are HAZID,
procedure HAZOP and FMECA.
5
Definition of a dedicated cost breakdown structure for an intelligent well.
6
Discussion of production regularity and definition of a suitable measure for an intelligent well system.
7
Discussion of different approaches (analytical, dynamic programming and stochastic
simulation) for quantifying the production regularity. The measure defined in chapter 6
is used.
8
A brief description of an LCC/LCP model for a general intelligent well.
9
Conclusions and recommendations for further work.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
2. Intelligent wells
There are several intelligent well systems available. The present status for different systems is presented in table 2.
There are 17 installations producing from in total 39 zones.
TABLE 2. Current status for Intelligent Well system (source SINTEF)
Operator
System and zones
Well and application
Installed
Saga
1 zone/
Production control
August 1997
SCRAMS
Platform
1 zone/
Production control
SCRAMS
Platform
4 zones/
Production control
SCRAMS
Platform
4 zones/
Production control
SCRAMS
Platform
3 zones/
Production control
SCRAMS
Platform
3 zones /
Production control
Camco/Sch TRFC-H
Platform
Camco WRFC-H, 1
valve unit
Control of gas flow for
gas lift in oil production well
1998 to 2000
2 zones /
July 2000
Baker HCM
Production control
Platform
4 zones/
Production control
May 1999
SCRAMS
Platform
4 zones/
Production control
SCRAMS
Platform
2 zones/
SCRAMS
Gas lift valve and
chemical diverter
(special design)
Subsea 829 m
1 zone/
Gas lift valve
SCRAMS
Subsea 848 m
2 zones/
Injection control
SCRAMS
Platform
PES mini-hydraulic
one zone
Production control
November 1998-
3 platform wells
January 1999
Norsk Hydro
Statoil
Agip
Maersk
Shell
April 1998
August 1998
November 1998
Summer 1999
May 2000
April 2000
January 1998
March 1998
September 1998
3 land wells
Baker HCM single
units
Production control
land wells
August 1999
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
TABLE 2. Current status for Intelligent Well system (source SINTEF)
Operator
System and zones
Well and application
Installed
British Borneo
PES direct hydraulic
one zone
Production control
April-July 1999
Camco: 3 WRFC-H,
Production control in
oil wells, land
BP Amoco
1 TRFC-E
Subsea 965 m
January 1999
September 2000
The SCRAMS system is the system with the longest track record with 10 installations.
2.1 Typical Intelligent Well System
The main objective of the intelligent well is production optimization. This is the common objective for all systems present on the market although they use different solutions to fulfill this objective. Still, it is possible to divide all systems into five component groups [13].
The different component groups are:
• Surface control device
• Surface controlled downhole installed Inflow Control Device
• Sensors for permanent monitoring for real time well condition measurements
• Zonal isolation pack off-devices
• Umbilical.
The component groups included in this diploma thesis are presented in sections 2.1.1- 4.
The Surface Control Device is not included in this study, and therefore no further presentation of
this equipment is given.
2.1.1 Inflow Control Device
The heart of the intelligent well is the inflow device that enables the intelligent well to manipulate
the flow from the different zones. The flow is guided from the annulus to the tubing through a variable opening. This could both be an integrated part (sliding sleeve) of the tubing or it could be an
annulus valve (side pocket mandrel).
With regard to the downhole chokes or sleeves, there are three main features that distinguish the
different technical solutions [5]:
• Actuation method
• Isolation (on-off) or multi-position choke capability
• Tubing or wireline retrievable.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Actuation Method
The actuation method is the method used for moving the inflow device. There are currently five
different types available [4] and the characteristic for each is presented in table 3:
TABLE 3. Different types of actuation methods and their characteristics
Type
Motive force
Control
Data transmission
Mechanical
None
None
None
Hydraulic
Hydraulic
Hydraulic
None
Electric/Hydraulic
Hydraulic
Electric
Electric
All electric
Electric
Electric
Electric
Fiber optic
Hydraulic
Electric
Electric
The “Mechanical” option was the first actuation method available.
Today it is possible to combine Thru' flow Line technology and downhole tractor devices with
non-hardware communication techniques and this combination could therefore work well without
well interventions.
Direct hydraulic power downhole is well known technology in the oil industry. It has been extensively used for sub-surface safety valves. The performance is well documented and is public available from organizations such as SINTEF. The challenges involved are well understood. Hydraulic
systems may easily deliver large amounts of power, if required. The main disadvantage is the
dynamic control seal system reliability in deeper wells, and this is especially a problem for sub-sea
application.
The “electro hydraulic” option combines electric and hydraulic power. Electric powered solenoids
are used to direct hydraulic pressure from the surface control unit to shift the downhole tool in
either the closing or opening direction. This enables the choke or sleeve to stop in all positions for
true multi-position choking capability.
Electric powered downhole safety valves were developed during the early 80's but were disregarded because of problems with, among other, power cable connections. The power connector
technology has advanced and the pure electric alternative is a viable alternative. With further
development of other downhole devices, such as EPS’s and downhole separators, it is very likely
that the pure electric system will be more attractive in the future.
There are on-going efforts in the industry to bring electrical power to platform based and subsea
wellhead valve actuators, as well as overall electrically powered subsea control system.
The fiber optic alternative is currently only used by Sensor Highway. The system is an unique
optical powered actuator for use in remote and hostile environments [What should I refer to?].
Retrieval method and choke capability
All the systems deployed today are tubing integrated. They are therefore retrieved and installed as
an integrated part of the tubing. The different suppliers have different solutions available and they
vary ,i.e., in the number of valve positions and how they regulate the flow.
The current flow control valves today are:
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
• Sliding sleeves (on/off, multi-position)
• Needle choke valve (continuous)
• Caged sleeve choke valve (continuous).
The item with longest experience is the sliding sleeve. It offers larger inflow area than the other
options. This is because it is integrated as a part of the tubing. One other advantage with sliding
sleeves is that it is easy to override a jammed sliding sleeve.
In the industry today there is a drive to develop new control valves and to improve the performance of existing inflow devices. A continuous sliding sleeve has already been developed [9].
This will improve the control ability for the inflow device but it is also important to bear in mind
that an infinite number of positions might not be necessary.
In the future it is likely that the design will depend on the location in the completion. The sliding
sleeve will also be used extensively [13]. The main reasons are the large inflow area and because it
is tubing integrated.
2.1.2 Sensor for permanent monitoring
Real time monitoring of the flow is not a new technology for the oil industry. Permanent downhole
gauge systems have been installed in several hundred oil and gas wells to date [30]. The objective
of these systems is to avoid wireline-conveyed downhole surveys. These systems therefore already
have a track record and many of the “early design” failures are removed.
In the Intelligent Well system the information from the sensors is fed into a surface computer for
analysis. The processed information is used as a decision base for flow manipulation in the well.
The sensors require electricity and therefore most of the systems on the market require electricity,
but it is possible to combine a pure hydraulic system with a separate sensor package. All the electro/hydraulic and pure electric system has incorporated the sensors in the inflow device.
Today there exist sensors to measure different flow parameters:
• Flow
• Water Cut
• Temperature
• Pressure.
In addition to the flow measure sensors, position measure sensors for the inflow device are common. The different systems include different sensors.
Influence of temperature
One important parameter for the reliability of downhole sensors is temperature. The wear-out time
for sensors decreses when the temperature oncreases. Today all electrical components are tested
and qualified for 125 oC. Survival analyse of single Permanent Quartz Gauges (PQG) show that
they have a 90% 5-year survivability for less than 100 oC, but from 100 oC to 155 oC, the 5-year
survivability may drop to about 50% [32].
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
SINTEF Electronics in Oslo, Norway is currently testing high temperature resistant electronic
components for downhole survey but they still have no qualified electronic components.
Another solution is to lower the working temperature for the sensors. PES has developed a cooling
device for their SCRAMS system called Thermo Electric Cooling (TEC). This lowers the temperature with between 10 - 20 degrees, but there are some drawbacks with the system: the device is
relatively power consuming and there are some main concerns with the TEC reliability.
2.1.3 Packer (Zone isolation)
The packer is used to mechanically isolate the production from different zones. Today there are
different solutions even if the main functions are the same [22]:
• Provide a tubing to casing seal and pressure barrier
• Separate zones and laterals from the main well bore
• Accommodate compressive and tensional tubing loads
• Provide feed through for the required hydraulic, electric and fiber optic lines.
For all the systems there are installed protector clamps, or other protection devices are installed
above and below the packer for protection of hydraulic, electric, fiber optic and/or chemical injection control lines.
There are two main types of packers and the difference between them is how they are set:
• Based on traditional Packer technology
This is based on packers used in ordinary wells. The packer is set by pressurizing the
tubing but it is released with a mechanical intervention. The release may be performed
by several different methods. The Camco / Schlumberer packer is an example of this
type of packer.
• Packer set by hydraulic pressure.
This type of packer is set the same way as the traditional, but the release of the packer
is different. The packer is released by bleeding the pressure by the permanent downhole control system. It is therefore not necessary to perform a well intervention to
release the packer.
The Multi-Feed through packer is described in reference [11].
The most important packer is the upper packer because it also serves as a Production Packer.
Halliburton has developed new technology for setting a stand alone Packer utilizing acoustic
pulses through the contents of tubulars [26]. This technology may also be used to develop an
umbilical free intelligent well system in the future.
2.1.4 Umbilical
The type and number of umbilicals required are dependent on the configuration of the system. A
pure hydraulic system have a different requirement than an electro/hydraulic system.The different
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
requirements are presented in table 4. The mechanical actuation does not require any umbilicals
and is therefore left out.
TABLE 4. Umbilical requirements for different types of Intelligent Wells
Type
Hydraulic line (s)
Electrical Line (s)
Fiber Optic line (s)
Hydraulic
Yes
-
-
Electric/Hydraulic
Yes
Yes
-
All electric
-
Yes
-
Fiber optic
-
-
Yes
The main functions of the umbilicals are also different for the different configurations and is summarized in table 5.
TABLE 5. Main function for the different lines in the umbilical
Hydraulic line (s)
Electric line (s)
Pure Hydraulic
Electro/
Hydraulic
Provide downhole motive force
Provide downhole motive force
Provide control
of the downhole
Inflow Device
Provide control
of the downhole
Inflow Device
-
Transmit data to
the surface
Pure Electric
Fiber Optic
-
-
Provide downhole motive force
-
Provide control
of the downhole
Inflow Device
Transmit data to
the surface
Fiber optic line
(s)
-
-
-
Provide control
of the downhole
Inflow Device
Transmit data to
the surface
The problems providing signal and control down to the wellhead is not considered because this is
outside the boundary defined in the report. Today, all the systems use umbilical, although there has
been some research on wireless intelligent wells.
The cables are located outside of the production tubing through the FT Packers and down to the
lowermost flow control valve.
The penetration of the tubing hanger and the wellhead could also be an issue for the development
of the intelligent well. The choice of motive force and level of redundancy defines the number of
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
penetrations needed. The maximum number of penetrations (with full redundancy) is presented in
table 6.
TABLE 6. Maximum number of Wellhead penetrations required for different systems.
Electric lines
Fiber optic lines
Total
penetrations
Type
Hydraulic lines
Pure hydraulic
2 (for each zone)a
-
-
2 (for each zone)
Electro Hydraulic
2
2
-
4
Pure electric
-
2
-
2
Fiber optic
-
-
2
2
a. This may be reduced by using a common return line and one Tubing Encased Conductor (four
zone would require six penetrations) [11].
10
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
3. Typicall Intelligent Well System
It was decided to create a base case of a typical intelligent well system. The base case is required in
order to create the LCC/LCP model. There are many systems on the market today and it is not possible to create an example case which is representative for all systems. The two factors used to
choose which system to use were:
• Complexity of the system
• Track record.
The system should be one of the most complex because a model for such a system is easy to adapt
to a simpler system. The length of the track record is important because a system with longer track
record offers more operational reliability data then a simpler one.
The SCRAMS system is the system with largest number of present installations (table 2) and it is
also the most advanced system on the market today. Therefore SCRAMS was chosen as the example case.
The SCRAMS system components are presented in detail in section 3.2.
The production in the base case is from 4 zones (figure 1). It is assumed that the different zones
have different production profiles (figure 2). The life of the field is 13 years and the well is only
producing oil.
Wellhead
Boundary
DHSV
Zone 1
Feed Through packer
Flat Pack umbilical
Zone 2
Inflow Device
(ICV and Sensor Package)
Zone 3
Zone 4
FIGURE 1. Typical intelligent well system (example used as a base case for the project)
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Test
6000
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
Phase 6
Barrels per day
5000
4000
Zone 1
Zone 2
Zone 3
Zone 4
3000
2000
1000
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Year
FIGURE 2. Production profiles for an intelligent well producing from four different zones
(constructed example).
During the field life the sliding sleeves (valves) will be moved a number of times. The time
between a sliding sleeve is moved until another sleeve is moved is called a phase. The different
phases are drawed in figure 2. The slinding sleeves in the SCRAMS system may have four different positions: closed, 1/3 open, 2/3 open and fully open. It was neccessary to construct a set of
positions and the positions for the constructed example are given in table 7. The production profile
and the sleeve positions are required input for the regularity assessment in chapter 7.
TABLE 7. Position of sleeve in different phases (constructed example)
Zone/
Phase
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
Phase 6
Zone 1
Closed
2/3 Open
Open
Open
Open
Open
Zone 2
Closed
Open
Open
Open
2/3 Open
2/3 Open
Zone 3
Open
Open
Open
Closed
Closed
Closed
Zone 4
Open
Open
Open
Open
Open
1/3 Open
3.1 SCRAMS overview
SCRAMS is a fully integrated adaptive completion system. The system objective is to provide
enhanced reservoir and well management capability. The capability is realized through the implementation of an integrated downhole completion system that facilitates the independent remote
analysis and control from a surface based PC. The production flow or injection is controlled from
any number of zones [9].
Permanently installed hydraulic lines are used in conjunction with solenoids, under electronic control, to selectively manipulate each downhole tool. The electronics located in each zone can detect
and bypass the failed hydraulic cable or I-Wire.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
SCRAMS minimum casing size is 7 inches, with minimum tubing size of 3.5 inches. Minimum
inner diameter (ID) Flow Control Valve is 2.812 inches. The flow port area is variable and specified for each operator.
3.1.1 Horizontal vs. Conventional trees
It is possible to use an intelligent well system with both an horizontal and a conventional christmas
tree. The main difference between the horizontal and the conventional tree is the order of pulling
the tree and the tubing. A conventional tree must be pulled before the tubing is retrieved but the
tubing must be retrieved before the horizontal tree is retrieved.
In an intelligent well system the umbilical is clamped on the outside of the tubing and therefore the
installation process is critical. The number of retrievals of the tubing should therefore be minimized. For a horizontal tree it is necessary to retrieve the tubing each time a tree failure occur.
This is important to consider when the type of tree is chosen.
In the future it is possible that the systems that offers both the advantages of the horizontal and the
vertical tree will be developed.
3.2 SCRAMS components
3.2.1 Dual flat pack umbilical
The system uses permanently installed electric cables (I-wire) to provide power and to communicate with each downhole sensor and well tool. The hydraulic power is provided throug a hydraulic
line. These lines are combined into a single re-enforced flat pack which is clamped with cable
claps at each tubing joint to the completion string (figure 3). Additionally, a redundant pair of
hydraulic and I-Wire is run. The redundancy is configured such that multiple failures in these lines
can be tolerated without any loss of functionality.
Flat Pack
Allen Bolt
Fastener
Cable Clamp
Tubing
Source: PES
Flat Pack
FIGURE 3. Dual flat pack and clamp system for the SCRAMS system (Copied from [21])
The umbilical is fed through the HF packer and down to the inflow device. Arriving at the inflow
device the electrical lines and hydraulic lines are connected to the inflow device with a FMJ connector. The FMJ Jam Nut Fitting is a high performance, metal-to-metal seal connection for use
with standard control line and electrical I-wire. Two flat pack umbilicals are run to each production zone.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
3.2.2 HF Packer
The Hydraulic feed through packer (figure 4) is used for mechanical isolation of the different
zones in the SCRAMS system. The packer is retrievable and is set and released by hydraulic pressure. The main function for the packer is to separate zones and laterals from the main well bore but
it also serves as a feed through for the flat pack umbilical and provides a tubing to casing seal and
pressure barrier. The lines feed through is machined/drilled through the housing.
The packer is set and released by hydraulic fluid guided through a solenoid in the ICV. The
hydraulic fluid is guided into the dual action setting chamber which actuates the double grip slips
into sealed position.The packing element is made of Nitrile/ 70 / 90 HRC.
Dual Electro/Hydraulic Flat
Pack
Packing Element
Dual Action Setting
Chamber
Double Grip
Slips
Dual Electro/Hydraulic Flat
Pack
Wireline or Coiled Tubing
Actuated Release Sleeve
Source: PES
FIGURE 4. HF retrievable production packer in the SCRAMS system (copied from [21])
It is possible to mechanical release the sleeve if the hydraulic system fails. This is a sleeve made of
420 modified 22 Rc.
3.2.3 Inflow device
The inflow device (figure 5) is made directly to the tubing string and is run as an integrated part of
the tubing string. Several inflow devices may be run in any well, all connected to the same dualredundant umbilical system, without the requirement for a multiplicity of individual lines controlling each device.
The inflow device may be divided into two main component groups:
1. Sensors and control
1.1. Electrical control and data transmission
1.2. Sensors for monitoring
1.3. Hydraulic control
2. ICV Sliding sleeve.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Tubing
Electro/Hydraulic
Flat Pack
Sensors
Auxiliary
Hydraulic
Output
Electronics
Solenoid Valves
Position Sensor
Sliding Sleeve
Electro/Hydraulic
Flat Pack
Source: PES
FIGURE 5. Inflow device for the SCRAMS system (copied from [21])
Electric control and data transmission
The SCRAMS system utilizes the SEGNET protocol as a power and communication standard.
This system provides capability of by-passing failed units within the system. The I-wire controls
each device by calling its unique address and request pressure, temperature and position data to be
sent on demand to surface.
The redundant I-wire from the flat pack umbilical is connected to the inflow device and each line
is connected to a separate AEM. The electric output lines are ran from the AEM and down to the
next zone if required. At the bottom of the ICV, the electrical (I-wire) lines are commingled with
one hydraulic line into a flat pack umbilical.
The heart of the control is the actuator electronic modules (AEM). Each inflow device consists of
two AEM for redundancy purposes and they are both installed in separate hydraulic sealed and
pressure containers. Each of them can provide the control functions and digital communication
within the inflow device. They can independently execute sleeve repositioning and network control commands and report the gauge readings and the ICV position status.
Both AEMs are connected to the solenoids and the position sensor but each AEM has its own temperature and pressure sensors.
Sensors for monitoring
Three types of sensors are currently incorporated in the SCRAMS system:
1. Position sensor
2. Quartz temperature sensor
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
3. Quartz pressure sensor.
Both the temperature and the pressure sensor measures data both in the tubing and the annulus.
There is a redundant pair of temperature and pressure sensors and one is connected to AEM A and
one is connected to AEM B. There are only one pair of position sensors.
The sensors are trigged from the AEM. It is possible to switch the sensors to stand by mode or
decide a frequency for when to report data.
Hydraulic control
The redundant hydraulic lines are connected to the inflow device then guided through separate filters (304 L CRES / 90 Microns). After the filter the lines are commingled through a check valve
before a single line leads the fluid to the hydraulic manifold in the inflow device, which directs the
hydraulic fluid to the five solenoid valves present in each inflow device. The solenoids are controlled by the AEM and directs the hydraulic fluid in the desired direction. The different solenoid
valves and their normal position are given in table 8.
TABLE 8. Normal position of the solenoid valves
Name of solenoid
Stand by position
Hydraulic Output A
Open
Hydraulic Output B
Open
Auxiliary Output
Closed
Stroke up valve
Closed
Stroke down valve
Closed
A filter is located in front of all the solenoid valves. The hydraulic output solenoid valves (A and
B) provide the zones below with hydraulic fluid. The auxiliary output solenoid valve is used to set
and release the packer. To release the packer a check valve with connection to the tubing is opened
and the pressure of the packer is bled by directing the fluid through the auxiliary solenoid valve
through the check valve and out in the annulus.
The sleeve is controlled by two solenoid valves: stroke up and stroke down. These two solenoid
valves direct the hydraulic fluid to the actuator piston for the sleeve and moves the sleeve in the
desired direction. A filter is located in front of the actuator chamber. When the sleeve is moved
there is still hydraulic fluid in the acutator chamber on the other side of the seal. This fluid is
guided to the annulus through a filter and a check valve.
3.2.4 ICV Sliding Sleeve
The interval control valve (ICV) is an electro hydraulic, remotely operated downhole valve
(figure 5). The Sliding Sleeve provides the flow control for the reservoir. The sliding sleeve has a
power requirement equivalent of 10 ton, and it contains a wiper ring for mechanical scale purposes.
The sliding sleeve is moved by sending an electrical signal through the I-wire which opens the
solenoid (stroke up/down) for the desired action. The hydraulic fluid is lead to the actuator and the
sleeve is moved to the required position. The SCRAMS system considered for this diploma thesis
offers four position options; fully closed, 1/3 open, 2/3 open and fully open.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
4. Failure and operational analysis
Preliminary risk analysis, or hazard analysis, is a qualitative technique that involves a structured
analysis of the event sequences that could transform a potential hazard into an accident. With this
technique, the possible undesirable events are identified and analyzed separately. For each undesirable event or hazard, possible improvements or preventive measures are found [24].
The analysis must not be initiated without a clear reference to a relevant decision problem, and the
input needed to reach a decition.The risk analysis provides answers to three main questions:
1. What may go wrong? (i.e. hazard identification)
2. What is the likelihood of the happening? (i.e. frequency analysis)
3. What are the consequences? (i.e. consequence analysis)
The result of the analysis provides a basis for determining which categories of hazards should be
looked into more closely. With the aid of a frequency/consequence diagram, the identified hazards
may be ranked according to risk, allowing measures to prioritized to prevent accidents.
4.1 Identification of potential failures
The objective for the failure and operational analysis is to reveal potential failures that may occur
in an intelligent well. It is extremely important to identify the failures in order to design a correct
LCC/LCP model. This proves that it is important to apply a structured approach.
Three different techniques are used to identify the potential failures for the chosen well system:
1. Hazid Identification (HAZID)
2. Procedure HAZOP
3. Failure Mode, Effect and Criticality Analysis (FMECA).
The objective with the analysis results are not to present a complete analysis of the whole system
but to present the different techniques and to assess some important incidents and/or components
with each technique. Table 9 presents an overview of the target for the different techniques.
TABLE 9. Target for each failure analysis technique
Technique
Operational Mode
Comments
HAZID
Installationa
Whole process
(Overall picture)
Procedure HAZOP
Installation
Not enough time to perform the
analysis but a description is provided.
FMECA
Normal operation
Inflow Device assessed
a. After casing is run and until Xmas tree is locked on to the Wellhead
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
The goal is to identify the frequency and the consequence of the failures. This will be used later in
assessment of the regularity and the LCC model. The FMECA is performed into most detail
because the output provided is the most useful for the reliability assessment.
4.2 HAZID
A hazard is defined as a “source of potential harm or a situation with a potential for harm”, where
harm is defined as “physical injury or damage to health, property or the environment”. An accidental event is defined as an “event which may cause harm”. A hazard may thus lead to an accidental
event [1].
The HAZID is used to identify the hazards in a systematic way and to create a sound basis for further analysis. The objective of the analysis is to reveal potential hazards at an early stage, such that
the hazards may be eliminated, minimized or controlled as early as possible in the development
phase. It is important that no hazards are forgotten, but it is possible to disregard hazards because
of:
• Low probability
• Small consequences.
In this thesis the HAZID analysis is performed on the installation phase. The HAZID was initiated
with a brainstorming session and the techniques presented in reference [25]. The intension is to
find broad classes of accidental incidents because the procedure HAZOP will focus more on
details.
The result from the analysis is documented in a specific HAZID work-sheet [25] in appendix A.
4.3 Procedure HAZOP
The procedure HAZOP is quite similar to the HAZOP method. The main difference is that the procedure HAZOP is developed for discrete operations and the HAZOP method is developed for a
continuous processes [24].
The purpose of the procedure HAZOP is to systematically review the procedures for the planned
operations to identify possible hazards and unsolved issues, that might lead to accidents, losses or
delays [24]. The planned operations shall be accomplished safely without exposing personal or
investments to unacceptable risks.
As for the HAZOP there are used a set of guidewords as help in the process. Example of possible
guidewords may be found in [24].
The procedure HAZOP was initialy intended performed the installation phase. Before the analysis
is performed it is necessary to divide the installation procedures into discrete steps. The discrete
steps performed during the installation is presented in figure 6 and details for each step are presented in appendix A.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Preparation
and run tubing
below Zone 4
Run SCRAMS
components
for Zone 4
Run tubing
between zone
3 and 4
Run SCRAMS
components
for Zone 3
1
2
3
2
Run SCRAMS
components
for zone 2
Run tubing
between zone
1 and 2
Run SCRAMS
components
for zone 1
Run tubing
above zone 1
2
3
2
3
Run Tubing
Hanger
Set Packer
Run X-mas
tree
Clean up well
5
6
7
N/E
Run tubing
between zone 2
and 3
3
Run rest of tubing
with DHSV and
Production Packer
4
FIGURE 6. Installation procedures for the SCRAMS system (details presented in appendix A)
It was not enough time available to perform a procedure HAZOP and therefore no results are presented.
For a complete procedure HAZOP analysis each single sub step had to be assessed with the chosen
guidewords. The the possible hazards and operational problems identifiyed would be documented
in a dedicated procedure HAZOP sheet.
4.4 Failure Mode and Effect Critical Analysis (FMECA)
FMECA is used as a technique for systematic failure analysis.
The study is often done as the first step in the system reliability analysis. During the analysis as
many components, assemblies and sub-systems as possible are reviewed to identify failure modes.
The objective is to identify all possible failure modes, causes and effects of such failures.
More detailed information on how to conduct a FMEA (and FMECA) may be found in MIL-STD1629A, British Standard BS 5760 Part 5, and IEC 60812.
One of the most difficult challenges is to have a good and logic distinction between the failure
mode and failure mechanism for the system.
A prestudy for the FMECA was done in accordance with a modified version of the method presented in [12] and is described in sections 4.4.2-7.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
The failure rates, severity ranking and risk reducing measures were decided in cooperation with
SINTEF. The classifications of the failure rates and severity ranking is presented in table 10.
TABLE 10. Classification of Severity ranking and Failure rate in FMECA sheets
Type
Classification
Comments
Severity ranking
Critical
Affects the system functionality
Marginal
may lead to system failure (failure in a redundant
line)
Negligible
Do not affect the system functionality
High
0 - 5 years
Medium
5 - 10 years
Low
10 - > years
Failure rate
An example of the FMECA sheet is presented in section 4.4.8 on page 23 and all the completed
work sheets are presented in appendix B.
4.4.1 Assumptions for the FMECA analysis
The main assumptions for the FMECA analysis are:
• Only one component fails at a time
• All the other components are assumed to work perfectly fine.
The SCRAMS systems have incorporated redundancy. For the FMECA study this is
reviewed such that a failure in only one of the redundant compounds are considered.
I.e. there are two solenoids for hydraulic output and in the FMECA sheet it is assumed
that only one fails at a time.
• Detection of failure
(Presented in section 4.4.6)
4.4.2 Definition and delimitation of the system
It is very important to define a clear system boundary that defines which components are to be
included and not included. Although the system boundary for the chosen intelligent well includes a
complete intelligent well system, only the inflow device is assessed with the FMECA analysis.
The reason for this choice is that the Inflow Device does not exists in a traditional well.
The components included in the FMECA study are:
For the Intelligent well system:
• The Inflow device
• Flow control device
• Sensor and monitoring.
The components left out:
Intelligent Well system:
20
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
• HF Packer
• Umbilical
• Control lines
• Connectors
• Clamps
• System control module (SCM)
• Back up equipment
• SEGNET protocol.
Other well equipment included in the system breakdown but not assessed with FMECA:
• Tubing Hanger
• Wellhead
• Production Packer
• DHSV.
4.4.3 System Breakdown and complete list of components for each sub system
The system breakdown was done in co-operation with SINTEF. The system is broken down to a
level which is suitable for a FMECA study. The normal procedure for an FMECA study is to break
down the system to a level where reliability data are available.
I.e., the solenoid valve is not broken down to an electrical part and a valve part because it is very
difficult to find separate reliability data for each part.
This is in accordance with the taxonomy in the OREDA subsea [20] and Wellmaster [33] database.
The system breakdown is performed in the computer program Visio and is presented in appendix
B. It is important to bear in mind that the system breakdown does not intend to reflect the system
functionality.
4.4.4 The main function of the system
In this step the main function of the system and function(s) of each components are decided.
The SCRAMS serves two main functions:
1. Flow control
1.1. Open and close the inflow
1.2. Isolation of the zones
1.3. Barrier through the top HF packer
2. Flow monitoring
2.1. Monitoring of position
2.2. Monitoring pressure & temperature in the annulus
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
2.3. Monitoring pressure and temperature in the tubing
2.4. Monitoring pressure and temperature inside SCRAMS electronics.
Each of the sub system components functions may be fitted into one of these categories. It is also
possible to define different functions for the different components. This is done for all the components included in the FMECA analysis.
The function(s) of the compounds may be found in the completed FMECA sheets.
4.4.5 Operational modes of the system
There exists several techniques for defining the operational modes for the system. The one used
for this assessment is to consider a function (figure 7) as a black box and to find the associated
operational modes during a brainstorming session.This was performed for all the components
included in the FMECA analysis and is presented in appendix B.
Control
Energy
Functional requirements
Input
Materials
Function
Output
Resources and equipment
FIGURE 7. Analysis of active functions
4.4.6 Detection of failure
In general it is possible to divide failures into two types:
1. Hidden
2. Evident
The evident failures are detected instantly after they occur. When the temperature sensor fails this
will immediately be detected on the PC at the surface. A failure is hidden when the failure is
detected during testing of the equipment. One example of such a failure is “failure to move sleeve
on command”.
There are several methods for detecting errors. For this assessment the following methods are considered:
1. On demand
2. Periodic testing
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
3. Monitoring.
4.4.7 Review of system functional diagram and drawings
This step is used to identify the interrelationship between the various subsystems and to decide the
effect a failure may have on a component in the subsystem and on the system functionality.
Obviously there is a relationship between the different zones in the Intelligent Well installation. If
both of the redundant lines are broken there are no way of communication with the zones below.
There are also other relationships between different components within the boundary for the
FMECA analysis. In table 11 three examples of failures that may affect other components in the
sub system are listed.
TABLE 11. Relationship between the different components in the SCRAMS system
Component A
Component B
Comments
AEM “A”
AEM “B”
Short circuit in one of the
AEMs may result in a short circuit of the other
Control Manifolds (solenoids)
AEM “A” or AEM”B”
Hydraulic fluid leakage may
cause short circuit
Check valve for stroke up/
down
Stroke up/down solenoid valve
Failure in check valve may
cause failure in movement for
the sleeve.
4.4.8 Example of FMECA work sheet
Description of Unit
Description of failure
ID
Compone
nt
Function
Operational
mode
4.1.2.3
Sleeve
Flow
regulation
Closed
(stand by)
Opening
from closed
position
1/3 open
(stand by)
1/3 open
(opening/
closing)
2/3 open
(stand by)
2/3 open
(opening/
closing)
Failure
mode
Failure
mechanism
Detection of
failure
Failure rate
Severity
ranking
Not optimised
production
Low
Marginal
Effect of failure
On components
in the
subsystem
On the system
function
Leakage
Corrosion/
from
Sand production/
reservoir into
Seal failure
tubing
Monitoring
Leakage
from tubing
into
Reservoir
Unwanted
opening of
sleeve
Not opening
on command
Corrosion/
Sand production/
Seal failure
Monitoring
None
Not optimised
production
Low
Marginal
Electric signal
Monitoring
None
Not optimised
production
Low
Marginal
No control
signal/
Scale build up/
Electric signal
Corrosion/
Wear out/
vibration
No control
signal/
Scale build up
Periodic testing /
On demand
None
Low
Critical
Monitoring
None
Not possible to
produce from this
zone
Not optimised
production
Low
Marginal
On demand/
Periodic testing
None
Not optimised
production
Low
Critical
Electric signal
Corrosion/
Wear out/
vibration
No control
signal/
Monitoring
None
Not optimised
production
Low
Marginal
On demand/
Periodic testing
None
Not optimised
production / Loss of
control function
Low
Critical
Unwanted
movement
(opening or
closing)
Not moving
on command
Unwanted
movement
(opening or
closing)
Not moving
on command
None
Comments
FIGURE 8. Example of FMECA work sheet
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
5. Cost Breakdown Structure (CBS)
To be able to calculate the Life Cycle Cost (LCP)/Life Cycle Profit (LCP) it is necessary to establish a dedicated Cost Breakdown Structure for an intelligent well system. This is done to ensure
that all the cost elements, which considerably influence the total LCC of the system, are included
[14]. In the international standard for LCC (IEC 60300-3-3) it is recommended to develop a cost
breakdown structure (CBS) as a basis to the definition of the cost elements in the LCC analysis. A
detailed description of how to define a CBS may be found in [14].
The cost categories for the intelligent well should be based on well configuration and operational
philosophy [27].
LCC models are frequently used as a basis for comparison of different concept alternatives. One
important object with the LCC model is that it should be easy comparable to the LCC model for
traditional concepts. The LCC for ordinary wells are well understood today and there exist recommendations for CBS for the oil and gas industry [18]. The purchase cost are normally between 30 40% of the total costs [14] and this shows how important it is to include the total cost during the
field life and not only the purchase cost.
The CBS defined in this diploma thesis is based on the NORSOK standard O-CR-002 which is frequently used for traditional concepts. This standard describes a common method for LCC calculation. The main reason for this choice is that this CBS is widely recognized throughout in the oil
and gas industry (especially in the Norwegian sector in the North Sea). An ISO standard is also
proposed and the intention is that this standard will replace the NORSOK standard. This shows
that it is important to consider this standard when the Cost Breakdown Structure for the intelligent
well is defined.
The main categories in the NORSOK standard O-CR-002 are:
• Capital Expenditures (CAPEX)
• Operational Expenditures (OPEX)
• Regularity Expenditures (REGEX).
(In the NORSOK O-CR-002 this is only cost of deferred production)
The different categories are presented in detail in sections 5.1- 3.
There are three important differences between an intelligent well and a traditional concept that
need to be considered for the CBS for the intelligent well.
1. The Intelligent Well may produce from different zones
A failure in one zone may only influence on the current zone. The other zones are producing as normal and the consequence is not a complete stop in production but only a
decrease in production.
2. Increased recovery factor
The Intelligent Well will drain the reservoir more effectively and this will increase the
total income from the field.
3. Accelerated production
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
All the costs and income through out the field life is transferred back to a base year. An
accelerated production will therefore increase the income.
These factors must be included in order to perform a comparison of the LCC/LCP between an
intelligent well and a traditional concept. The NORSOK is only a recommendation for a LCC definition.
The completion cost for an intelligent well will differ from a traditional concept. This is reflected
in the CAPEX cost. An intelligent well will increase the recovery factor and thus the net revenue
for the field. This is not possible to consider in the NORSOK model and a new category must
therefore be included to have a correct comparison basis. A detailed presentation is given in chapter 5.4.
5.1 Capital Expenditures (CAPEX)
The CAPEX is defined to be the same as for a conventional well. No new elements need to be
included in order to cover all the cost elements for the intelligent well. It is assumed that all the
design and administration cost and fabrication cost are held by the vendor of the intelligent well
system and is therefore left out.
The elements for the capex:
• Equipment and material purchase cost
• Installation cost
• Commissioning cost
• Insurance spare cost
• Reinvestment cost.
5.2 Regularity Expenditures (REGEX)
This category is the most difficult to determine and the method presented in the NORSOK standard is not suitable for an intelligent well. The NORSOK standard does only include one group for
deferred production. For an ordinary well it is assumed that the production is shut down when a
failure occurs and therefore this breakdown is suitable because this is a static scenario where the
cost of deferred production is easily calculated. For an intelligent well the REGEX should include
all the non planned costs during the field life.
For an intelligent well a failure may result in three different outcomes [27]:
• Immediate intervention and reconfiguration of the well
This will have the same impact as for an ordinary well.
• Intervention is required but not immediate
• No intervention and continue production with the failure (failed item).
This will make the production not optimized, but it is still possible to produce from the
well.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
These factors will affect the REGEX and must be considered for the intelligent well system. The
CBS for the REGEX is divided into two groups, Deferred Production and Reduced Production, in
order to include the different outcomes of a failure.
End of
field life
Production
Production
Profile
Reduced
Production
Time
The cost is deferred to after the end of the field life
FIGURE 9. The difference between reduced production and deferred production
5.2.1 Deferred production
Deferred production occurs when the well is shut down because of an intervention. It is normal to
consider this production to be deferred to after the field life. The method is illustrated in figure 9.
(The black bar is the production which will be deferred). It is assumed that the production is transferred to after the field life. This is because the production not is lost but only deferred. This will
result in a cost element because the income from the production after the field life is lower than to
production earlier in the field life.
A deferred production will occur when all the zones are shut down. This is regardless of the type
of intervention. Therefore it is not necessary to have a category “deferred production” for each
zone.
5.2.2 Reduced production
In an intelligent well the production profile for the different zones are different. Some of the zones
may produce during the whole field life but others may only produce for some years before they
are shut down. This is i.e if the sliding sleeve in zone 2 is stucked in closed position when it is
intended to open.
This is illustrated in figure 9 where it is assumed that one zone has failed but the other zones still
are producing (The grey area represents the gap between maximum theoretical production and
actual production). The “Reduced production” category must be different from the “Deferred production” category because it would be wrong to have a common category for all the zones.
A category “Reduced Production” category is required for each zone to reflect the behavior of the
intelligent well.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
5.2.3 Interventions for an intelligent well
The intervention cost are closely linked to the cost of deferred production. A full workover will
result in a deferred production. Although there is possible to replace a failed item in an intelligent
well system it is not likely to happen. This is because it will be extremely costly and the gain from
the intelligent well will disappear. Therefore it is assumed that no replacement of equipment is
done. On the other hand, it might be an option is to override a sleeve mechanically to fully open
position. This is performed as a mechanical well intervention.There are three different “groups” of
actions, which are possible to be carried out [27]. These are presented in table 12.
TABLE 12. Action Groups for Intelligent Wells
Group
Failure Example
Action
Consequences
Requires immediate
action
Failure causes loss of
a safety barrier or
greatly reduces the
production/injection
ability (premature/
spurious closure of
valves).
Full Workover 1
Well performance
permanently affected
Will require action
Failure causes loss of
function that is
required to enter into
a new phase (failure
to open an inflow
control valve).
Light intervention3
Well performance
temporarily affected
No action performed
Failure causes loss of
a system monitoring
function or has minor
impact on the production/injection
ability
None
Well performance
“not” affected
Partial workover 2
To be able to compare the LCC for this model with a conventional well, the occurrence of other
downhole failures will be included. These incidents will be taken from the WellMaster Subsea
Database.
The intervention cost are normally divided into scheduled and unscheduled interventions.
Scheduled Intervention
One of the main objectives with the intelligent well is to avoid scheduled interventions. The normal scheduled intervention considered includes well logging, reservoir stimulation and other wireline operations. This is not required for an intelligent well and for this reason no scheduled
interventions are considered.
Unscheduled Interventions
There are two types of failures that may lead to an unscheduled intervention:
1. Failures that influence the safety
1.1. LTA (Tubing leakage)
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
1.2. Critical failure for DHSV
1.3. Leakage of upper packer
A failure in this group will result in deferred production
2. Failures that influence system performance
2.1. Open/close inflow valve
2.2. Route the signal through the system
2.3. Monitor the flow parameters with the sensors
A failure in this group may both cause reduced production and deferred production.
The cost for unscheduled intervention should be be inculded in the group “Deferred production”
beacause the REGEX are defined to include the unplanned cost during the field life.
The different cost elements for the REGEX are thus defined to be:
• Deferred production
• Cost of deferred production
• Cost of unscheduled intervention
• Reduced production zone 1
• Reduced production zone 2
• Reduced production zone 3
•…
5.3 Operational Expenditure (OPEX)
The OPEX cost are the planned costs during the field life after the installation.
For an ordinary well the scheduled interventions are included in the OPEX but because there are
no planned interventions for an intelligent well system this is left out.
As for the CAPEX the OPEX are based on the NORSOK C-CR-002 standard [18]. There are some
items, which are not considered in the CBS for the intelligent well:
• Energy consumption cost
• Onshore support cost
The reason for these being left out is that these items will be almost the same for the conventional
and intelligent well.
The items which will be included for the OPEX is therefore:
• Spares and consumable consumption cost
• Logistics support cost
• Insurance cost
• Man hour cost
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
In addition will the following items be included:
• Abandonment and disposal
• Risk (cost of possible rebuilding and cleanup)
5.4 Life Cycle Profit (LCP)
REGEX
(Reduced production)
100%
production
avaliability
LIFE CYCLE PROFIT (LCP)
REGEX
(Deferred production)
OPEX
CAPEX
Installation
Actual
production
availability
Avaliability
INCOME/LOSS
The LCP calculation also includes the income from the production in addition to the costs. The
relationship between LCP and the LCC is given in figure 10.
TIME
End of field life
FIGURE 10. Relationship between LCC and LCP
Figure 10 illustrates that the LCP will increase when the production availability increases. A
higher production will also increase the cost of deferred production. A traditional intelligent well
will therefor have a higher LCC than the traditional concept. If an intelligent well is assessed only
with an LCC analysis it will in many cases be more expensive than a traditional concept.
For the intelligent well it is the increase in production that is one of the main reasons for justifying
the use of the concept. This must be considered in order to have a correct comparison basis for the
traditional concept and the intelligent well.
A new category, Production Income (PROIN) is included in the CBS in order to solve this problem. This category describes the maximum production income for the intelligent well. The PROIN
is closely linked to the LCP (equation 1).
TOTAL INCOME - REGEX - CAPEX - OPEX = LCP
(EQ 1)
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
5.5 Summary of Cost Breakdown Structure (CBS)
In table 13 a summary of the CBS defined for this assessment is given.
TABLE 13. Cost Breakdown Structure for intelligent Well system
CAPEX
OPEX
REGEX
PROIN
- Equipment and material purchase cost
- Spares and consumable consumption cost
- Maximum total
income
- Installation cost
- Logistic support cost
- Commissioning cost
- Insurance cost
- Deferred production
(Unscheduled intervention cost and cost
of deferred production).
- Insurance spare cost
- Man-hour cost
- Abandonment and
disposal cost
- Reduced production
zone 1
- Reduced production
zone 2
- Reduced production
zone 3
-............
This is the recommended CBS for a typical intelligent well system and this is used as a base for the
LCC model described in chapter 8.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
6. How to measure Production regularity?
The production regularity is a parameter used for evaluating production (“Production” in this context includes both production from a single well and from a whole field). In order to determine the
production regularity a regularity assessment is carried out. A description of a general regularity
assessment is given in the NORSOK standard Z-016 [19]. A regularity assessment may both be
qualitative and quantitative.
The objective of this assessment is to establish a suitable measure for production regularity for an
intelligent well system.
6.1 Definition of production regularity
Regularity may be defined as [19]:
A term used to describe how a system is capable of meeting demand for deliveries or performance.
Production availability, deliverability or other appropriate measures can be used to express regularity.
Note: The use of regularity terms must specify whether it represents a predicted or historic regularity performance.
The relationship between some important reliability terms is presented in figure 11 and an explanation of the terms included is given in table 14.
Regularity
Availability
(item)
Uptime
Reliability
Design
Tolerances
Design margins
Quality control
Operating
conditions
etc.
Availability
(system)
Production
Availability
Deliverability
Downtime
Maintainability
Organization
Resources
Tools
Spares
Accessibility
Modularization
etc.
Consequence
of item failure
Configuration
Utilities
etc.
Consequence
for production
Capacity
Demand
etc.
Compensation
Storage
Linepack
Substitution
etc.
Source:NORSOK Z-016
FIGURE 11. The relationship between some important regularity terms (copied from [19])
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
TABLE 14. Definition of important regularity terms (source [19])
Expression
Definition
System availability
The ratio of a period of up time to a period of operation time
Production availability
The ratio of production to planned production, or any other reference level, over a specified period of time.
Deliveriability
The ratio of deliveries to planned deliveries over a specified
period of time, when the effect of compensating elements such as
substitution from other producers and downstream buffer storage
is included
6.2 Different measures for production regularity
The most frequent way to measure for production regularity is to use production availability and
deliverability. Depending on the objective of the regularity analysis, the project phase and the
framework conditions for the project, the following additional performance measures may be used
[19]:
• The proportion of time production (delivery) is above demand (demand availability)
• The proportion of time production (delivery) is above 0 (on-stream availability)
• Number of times the production (delivery) is below demand
• Number of times the production (delivery) is below a specified level for a certain period
of time
• Number of days with a certain production loss
• Resource consumption for repairs
• Availability of systems/subsystems.
In figure 12 three measures for production availability for a field with a contracted volume are presented [14].
Figure 12 a illustrates the actual production.
Figure 12 b illustrates the “production availability” which reflects the actual production. If this
measure is used the “production regularity” will reflect the actual production vs. the maximum
theoretical production.
Figure 12 c illustrates a scenario where the demand for production is reflected in the regularity
analysis. This is called “demand production availability”. This measure is suitable when a contracted amount is to be delivered. I. e. gas wells where a fine has to be paid when the produced
about is below 100%.
Figure 12 d is the on stream availability. This defines the availability to be 0% when there is no
production and 100% else.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Contracted
volume
(a) Actual production profile
Time
100%
(b) Production availability
Time
100%
Time
(c) Demand production availability
100%
(d) On-stream production availability
Time
FIGURE 12. Illustration of different “production availability” measures (copied from [14])
6.3 Production regularity for an intelligent well system
The production regularity defined for the intelligent well system should be based on the NORSOK
Z-016 standard. The reason is that this is the norm today, and thus use of it will make it easier to
compare with models for conventional systems.
Only one separate well is looked at and the production regularity will describe the amount produced from this well. It is therefore not suitable to include the deliverability for the intelligent well
system.
In addition safety availability should be included (section 6.3.3). The production regularity for an
intelligent well system is illustrated in figure 13. Definitions and data for all the different groups
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
(Item, System, Safety and Production Availability) are required in order to define the production
regularity for an intelligent well system. The different groups are described in sections 6.3.1- 4.
Availability
(Item)
Uptime
Availability
(System)
Safety
Availability
Production
Availability
Downtime
Reliability
Maintainability
Design
Tolerances
Design Margins
Quality Control
Operation
conditions
etc.
Resources
Tools
Spares
Accessibility
Modularization
etc.
Decision after
failure
Consequence
of failure
Immediate shut
down
Wait for
intervention vessel
Configuration
Utilities
etc.
Consequence
for production
Capacity
Based on NORSOK Z-016
FIGURE 13. Production regularity for a typical intelligent well system (adaped from [19])
6.3.1 Item availability for an intelligent well system
The structure of an intelligent well system is relatively simple because it is the same equipment in
the different zones.
Today, the amount of reliability data is a critical issue. The SCRAMS is only installed in 10 fields.
The consequence is that the uncertainty of the input data is large. It is therefore very important to
collect reliability data from field experience.
Today there is an alternative to use reliability data from similar components. The challenge is that
the data need to be modified because of diffrences in conditions.
6.3.2 System Availability for an intelligent well system
The system availability for an intelligent well describes the consequence of an failure. In contrast
to the item availability this is more complex because of three aspects [27]:
• The reliability of the completion is not “static” in the sense that all component functions
are not necessarily required for the whole design life
• The possible repair and restore options have distinct effects on the residual lifetime of
the completion
•The action taken and consequences of failure in terms of direct costs and well performance will vary depending on the time the failure occurs, which component fails, the
failure mode and the state of the system and reservoir.
It is neccessary to incorporate these three aspects in the regularity analysis. How this is done is
described in chapter 7.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
6.3.3 Safety availability for an intelligent well system
The safety availability describes the availability for the safety barriers (equation 2).
AS =
Time when safety barrier is working
Time when safety barrier is working + Time when safety barrier is not working
(EQ 2)
For the DHSV it will be the time when it is possible to close the DHSV on demand.
Table 15 illustrates the relationship between production and safety. Production is divided into two
categories (0 and 1) where 0 is zero production and 1 is more than zero production. Safety is also
divided into the same two categories where 1 is safety barrier available and 0 safety barrier not
available.
TABLE 15. Impact of the combination of safety and production related failures on production
Production
Safety
0
1
0
1
Decision required
Decision required
(table 16)
(figure 14)
OK
OK
The two combinations (Safety = 1, Production = 1) and (Safety = 1, Production = 0) do not need
any decision. For the combination (Safety = 0, Production = 0) there may be two outcome depending on the future production (table 16).
TABLE 16. Consequence of safety failure when no production
Future production?
Consequence
Yes
Intervention performed with no impact on
production
No
No intervention performed
Combination (Safety = 0, Production = 1) is the most critical incident. It will require full workover, but there is possible with one out of two alternatives (figure 14)
• Immediate shut down (alternative 1)
This is when the well is shut down immediate after detection of failure. The well is
therefor not producing during the mobilization time of the intervention vessel.
• Wait until intervention vessel is on the spot (alternative 2)
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
This is when the well is producing until the intervention vessel is on the spot. The well
is therefore producing during the mobilization time for the intervention vessel
Alternative 2
Alternative 1
Time required
for intervention
vessel
Mobilisation time
Intervention time
TIME
FIGURE 14. Different actions for safety failures when well is producing
The most beneficial solution is obviously alternative 2, because the amount of deferred/lost production is much less than for alternative 1.
One of the main arguments for choosing alternative 2 is that it is less safe to try to stop the production than it is just to continue production. It is not possible to give a general answer to this problem
but it needs to be decided for each case separately.
6.3.4 Production availability for an intelligent well system
The Z-016 standard does not give a specific definition for production availability, only a description of possible definitions. Therefore it is required to define the production availability for a typical intelligent well.
One single well is evaluated and it is therefore assumed that the given production profile represents the maximum theoretical production. If a failure occurs it is therefore not possible to compensate the loss by boosting the production.
An intelligent well system produces from different zones and the different zones may have different production profiles. The failure may also have different impact on the system:
• Only in the current zone
• For the current zone and all the zones below.
To be able to evaluate these features it is neccesary to measure the production for each zone. The
production regularity is thus the actual production for the different zones vs. the max theoretical
production (equation 3).
Pr od AIntelligent Well =
Actual Pr oduction Z1 + Actual Pr oduction Z 2 + Actual Pr oduction Z3 + ..
Maximum theoretic production
(EQ 3)
There are different implications for oil and gas wells but this measurement is suitable for both
cases. The two cases are discussed below.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Production availability for a gas well
A gas field must produce a contracted amount of gas and a gas well is never completed before this
contracted amount is defined. When the total production is below this amount it is common that
the operating company must pay a penalty. The reliability is therefore very important. A possible
scenario for a gas field is shown in figure 15. There are four fields exporting through the same
export line in the area. Field 4 consists of three different Production Units. PU III has one manifold
where the Intelligent Well is tied in.
Production
Unit II
Production
Unit III
Intelligent Well
Production
Unit I
Field 4
Manifold A
Contracted amount
Field 1
Field 2
Field 3
Export line
FIGURE 15. Possible field scenario for gas field (constructed example)
The contracted amount is the total production from field 4 and not what each well is producing.
The penalty is not paid if one well is down and the total production from Field 4 is above the contracted amount.
For an intelligent gas well system there will not be a contracted amount of gas, from each zone. It
is assumed that the drainage strategy is to produce as much as possible from each zone. This is
done to ensure that the overall production for the field is equal (or larger) than the contracted
amount of gas. A production below the theoretical max production is considered as a loss.
Therefore the production availability for the intelligent gas may be expressed by equation 3
(page 36).
Production availability for an intelligent oil well
For oil producing fields there are no production requirements and therefore the regularity is normally defined as the actual volume produced related to maximum theoretical production (or other
measures). There are no penalty paid and the payment is per barrel. The gain from the intelligent
well is increased production. To reflect this in the regularity, the production availability for an
intelligent oil well should be defined as the actual production related to the theoretical production.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Production
For a single well system it is therefore suitable to use the same measures on production regularity
for oil and gas wells. Figure 16 illustrates the concept for production availability for each zone in
an oil or gas well.
Maximum
theoretical
production
Actual production
Time
FIGURE 16. Production availability for a single intelligent oil or gas well
38
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
7. Production Regularity Assessment
7.1 Introduction
The objective of this chapter is to discuss different methods to quantify the production regularity
using the measure defined in chapter 6. The production regularity in chapter 6 was defined to be
the actual production vs. the maximum theoretical production.
One of the most important input data for the production regularity assessment is failure data. The
first step was thus to collect failure data associated with intelligent wells.
After closely examination of the failures that may occur for a general intelligent well it is recommended to divide the failures into two groups:
1. Safety related failures
The safety related failures are failures that always will cause a stop of production
2. Production related failures
The production related failures are failures that may have several outcomes
It is possible to model these failures separate or together. For the most simple analytical calculations it is recommended to perform separate calculation. For more accurate calculations it is necessary to model these failures together.
Sections 7.3-5 present how the two types of failures may be assessed separately and section 7.5
presents how the two types of failures may be combined in a single simulation.
7.2 Failure data for intelligent wells
Failure data are viable for a correct regularity assessment. The total track record for intelligent
wells are short and therefore the amount of data available is sparse. In addition some of the failures
recorded are design failures that lead to a higher failure intensity than the actual value.
This shows that it is important to have an increased focus on collecting and processing of failures.
Processing of data means the way the data are interpreted. I.e., it is important to record special
information about the data such as:
• Is the failure an early design failure?
• Are there special conditions that may caused the failure (temperature, pressure)?
7.2.1 Failure data for typical intelligent Well
The failure data applied for the SCRAMS equipment are based partly on information from SINTEF and partly on engineering judgement. The failure data used in this assessment are presented in
table 17.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
TABLE 17. Failure data for the SCRAMS system
Group
Failure
MTTF
(years)
Source
Production related failures
Solenoid Valve
114.6
SINTEF
Hydraulic line
114.6
SINTEF
Check Valve
38.5
SINTEF
Electric line
223.8
SINTEF
Sliding sleeve
713.7
SINTEF
Tubing Leakage
56.18
SINTEF (WellMaster phase II
report)
Critical DHSV failure
30
SINTEF (WellMaster phase II
report)
Production Packer
leakage
340.1
SINTEF (WellMaster phase II
report)
Safety related failures
It is important to have a correct interpretation of the MTTF. I.e., the MTTF for the sliding sleeve is
713.7 years. The correct way to interpret this value is for a population of 100 sliding sleeves operating over a period of 8 years (together 800 years) it is expected that at least one of sliding sleeves
will fail. It is not possible to claim that a single sliding sleeve will survive 713.7 years.
7.2.2 Calculation of MTTF for a group of components
For a system of components it may be required to calculate the MTTF for the whole component
group. There are two main different approaches to evaluate the MTTF:
• Analytical approach (presented in appendix C)
• Simulation (used by the CARA software [2])
The ability for the CARA fault tree program to calculate the MTTF was evaluated in order to
obtain the MTTF for a group of components. Fault trees for the block diagrams presented in
figure 21 (page 48) were constructed and evaluated with CARA (appendix C).
CARA provides two different methods to calculate MTTF
1. Kinetic Tree Theory
2. Stochastic Simulation
Each of these methods require that a total mission time is defined. It is recommended that the mission time should be 2 - 3 times the expected MTTF. The mission time was changed to observe the
effect on the MTTF for the two types of failures considered. The correct value for the MTTF is
calculated analytically with MathCad (appendix C).
The results showed that great care must be taken if CARA should be used to calculate the MTTF
for a group of components. It is important that the user has a clear understanding of the methods
used by CARA and their limitations.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
It is recommended that the calculations are performed analytically. This is easily done in computer
programs such as MathCad.
7.2.3 Problems associated with failure data
The accuracy of an LCC model is heavily dependent on the input data. The term “garbage in, garbage out” is a good illustration of the importance of the input data.The validity of the reliability
data are therefore critical for correct calculations. In figure 17 a possible problematic scenario is
illustrated.
The data collection is performed mostly in the early component years. This may lead to a lower
failure rate than the actual failure rate.
0.14
Fauilure intensity
0.12
0.1
Field experience
0.08
Constant failure rate (lamda = 1)
Increasing failure rate (lamda = 2.5)
0.06
Calculated failure intencity
0.04
0.02
0
0
2
4
6
8
10
12
14
16
18
20
Time (Year)
FIGURE 17. Estimation of failure data compared with two types of Weibull distributions
Figure 17 also illustrates the difference between the Weibull distribution and Exponential distribution. The failure intensity for the Weibull distribution starts at zero an increases. In the case of the
Exponential distribution the failure intensity starts at one value larger that zero and is constant.
This is important to bear this in mind when which distribution to use is decided.
7.2.4 Calculation of cost
To be able to add cost from different years during the field life it is necessary to transfer the value
of the cost to a base year. This base year is normally chosen to be year zero. If a nominal cost (Ct)
is assumed constant during the field life, equation 4 calculates the total cost back to the base year.
n
Vp = ∑
t =0
Ct
(1 + i )t
(EQ 4)
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
VP
= Net present vale (value at the “base year”)
Ct
= The nominal cost in year t
n
= Life of the system (in years)
i
= Discount rate
7.2.5 Cost of deferred production
An intervention will require a stop of production. This will create a cost of deferred production and
this should be included in the LCC model.
There are different methods to calculate the value of this deferred production.
Method 1: Assumption of lost production
It is possible to consider the deferred production as lost production. This is correct when the production is lost due to leakage to the environment or when it not is possible to produce after the
defined design life. The cost is calculated with equation 5.
n
 1 
CINDISP = ∑ CPPt ⋅ 
t 
t =0
 (1 + i ) 
CINDISP
= The cost of unavailability (At year zero)
CPPt
= The cost of production loss at year t
n
= Life of the system (in years
i
= Discount rate
(EQ 5)
This method is used i.e. by the MAROS software.
Method 2: Production is recovered after design life
A more correct method when the production not is lost is to assume that the production is recovered after the designed field life. The cost is then the difference in value of production between
year t (when the production is stopped) and year n+1 (one year after the end of the field). Equation
6 illustrates how this is calculated.
n
 1

1
CINDISP = ∑ CPPt ⋅ 
−
t
n +1 
t =0
 (1 + i ) (1 + i ) 
(EQ 6)
Method 3: Assumption of a percentage production loss
A third method is to assume that only a percentage of the deferred production is lost. This is therefore a modification of the first method. It is normal to assume that between 50% and 70% of the
possible production is lost.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
7.2.6 Cost of intervention
To be able to calculate LCC it is necessary to know the time and cost of different intervention
types that may be performed
The numbers used are decided in cooperation with SINTEF and are presented in table 18.
TABLE 18. Cost and duration data for different type of intervention
Type
Mobilization
cost (US$)
Cost
(US$ per day)
Mobilization
time (weeks)
Intervention
time (weeks)
Full Workover
750 000
150 000
2
2
For a complete LCC calculation it is necessary to specify the man hour cost and logistic cost. Here
it is assumed that these categories are included in the mobilization cost and day rates.
7.3 Failures which influence the safety of the Intelligent Well
The safety related failures are failures that influence the safety of the system and therefore require
immediate action. If one of the failures occur a full workover is required and the production from
all the zones is stopped. This may happen upon detection of failure or when the intervention vessel
has arrived on the spot.
In this case it is assumed that the deferred production is lost. The reason is that this would make it
possible to compare the results with the output data from the computer program MAROS
(described in detail in section 7.5.2).
The failures related to safety will have only one of two possible outcomes for the whole system in
each interval:
• Failure (an intervention is required)
• No failure (no action is required).
This is the same for a conventional system and thus existing techniques may be used to estimate
probability of failures and costs related to the safety failures.
It is possible to perform a quick estimate of the cost of the safety failures. The analyical approach
combined with use of simple computer programs is suitable and one approach is presented in section 7.3.1. To evaluate the result from the analytical approach the safety failures were simulated in
the computer program MAROS and the results are also presented in the same section. The results
from the different approaches are almost identical.
7.3.1 Analytical solution and stochastic simulation of safety related failures
The analytical approach is based on the assumption that the different failures are independent and
that one failure will result in a full workover. This may be represented with a serial structure
(figure 18). There are three types of failures considered (Critical DHSV Failure, Tubing Failure
and Production Packer Failure). The critical failures will require a full workover and it is easy to
include other types of failures. For each failure it is necessary to decide if the well should be shut
43
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
down immediately or wait until the intervention vessel is on the spot. In this assessment it is
assumed that the well will be shut down when the failure occurs.
Critical
DHSV failure
Tubing
leakage
Production
Packer failure
....
FIGURE 18. Serial structure of safety related failures
In order to quantify the production regularity it is necessary to know the failure distribution for the
different failures. Here Weibul distribution is assumed. The Weibull distribution is chosen because
it will give a more correct approximation than other distributions such as the exponential distribution. To determine the parameters for the distribution an alpha is assumed and the lambda is calculated with equation 7. The calculations of the failure rates are performed in MatCad and is
presented in appendix D. The main results are presented in table 19.
λ=
1
1
* Γ( + 1)
MTTF
α
(EQ 7)
TABLE 19. Failure rates for safety failures
Item
MTTF (years)
Alpha
Lambda
Critical DHSV failure
25
1
0.04
Tubing leakage
56.18
2.5
0.016
Production Packer failure
340.1
1
2.94E-3
The failure rate for the Weibull distribution is given in equation 8 and the graphical distribution for
the different failures are presented in figure 19.
z (t ) = (α ⋅ λ ) ⋅ (λ ⋅ t )α −1
(EQ 8)
44
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
0.05
0.04
z tubing ( t )
z DHSV( t )
z Packer( t )
0.02
Z s( t )
0
0
5
10
t
0.1
15
20
20
FIGURE 19. Failure distribution for the different failures z(t) and the combination of the failure
Z(t)
When the failure distribution is known it is possible to calculate the probability for failure within
an interval [t1,t2] with equation 9.
t2
∫ Zs ( s ) dt
Pyeari = 1 − e t 1
(EQ 9)
The probability of failure is calculated for each year and the results are presented in table 20.
TABLE 20. Probability of safety related failures for each year
P
1
2
3
4
5
6
7
8
9
10
11
12
13
0.036
0.036
0.036
0.036
0.036
0.037
0.037
0.037
0.037
0.038
0.038
0.039
0.039
Cost of safety failures
When the probability for safety failures is known it is possible to calculate the expected cost with
equation 10.
n
 1 
E[Cost ] = ∑ Ct ⋅ 
⋅P
t 
t =0
 (1 + i ) 
(EQ 10)
The calculation of the expected cost are easily performed with a simple spread sheet in the computer program Excel. An illustration is given in figure 20 and the complete result presented in
appendix D. The expected cost during the is 1.017 mill US$.
45
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
FIGURE 20. Excel sheet for calculation of expected cost for safety failures
Results from MAROS simulation
To evaluate the validity of the analytical approach a MAROS model was created. The model calculated the cost associated with the production stop to 1.005 mill US$. The results are presented in
appendix D.
Lost production compared with deferred production
As previous mentioned there are different methods for calculating cost of reduced production. To
expemplify the difference the consideration of lost production was compared with the assumption
of deferred production.
The difference between considering the production as lost and deferred during a production stop is
presented in table 21. The difference is approximately 15% and illustrates the importance of
choosing which method to use with great care.
TABLE 21. Difference between lost and deferred production
Type
Cost
Lost production
1.017 mill US$
Deferred production
0.865 mill US$
7.4 Production related failures
The production related failures are more complex. This is because the action after a failure is
occurred is not certain and the fact that some failures only will affect the current zone with other
failures will affect the current zone and all zones below. To solve this problem the failures were
divided into two groups (figure 21):
• Failures that only will affect the current zone (Type 1 failure)
This failure group was named: “not possible to move sleeve in current zone”
• Failures that will affect the current zone and all zones below (Type 2 failure)
46
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
This group was named: “not possible to provide electric signal/hydraulic fluid to the
current and the zones below”1.
7.4.1 Assumptions for production related failures
In order to evaluate the regularity some assumptions had to be taken. It is of high importance that
the assumptions do not remove the complexity of the system. The assumptions are presented in
table 22.
TABLE 22. Assumptions for production related failures
Assumption
Justification
Only the failures in table 17 on page 40 are considered
These were the failures available.
The communication between the wellhead - zone
1 and zone n - zone n +1 is the same (figure 21).
This structure is easy to expand if other failures
should be included.
The block diagram for used for “actuation of the
ICV” in each zone is presented is given in
figure 21.
The failures are exponential distributed.
This may be a realistic distribution for a unit during its useful life period [12]
Only one intervention annual is possible.
There are only two failures considered:
Type 1 failure: Not possible to move sleeve
Type 2 failure: Not possible to provide electric
signal/hydraulic fluid to the current and the zones
below.
The two different types of failure are examined at
different intervals:
The failure of the sensors are not considered
because failure of the sensors do not have direct
impact on the production.
The type 2 failure will affect the zones below but
a type 1 failure only will affect the current zone.
Type 1 failure: Only at the beginning of the year
when the sliding sleeve is due to be moved.
Type 2 failure: At the beginning of each phase.
1. It is important to remember that it is not possible to move the sliding sleeve after a type 2 failure has
occurred.
47
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Electric signal and hydraulic fluid between zones
I-Wire between zones
Hydraulic between zones
El. line
Solenoid Valve
Hydraulic line
Check Valve
El. line
Solenoid Valve
Hydraulic line
Actuation of ICV
Solenoid Valve
(Stroke up)
Solenoid Valve
(Stroke down)
Sleeve
FIGURE 21. Block diagram for SCRAMS functionality (simplified)
Consequence for production related failures
A production related failure may affect all zones or just one single zone. The production may only
be reduced if such a failure occurs. This is different from the safety related failures where the production is stopped when this type of failure occurs.
It is therefore required that the effect of not being able to move the sleeve is determined in order to
quantify the production regularity. This means that the effect upon production when it is not possible to move the sleeve must be decided. As an example for this assessment the values given in
table 23 were chosen.
TABLE 23. Effect of type 2 failures on production (percentage reduction from each zone)
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
Phase 6
Zone 1
(shift 1/2)
-
100%
100%
100%
100%
100%
(shift 2/3)
-
-
15%
15%
15%
15%
Zone 2
(shift 1/2)
-
100%
100%
100%
100%
100%
(shift 4/5)
-
-
-
-
20%
20%
Zone 3
-
-
-
10% of
total prod.
10% of
total prod.
10%of
total prod.
Zone 4
-
-
-
-
-
10%
The impact of a type 1 failure and a type 2 failure is different. As an example a failure in zone 1
phase 1 is considered. The different outcome will be:
• Type 1 failure: The production is lost from zone 1
• Type 2 failure: The production is lost from zone 1 AND zone 2.
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
7.4.2 Analytical approach
The most common and simple analytical method for obtaining number of failures (repairs) during
the design life of a system is to apply the homogeneous Poisson process (HPP) where an expected
number of system failures are calculated equation 11.
E[# of system failures during design life] =
TDesign
MTTFs + MTTRs
=
TDesign
MTBFs (EQ 11)
The NORSOK C-CR-002 [18] applies this definition. Some of the restrictive assumptions for this
HPP model are that time between system failures should be independent and identically exponentially distributed.
The analytical approach is not suited for very complex dynamic systems where consequences and
time to occurrences of failures may vary considerably. Still it is possible to use an analytical
approach to evaluate a simple scenario for an intelligent well.
To be able to create a model it is required to have a good understanding of what might happen.
During the project various methods were tried out in order to get an overview over the possible
outcomes for the field.
The first approach was to create a table with all the information (figure 22). The problem with this
approach is that it is very difficult to get an overview and to expand this method to more complex
structures.
Zone
Zone 1
Type of
failure
11
2
2
Phase I
Phase II
Not possible to move
sleeve from closed to
open 2/3 position in next
Not possible open sleeve
from 2/3 open to fully
open position.
Influence on the phase
shift between phase
Influence on the phase
shift between phase
I and II
II and III
Not possible to perform
control or monitoring in
current and zones below
II and III
Phase III
Phase IV
Phase V
Phase VI
None
None
None
None
Not possible to perform
control or monitoring in
current and zones below
Not possible to perform
control or monitoring in
current and zones below
Not possible to perform
control or monitoring in
current and zones below
Not possible to perform
control or monitoring in
current and zones below
None
Influence the data
transmission for all
zones
Influence the data
transmission for all
zones
Influence the data
transmission for all
zones
Influence the data
transmission for all
zones
Influence the data
transmission for all
zones
Influence on the phase
shifts in:
Influence on the phase
shifts in:
Influence on the phase
shifts in:
Influence on the phase
shifts in:
Influence on the phase
shifts in:
Zone 1: I/II and II/III
Zone 2: I/II and IV/V
Zone 3: III/IV
Zone 4: IV/V and V/VI
Zone 1: II/III
Zone 2: IV/V
Zone 3: III/IV
Zone 4: IV/V and V/VI
Zone 1:
Zone 2: IV/V
Zone 3: III/IV
Zone 4: IV/V and V/VI
Zone 1:
Zone 2: IV/V
Zone 3:
Zone 4: IV/V and V/VI
Zone 1:
Zone 2:
Zone 3:
Zone 4: IV/V and V/VI
FIGURE 22. Effect of production related failures upon production
The second approach was to develop a method for illustrating the outcome graphically. This
proved to be a much more effective. It will also be proved later that it is possible to apply this
method on more complex scenarios.
The method is based on an event tree where there is a start point for each zone and the outcomes
from the event tree cover all possible outcomes. A simple example (figure 24) is the best way to
explain the method.
49
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Figure 24 illustrates how it is possible to describe the possible outcomes for a scenario where no
intervention is considered. For each phase it is examined what will happen if a failure 2 has
occurred (This must be examined first because it is not possible to move the sleeve if a failure 2
has occurred). If the zone is not the uppermost it is required to consider the possibility for a of type
2 for the zones above. The failure 2 may have 2 different outcome: s (no failure) or f (failure).
If the sleeve is due to be moved in this phase an evaluation of type 1 failure is performed (only if
the failure 2 have the outcome s). The failure 1 may also have the same outcome as failure 2 (s or
f).
In the next phase the pattern is repeated. This is done until the last phase is reached.
Pattern for a phase where the sliding sleeve is due to be moved
(in current zone)
Phase n
From Phase
n-1
Phase n+1
Z1 2
S
F
2
The survival
probability for
type 2 failures for
the zones above
S
F
1
S
Pattern for a phase where the sliding sleeve is NOT due to be moved
(in current zone)
Phase n
From Phase
n-1
Phase n+1
Z1 2
S
F
2
The survival
probability for
type 2 failures for
the zones above
S
FIGURE 23. Pattern for decision tree
Calculation of availability
It was not enough time to make complete demonstration on how the system availability is calculated for the example illustrated in figure 24. Only the probability of occurrence is calculated. The
MTTF for the two types of failure is calculated analytically and is presented in appendix C. The
main results are presented in table 24.
TABLE 24. MTTF for production related failures
Failure
MTTF (years)
Type 1: Not possible to move actuate sleeve
52.85
Type 2: Not possible to provide electric signal or
hydraulic fluid between zones
24.43
The assumption of exponential distribution gives the reliability (surveyor) function in equation 12.
R(t ) = e− λt
(EQ 12)
50
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
The survival probabilities are presented in table 25.
TABLE 25. The survival probability for the different failures
Phase
Type 1 failure
Type 2 failure
1
0.98
0.96
2
0.96
0.92
3
0.91
0.81
4
0.86
0.72
5
0.83
0.66
The survival probability is applied to each node. The numbers in table 25 are the survival probabilities (s). At each node the value for f = 1 - s. Figure 24 illustrates the pattern for an event tree with
two types of failure and no interventions.
Phase 1
Phase 2
Phase 3
0.04
Not possible to control sleeve in any zones
F
0.02
2
S
0.96
0.08
F
1
Not possible to move sleeve in zone 1
Not possible to control sleeve in any zones
F
S
Zone 1
0.04
2
S
0.98
0.92
Not possible to move sleeve in zone 1
F
1
S
0.96
0.0784
Z1 2
S
Zone 2
Not possible to control sleeve in zone 2 and below
F
0.02
2
S
0.9216
Not possible to move sleeve in zone 2
F
1
S
0.1536
Z1 2
0.98
S
F
Not possible to control sleeve in zone 2 and below
2
S
0.8464
FIGURE 24. Event tree with two types of failure
The complete numbers for the first two zones are given in appendix E.
It is important consider the survival probability for type 2 failures for the zones below the uppermost.
The result for zone 1 and 2 is presented in appendix . From these values it is possible to calculate
the expected cost with a simple spread sheet.
Inaccuracies with the analytical approach
Obviously this model is not very accurate. There are two main weak points for the model
1. The model does only calculate the production related failures. It must be combined
with the model for the safety failures (section 7.3). If a safety failure occurs and the
51
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
equipment is retrieved a failure with the SCRAMS equipment will be repaired at the
same time.
2. The model does not include the possibility of intervention (repair upon failure).
These constrains stress that more complex models should be used in order to quantify the production regularity.
Analytical approach for more complex scenarios
For more complex models it is still possible to calculate the expected cost analytically but it is not
adequate. Figure 25 illustrates a scenario with two types of failure and one type of intervention. It
is the same pattern as for the simple scenario but for each failure it is possible to perform an intervention. This intervention may be a success or a failure.
In this case it is three output nodes which are a success and four output nodes that are failures (If
the sleeve is not is moved the number of output nodes with success is two and failure output are
reduced to two). If it is assumed that the sleeve should be moved two times during the field life the
number of output nodes for this zone is (3^2)*(2^3)+(4*2)*(3*3)= 89 outnodes.
This emphasize the fact that other methods are required if decisions are to be included in the
method.
Not possible to control current zone or zone below
Leave as is
F
F
Cost of full workover, not possible to control any zones
Full workover
2
Cost of heavy workover
S
Not possible to move sleeve in current zone
Leave as is
S
F
Cost of heavy workover, not possible to
move sleeve in current zone
Full workover
F
S
Cost of heavy workover
Each input will
generate three new
output in the next
phase
1
S
Next phase
FIGURE 25. Decision tree with two types of failure and one type of intervention (one phase in one
zone).
52
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
7.4.3 Dynamic programming
An other approach to quantify the production regularity is to use computer based techniques. One
possibility is to use dynamic programming to quantify the production regularity.
Dynamic programming solves problems by combining the solutions to subproblems (“programming in this context refers to a tabular method, not writing computer code). The dynamic programming is applicable when the subproblems are not independent, that is, when subproblems share
subproblems [3].
A dynamic-programming algorithm solves every subproblem just once and then saves its answers
in a table, thereby avoiding the work of recomputing the answer every time the sub subproblem is
encountered.
More information of dynamic programing may be found here [3].
When a program that uses dynamic programming is run, information obtained during the session
are stored (and updated if desirable) in a table. It is the possible update of the table that makes it
suitable for the regularity quantification. For this assessment this table is exemplified in table 26.
For each phase in each zone there is an object which consists of the required information.
TABLE 26. Structure of table for dynamic programming in thesis
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
Phase 6
Zone 1
O11
O12
O13
O14
O15
O16
Zone 2
O21
O22
O23
O24
O25
O26
Zone 3
O31
O32
O33
O34
O35
O36
Zone 4
O41
O42
O43
O44
O45
O46
A simple computer program was written to solve the simple problems with two failures and no
intervention presented in section 7.3.1. The computer language used is Perl and the source code is
given in appendix E.
The input data for this program is the probability of survival for the two types of failure and when
(in time) the phase shifts will take place.
The program is runs through the objects zone by zone starting with zone 1. For each phase the survival probability for type 2 failure is stored in the object. I.e., the survival probability for zone 2 in
phase 3 is stored in object O23. This information is used for zone 3 in phase 3 during the next run
(stored in O33).
The results provided by the program is identical with the results obtained from the analytical calculations.
The computer program written program is a very simple program but it is easy to expand in order
to solve more complex structures. Figure 26 illustrates a scenario where it is possible to perform an
intervention if a type 2 failure occurs. To solve this problem with dynamic programming the
objects must be updated through out the simulation (i.e. if a full workover is performed and it is a
success, the survival probability for type 2 failures must be updated for all the remaining phases.
53
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Not possible to control current zone or zone below
Leave as is
F
F
Cost of full workover, not possible to control any zones
Full workover
2
S
Cost of heavy workover
S
Not possible to move sleeve in current zone
F
Each input will
generate three new
output in the next
phase
1
S
Next phase
FIGURE 26. Patter for decision tree with two types of failure and one type of intervention
When more complex scenarios are to be assessed it is required to draw the decision tree first and
then design the algorithm for the computer program.
7.5 Simulation of both types of failures
In order to have a more accurate model it is necessary to combine the production related and safety
related failures. It is possible both to perform this analysis with dynamic programming and by the
computer program MAROS that uses Monte Carlo Simulation.
7.5.1 Dynamic programming
The dynamic programming approach uses the same techniques as presented for the production
related failures. The safety failures will be evaluated annually (or more frequent if desirable) and
the production related failures will be evaluated each time a phase shift occurs. This pattern is
described in figure 27. No attempt is done to implement this structure but it is discussed with Stud.
Techn. Petter Fornæss at NTNU and Cand. Scient. Fredrik Borg at UiO (both with a degree in
computer sciences). They both claim that it is fairly easy to implement these structures as long as
the pattern is described.
54
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Annual basis
Not a repeated
frequency
Safety failure
Production related
failure
Year without a phase shift
Year with a phase shift
Next year
Next year
F
F
F
Safety
failure
F
Full workover
S
Full workover
Safety
failure
S
Leave as is
S
S
F
F
Prod.
failures
Full workover
S
2
S
Leave as is
F
F
Full workover
S
1
S
FIGURE 27. Pattern for combination of safety and production related failures
7.5.2 Stochastic simulation (Monte Carlo simulation)
To evaluate stochastic simulation the computer program MAROS was chosen as an example.
More information about MAROS and Monte Carlo simulation may be found in reference [24].
In order to quantify the system regularity the advanced features of MAROS must be used.
MAROS uses reliability block diagram in order to describe the system (figure 28). Under each
block the relevant failures and their impact on production and production profile (figure 29) is
described.
The production profiles are input with the type 1 failures and the impact of type 2 failures on production must be calculated for each phase and zone (i.e. the total impact of a type 2 failure in zone
2 in will impact on zone 2, 3 and 4).
55
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
FIGURE 28. Block diagram in MAROS
FIGURE 29. Failures associated to each block in the block diagram in MAROS
It is assumed that the failure rates are exponential distributed and therefore the failure intensity is
constant. In MAROS it is possible to use a Weibull distribution with a decided time to first failure
(but not to the second failure). With the choice of alpha equal to 1 the Weibull distribution is equal
56
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
to an exponential distribution. It is also possible to choose a point of time where the failure no
longer will occur. This allows the user to specify a time interval where a failure may occur and
associated an intervention profile and impact upon failure to this failure (figure 30).
Delay to first failure
(Weibull distribution
with alpha = 1)
Interval where failure may
occur
(Intervention strategy and
impact on production
described)
Time
FIGURE 30. Description of time dependent failures in MAROS
For the example used in this thesis it is then necessary to five time dependent failures for each
zones (in total 20 time dependent failures). This is required in order to enable the different intervention profiles for each phaseshift.
A MAROS model as described above was created. The safety failures was also included. The way
this is done is that a block called common (figure 21 (page 48)) at the output of the block diagram
and the safety failures are used as input to this block. In this simulation it was assumed no intervention for the production related failures. The result of the simulation is given in figure 31.
PERFORMANCE SUMMARY ( with respect to Potential Efficiency )
-----------------------------------------------------------potential efficiency
: 78.308 %
average efficiency
: 89.009 % +/- 12.810 %
availability of max capacity : 29.267 % +/- 19.541 %
total shutdown time
:
0.303 % +/- 0.647 %
actual volume produced
: 0.16536E+08 units
total production losses
: 0.20420E+07 units
average loss/annum
: 0.15708E+06 units
gains from recovery operations: 0.32941E+06 units
1.77 %
average nr. outages/annum
: 0.359999985E-01
average duration of outages : 0.30767E+02 (dys)
longest duration of outages : 0.36496E+03 (dys)
shortest duration of outages : 0.15716E+01 (dys)
repair service losses
:
26.67 (dys)/(yrs)/item
FIGURE 31. Output data from MAROS simulation (no intervention assumed for production
failures assumed)
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Control of simulation
During the simulation two incidents were reviewed closely (table 27).
TABLE 27. Check points for MAROS simulation
Check point
Comment
Does the failure occur only in the determined
time interval?
Yes, but it is necessary to choose a high number
of simulations
Does any intervention for the production relation
failures occur?
No. The mobilization time was set to a longer
period than the field life and this worked.
Requirements for a more accurate MAROS simulation
In order to fulfill the requirement of no intervention before a phase shift (only for the production
failure) it is necessary to give some conditional events as input. This would approximately take
two weeks (included debugging) for an experience used and therefore not enough time was available to create a model. Only the method is described here.
It is necessary to create a conditional event for each zone. Each type 2 failure must have a an
impact upon failure equal to 0% (figure 32 a)). The condition contains of two elements (figure 32
b)):
1. Occurrence of failure
2. The time of occurrence
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Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
The system logic is described in figure 33.
Zone Condition
Phase II
Phase III
Phase IV
Phase V
Distribution
(Prod = 0
Rep = 0)
2
Dependent
of time and
occurrence
Distribution
(Prod = 0
Rep = 0)
..
..
..
..
3
Dependent
of time and
occurrence
..
..
..
..
..
4
Dependent
of time and
occurrence
..
..
..
..
..
Distribution
(Prod = 0
Rep = 0)
Phase VI
Not evaluated
Dependent
of time and
occurrence
Distribution
(Prod = 0
Rep = 0)
1
a)
Phase I
Distribution
(Prod = 0
Rep = 0)
Time of occurrence
b)
Failure in phase
n
Condition
Occurrence of failure
FIGURE 32. MAROS input for a general intelligent well system
From previous
phase
NO
Failure
in phase
YES
End of
phase
NO
Wait until
end of
phase
YES
Calculate
consequence
Enter next
phase
FIGURE 33. MAROS conditional system logic
59
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
In addition to this input it is also possible to use so called “mutual exclusive” events. These events
are used to fulfill the condition If an intervention is performed in phase n it is not possible to perform an intervention in a zone below.
7.6 Validity of methods described to other systems
The methods used in this report are only applied on the SCRAMS system which is an electro/
hydraulic system. A detailed LCC/LCP model should also embrace electro and fiber optic system.
It easy to modify the methods used in this report to other systems. A zone in the SCRAMS system
is dependent on the zone above (and it contains multiple redundancies). This is the most complex
system available.
If another system is build up in the same manner the only modification required is to alter the
block diagram and calculate the MTTF for the type 1 and type 2 failure.
The other possibility for control is that the system uses direct control for the different zones. In this
case the system is less complex and it is possible to use a model with a single failure group for
each zone.
60
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
8. LCC model
The time was not sufficient to establish a detailed LCC/LCP model of the chosen intelligent well
system. It turned out that this was a much larger task than first expected.
Therefore only som guidelines for the LCC model are presented in this chapter.
8.1 General features
A model for an intelligent must not be applicable only for one system. Althoug the SCRAMS system is the market leader today it is very likely that other system will be tried out in the future.
Therefore the model must not only be applicable for electro hydraulic systems but also for pure
electric and fiber optic systems.
8.2 Layout of a LCC/LCP model
Figure 34 illustrates the layout of a LCC/LCP model for a general intelligent well system. It is necessary to chose which system to use. The best alternative is to let the user describe the configuration that is used by the program.
The following input data must be avaliable:
• Reliability data
• Cost Parameters
• Reservoir data
• Production data
Reliability data
- Safety failures
- Production
failures
- Maintenance
strategy
- .......
Cost Parameters
- Oil price
- Intervention
costs
Reservoir data
- Number of zones
- Production
profiles
- Phases
Prod. data
- What to happen
at the different
phase shifts
- The
consequence
when the action
not is achieved?
Electro
hydraulic
Type of
system?
Electric
Calculation
Fibre
optic
Presentation of output data
FIGURE 34. Layout of LCC/LCP model for a typicall intelligent well
61
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
9. Conclusions and recommendations
An intelligent well consists of a relatively simple structure. This makes the reliability modelling
relatively simple. The most important issue is to have an in depth understanding of the assessed
system and the reliability data applied in the model.
Today the amount of reliability data is sparse and it is very important to focus on data collection
and data processing. For the SCRAMS system some of the failures recorded were caused by
design failures and it is extremely important that these, and similar data, are not used without
sound criticism.
There are other factors than the reliability that makes the estimation of LCC and LCP challenging:
• Not all the components are required for the whole design life
• The actions taken and consequences are time dependent
• The possible repair and restore options have distinct effects on the residual lifetime
These factors seen together with the main differences between an intelligent well and a conventional well (production from different zones, increased recovery factor and accelerated production)
made a definition of a dedicated cost breakdown structure (CBS) and regularity measure for intelligent wells necessary.
The dedicated CBS is based on the NORSOK standard O-CR-002. The elements in the CAPEX
are the same as for a conventional well but the elements in the REGEX and OPEX are modified.
The REGEX must consist of one category for deferred production and one category for each producing zone. This is because a failure may have different impact in different zones. The cost for
unscheduled events should also be moved from the OPEX to the REGEX. Scheduled intervention
is removed from the OPEX because an intelligent well do not have scheduled intervention.
In order to include the benefits of increased recovery factor and accelerated production it is
required not only to calculate the LCC but also the LCP. It is the LCP that will be compared with
ordinary concepts. A new category in the CBS, PROIN (production income) is thus defined. This
category contains the maximum income for the field.
The production regularity measure defined in this project is feasible for only one well. For this
type of systems it is recommended to define the production regularity related to the maximum production. The production should be measured for each zone separately:
Pr od AIntelligent Well =
Actual Pr oduction Z1 + Actual Pr oduction Z 2 + Actual Pr oduction Z3 + ..
Maximum theoretic production
This measure is applicable for both gas and oil wells.
To measure the production regularity for an intelligent well, different approaches are evaluated in
order to quantify the production regularity. The concepts used in this project are applied for an
electro/hydraulic system but they are also applicable for other types of systems.
The failures associated with intelligent wells are divided into two groups:
• Safety related failures (require a full workover)
62
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
• Production related failures (intervention may be performed).
The LCC/LCP associated with safety related failures may easily be calculated with existing computer programs such as MAROS or a simple spread sheet (analytical calculations). Both methods
are applied and the calculated results are almost identical.
For production related failures, the scenario is more challenging. For a very simple scenario, with
no interventions, it is possible to estimate the LCC with analytical techniques but if decisions are
involved, this is not desirable because of the complexity.
Dynamic programming may be used to calculate the LCC/LCP for more complex structures. A
simple computer program was written and the result compared with the result from the analytical
approach. These values are found to be the same.
The dynamic programming may incorporate various features such as stochastic simulation or analytically estimated failures.
To have a more accurate model it is necessary to include both types of failures in the same model.
Dynamic programming is suitable to create such a model. The pattern for the decision tree for such
a model is described. The computer program is not written out. Discussions with two different
people with knowledge of dynamic programming proved that this should be fairly easy when the
pattern of the decision tree is described.
In cooperation with ABB, the ability of the computer program MAROS to quantify the regularity
was evaluated. MAROS offers both the possibility to model time dependent failures and the possibility to associate different intervention strategies for different failures. These features together
with the possibility of implementing conditional events may make MAROS suitable as a simulation tool for intelligent wells.
The implementation of a detailed LCC/LCP model for a general intelligent well would have been
too time consuming and is therefore not executed during the project.
A common factor for all approaches is the work done prior to the simulation. The models are
heavily dependent on the input data. For all the models it is required to decide the parameters for
production (production profiles, phases), consequences of failure, and so on.
9.1 Recommendations for further work
It is recommended that the findings in this report are explored further. There are four main action
points to be carried out:
1. Evaluation of a complete MAROS model
2. Development of a dedicated computer program based on dynamic programming
3. Evaluation of the reliability data
4. Further development of the tree structure used for describing the possible incidents
63
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Evaluation of a complete MAROS model
A complete MAROS model should be developed and simulated with different case studies. So far
no complete model for intelligent wells have been developed and evaluated.
This work could be carried out in cooperation with ABB or another institution that possess a
license of the software. It is extremely important that this work is carried out under supervision of
an experienced MAROS user because the modelling and debugging is complex.
If the complete MAROS model proves to be correct it is possible to use MAROS for calculating
LCC/LCP for intelligent wells.
Development of a dedicated computer program based on dynamic programming
The possibility of using dynamic programming for quantifying the production regularity should be
explored further. This approach offers the required functionality for an LCC/LCP model for an
intelligent well.
The structure of the program should be based on the pattern for decision trees developed for this
project.
A model should incorporate both the production related and safety related failures. The possibility
of defining this work as a project thesis or a diploma thesis should be considered. The main challenge is that the programmer should have detailed knowledge of the system and its complexity
before the program is written.
Quantification of uncertainty and sensitivity analysis
In this report the data used are assumed to be correct and no quantification of uncertainty is carried
out. As a result of this assumption no sensitivity analysis is undertaken. For a real case there are
uncertainties linked to the input data and these must be quantified. Today the lack of failure data is
a large problem.
It is recommended that an evaluation of the input data is performed and the uncertainties calculated. The method used may be based on the method given in the NORSOK standard. An increased
focus on gathering of failure data should also be undertaken. To aim of this work should be to
decrease the uncertainties that affects the LCC/LCP model and to evaluate which input data is
most important.
A decrease of the uncertainties will result in a more accurate model. The sensitivity analysis will
also reveal which factors are the most important (regarding uncertainty). A better understanding of
the factors will highlight where the focus for improvement should be.
Further development of the tree structure used for describing the possible incidents
For this thesis a technique for describing possible scenarios for a field was applied. During the
project this technique proved to be an extremely effective tool for understanding what may happen
during the life of the field.
This technique should be developed further in order to develop an optimal method for describing
possible outcomes for a field. The possibility to combine this technique with other methods, i.e.,
Monte Carlo simulation should also be explored.
64
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
The benefit will be a powerful method which may be used on any intelligent well regardless of
configuration.
65
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Appendix A
HAZID and HAZOP results
66
HAZID
System
Subsystem
Drawing
Intelligent Well System
System Breakdown
Accidental event
Drop of object in well
Drop of Christmas tree
during installation
Blow-out
Drop of BOP
Drop of objects
Major effects
16/01/01
1
of 1
Corrective/preventive
measures suggestion
Delay of installation
Cut of flat pack umbilical:
- While tubing hanger landed
on the tree.
- Crush of cable because of
tubing loads.
- While guided across large
components in the
completion string.
Leakage of hydrocarbons
to environment.
Page
Probable causes
Bad weather
SCRAMS components
failure
Date:
Not possible to communicate or control with the zones Qualified personnel
below the fracture.
Preparation of procedures
Re-run SCRAMS components
Wear/
Design error/
Human error
Human error /
Wrong procedures
Pollution/
Delay in installation/
Correct Procedures and training of personnel
Delay in completion/
Pull of equipment
Correct Procedures and training of personnel
Human error/
Wear/
Corrosion
Reservoir pressure/
Human error / Barrier failure
Delay of installation/
Damage of wellhead/
Replacement of Xmas tree
Environmental consequences/
Human hazards/
Delay in completion/
Correct Procedures and training of personnel
Weather,
Human error,
Transfer of objects from supply
boat
Human error, Weather,
Transfer of objects from supply
boat
Delay in production/
Damage of wellhead/
Pollution of environment/
Economical costs
Delay in production
Damage of wellhead
Pollution of environment
Correct Procedures and training of personnel
Correct Procedures and training of personnel
Installation procedures for SCRAMS
equipment
Preparation
and run tubing
below Zone 4
Run SCRAMS
components
for Zone 4
Run tubing
between zone
3 and 4
Run SCRAMS
components
for Zone 3
1
2
3
2
Run SCRAMS
components
for zone 2
Run tubing
between zone
1 and 2
Run SCRAMS
components
for zone 1
Run tubing
above zone 1
2
3
2
3
Run Tubing
Hanger
Set Packer
Run X-mas
tree
Clean up well
5
6
7
N/E
Run tubing
between zone 2
and 3
3
Run rest of tubing
with DHSV and
Production Packer
4
1
2
Transport tub.
joint to drill floor
1
Transport of
SCRAMS
module to
drillflor
1
Transport tub.
joint to drillflor
1
Transport DHSV
and PP to
drillflor
1
Transport
tub. joint to
drillfloor
5
Lift tub. joint to
rotary table (with
crane)
2
Lift module to
rotary table (with
crane)
2
Lift tub. joint to
rotary table (with
crane)
2
Make up of
DHSV with
umbilical
2
Lift joint to rotary
table (with crane)
6
3
Make up and test
of electric and
hydraulic
terminations
3
Make up
connection
between joints
3
Run tubing with
DHSV and
cables
3
Clamp flat pack
umbilical and
DHSV umbilical
to tubing
7
Connect next
joint to SCRAMS
module
4
Make up
connection
8
Lower joint
(connected to
Rotary Table
with slips)
9
Yes
Reached next
zone?
4
NO
Install feed
through through
FT packer
Test of zone
before next is
run
5
6
Clamp flat pack
umbilical to
tubing joint
4
Lower joint
(connected to
Rotary Table
with slips)
5
Reached next
zone?
NO
Make up
connection
NO
(Production Packer (PP)
is run and
electric/hydraulic
feed through are
made up through PP at
decried position in well
4
Reached
tubing hanger
Yes
Lower joint
(connected to
Rotary Table
with slips)
4
Yes
Make up
connection
between joints
3
5
6
Transport of
Tubing Hanger
to drillflor
1
Make up
connections of
electric and
hydraulic lines
Run TH with TH
running tool
7
Prepare set of
packer
1
Prepare tree
(transport to
launch spot)
1
Lift Tree
6
Pull Wireline
Plug
11
2
Land actuation
ball in tubing
2
Installation of
control cable to
surface
2
Lower tree
7
Run Tree Cap
12
3
Build up
pressure in
tubing
3
Install guidelines
3
Land tree on
Wellhead
8
Lock TH to
wellhead
4
Set P-pkr and
test P-pkr.
4
Prepare crane
4
Lock tree to
wellhead and
Test x-mas tree
connection
9
Set wireline plug
and test plug
5
Run ball down in
well
5
Connect tree to
crane
5
Test SCRAMS
equipment
10
Retrieve BOP
6
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Appendix B
FMECA results
71
FMECA
System:
Ref. drawing no.:
Inflow Control Valve
System Break Down
Performed by:
Date:
Description of Unit
Knut Eivind Borg
16/01/01
Description of failure
ID
Compone
nt
Function
Operational
mode
4.1.2.3
Sleeve
Flow
regulation
Closed
(stand by)
Opening
from closed
position
1/3 open
(stand by)
1/3 open
(opening/
closing)
2/3 open
(stand by)
2/3 open
(opening/
closing)
Failure
mode
Failure
mechanism
Detection of
failure
Page:
Failure rate
Severity
ranking
Not optimised
production
Low
Marginal
Effect of failure
On components
in the
subsystem
1
On the system
function
Leakage
Corrosion/
from
Sand production/
reservoir into
Seal failure
tubing
Monitoring
Leakage
from tubing
into
Reservoir
Unwanted
opening of
sleeve
Not opening
on command
Corrosion/
Sand production/
Seal failure
Monitoring
None
Not optimised
production
Low
Marginal
Electric signal
Monitoring
None
Not optimised
production
Low
Marginal
No control
signal/
Scale build up/
Electric signal
Corrosion/
Wear out/
vibration
No control
signal/
Scale build up
Periodic testing /
On demand
None
Low
Critical
Monitoring
None
Not possible to
produce from this
zone
Not optimised
production
Low
Marginal
On demand/
Periodic testing
None
Not optimised
production
Low
Critical
Electric signal
Corrosion/
Wear out/
vibration
No control
signal/
Monitoring
None
Not optimised
production
Low
Marginal
On demand/
Periodic testing
None
Not optimised
production / Loss of
control function
Low
Critical
Unwanted
movement
(opening or
closing)
Not moving
on command
Unwanted
movement
(opening or
closing)
Not moving
on command
None
of:
3
Comments
FMECA
System:
Ref. drawing no.:
Inflow Control Valve
System Break Down
Performed by:
Date:
Description of Unit
ID
4.1.2.2
Compone
nt
Connector
(Hydraulic)
4.1.2.1
Actuator
Piston
Function
ICV
hydraulic
connector.
Open or
close
Sliding
sleeve
Knut Eivind Borg
16/01/01
Description of failure
Page:
Failure rate
Severity
ranking
Low
Marginal
Low
Critical
Loss of hydraulic
control functions
(included zones
below)
Medium
Critical
Loss of hydraulic
control functions
(included zones
below)
Not optimised
production
Low
Critical
Low
Marginal
Loss of control
function
Low
Critical
Effect of failure
Operational
mode
Failure
mode
Failure
mechanism
Detection of
failure
On components
in the
subsystem
On the system
function
Open
(stand by)
Closing from
open
position
Unwanted
movement
Not closing
on command
Electric signal/
Corrosion
No control
signal/
Scale build up
Monitoring
None
Periodic Testing/
On demand
None
Not optimised
production
Loss of control
function/ not
optimised production
Connected
Leakage
Seal problems/
Corrosion/
Vibrations/
Stress
Monitoring
Short circuit of
electric
connectors
Unwanted
disconnectio
n
Stresses/
Vibrations
Wear out/
Monitoring
Stand by
Spurious
movement
Monitoring
Fill of
stroke down/
up chamber
of actuator
Leakage
between
seals/
Ext.
Leakage/
Stuck in mid
position/
Stuck in end
position/
Leakage
from prod
side to ret
side
Leakage/
Corrosion/
Seal failure
Corrosion/
Seal failure/
Wear out
Monitoring
Short circuit of
electric
components
Unwanted
movement of
sleeve
Not possible to
operate sleeve
2
of:
3
Comments
FMECA
System:
Ref. drawing no.:
Inflow Control Valve
System Break Down
Performed by:
Date:
Description of Unit
ID
Compone
nt
Function
Knut Eivind Borg
16/01/01
Description of failure
Operational
mode
Page:
Effect of failure
Failure
mode
Failure
mechanism
Detection of
failure
On components
in the
subsystem
On the system
function
No Hydraulic
Pressure to
actuation
Chamber
Leakage /
Corrosion
Monitoring
Not possible to
move sleeve
Loss of control
function
3
Failure rate
Severity
ranking
Low
Crtitical
of:
3
Comments
FMECA
System:
Ref. drawing no.:
Sensor and Control Module
System Breakdown
Description of Unit
Performed by:
Date:
Knut Eivind Borg
16/01/01
Description of failure
Page:
Effect of failure
ID
Component
Function
Operational
mode
Failure
mode
Failure
mechanism
4.1.1
ID_H
1.1
H. O.
Solenoid
Valve A
Control hydraulic
output from zone
Keep Flow
Open
(Stand by)
Closing
spuriously
Electric signal
failure/
Spring failure/
Corrosion
Monitoring
None
Fluid flow
restricted
or blocked
Condemnation/
Plugged
Failure on
demand/
Monitoring
None
Periodic
testing /
None
Close flow
Keep flow
closed
Not closing
No signal,
at all
Internal leakage
of hydraulic
fluid
Corrosion
Wear
Partly
Corrosion
closing
Wear
Leakage to Seal problem/
annulus
Corrosion/
Wear
Detection of On components
failure
in the
subsystem
Severity
ranking
Comments
Low
Negligible
The same as
H.O. Solenoid
Valve B
Low
Marginal
Low
Negligible
Loss of
redundant
hydraulic
control
function
Loss of
redundant
Hydraulic
control
function to
the next zone
Loss of
hydraulic
fluid
None
None
Low
Negligible
Failure on
demand
Non
Loss of
hydraulic
pressure
Low
Marginal
Failure
electric
control
function.
Loss of
hydraulic
pressure
Low
Critical
Low
Marginal
Monitoring/
Failure on
demand
Short circuit of
electronics
Spurious
opening
Electric signal
failure/
Spring failure/
Corrosion
Monitoring
None
7
On the
system
function
Monitoring
Seal problem/
Corrosion
of:
Failure
rate
Failure on
demand
Leakage to
solenoid
1
Both solenoid
valves need to
fail before loss of
control
FMECA
System:
Ref. drawing no.:
Sensor and Control Module
System Breakdown
Description of Unit
ID
4.1.1
ID_H
1.3
Component
Aux. Output
Solenoid
valve
Function
Set and release
Packer
Performed by:
Date:
Knut Eivind Borg
16/01/01
Description of failure
Page:
Effect of failure
Severity
ranking
Low
Marginal
Low
Marginal
Low
Marginal
Low
Critical
Low
Critical
Operational
mode
Failure
mode
Failure
mechanism
Open Flow
Not
opening at
all
No electric
signal/
Condemnation
Failure on
demand/
Monitoring
None
Flow less
than
specified
Unclean
hydraulic fluid/
Corrosion
Monitoring /
Failure on
demand
None
Leakage to
annulus
Corrosion
Wear
Seal problems
Condemnation/
Plugged /
leakage
Monitoring /
Failure on
demand
Failure on
demand/
Monitoring
None
Electric signal
failure/
Corrosion/
Spring failure
No signal,
Seal Problems
Corrosion
Wear
Monitoring/
Failure on
demand
None
Periodic
testing /
None
Loss of
hydraulic
fluid
Low
Marginal
Corrosion
Wear
Monitoring
None
Loss of
hydraulic
fluid
Low
Marginal
Keep Flow
Open
Fluid flow
restricted
or blocked
Spurious
closure of
solenoid
Close flow
Not closing
at all
Partly
closing
Detection of On components
failure
in the
subsystem
Failure
rate
None
2
of:
Comments
On the
system
function
Loss of
redundant
hydraulic
control
function for
the zone
below
Loss
redundant
hydraulic
control for
the zones
below
Loss of
zonal
isolation.
Not possible
to set packer.
Loss of zonal
isolation
Loss of zonal
isolation
Failure on
demand
No redundancy
of this function
7
FMECA
System:
Ref. drawing no.:
Sensor and Control Module
System Breakdown
Description of Unit
ID
Component
Function
Stroke up
ICV
Solenoid
Valve
Direct hydraulic
fluid to open
Sleeve
Knut Eivind Borg
16/01/01
Description of failure
Page:
Effect of failure
Operational
mode
Failure
mode
Failure
mechanism
Keep flow
closed
(stand by)
Leakage to
annulus
Monitoring/
Failure on
demand
Failure on
demand
None
Leakage to
solenoid
Seal problem/
Corrosion/
Wear
Seal problem/
Corrosion
Leakage to
HF packer
Seal problem /
Corrosion
Monitoring
None
Not
opening at
all
Flow less
than
specified
No electric
Failure on
signal/
demand /
Condemnation
Monitoring
Unclean
Monitoring /
hydraulic fluid/ Failure on
Corrosion
demand
None
Leakage to
annulus
Open Flow
4.1.1
ID_H
1.4
Performed by:
Date:
Keep Flow
Open
Close flow
Corrosion
Wear
Seal problems
Fluid flow Condemnation/
restricted
Plugged/
or blocked
Leakage
Spurious
Electric signal
closure of
failure/
solenoid
Corrosion/
Spring failure
Not closing
No signal,
at all
Internal leakage
of hydraulic
fluid
Corrosion
Wear
Detection of On components
failure
in the
subsystem
None
Failure
rate
Severity
ranking
Low
Marginal
Low
Critical
Low
Critical
Medium
Critical
Loss of
hydraulic
pressure
Loss of
control
function
Unwanted
setting of
packer
Loss of zonal
isolation
Loss of zonal
isolation
Low
Negligible
Monitoring/
Failure on
demand
Failure on
demand
None
Low
Critical
Low
Critical
Monitoring/
Failure on
demand
None
Loss of
control
function
Loss of
control
function
Loss of
control
function
Low
Critical
Periodic
testing /
None
Medium
Critical
Failure on
demand
of:
Comments
On the
system
function
None
None
3
Loss of
control
function
No redundancy
of this function
7
FMECA
System:
Ref. drawing no.:
Sensor and Control Module
System Breakdown
Description of Unit
ID
Component
Function
Performed by:
Date:
Description of failure
Operational
mode
Keep flow
closed
(stand by)
Failure
mode
Failure
mechanism
Partly
closing
Corrosion
Wear
Leakage to
annulus
Leakage to
solenoid
Spurious
opening
Open Flow
4.1.1
ID_H
1.5
Stroke down
ICV
Solenoid
Valve
Direct hydraulic
fluid to close
Sleeve
Knut Eivind Borg
16/01/01
Keep Flow
Open
Leakage
through
valve
Not
opening at
all
Flow less
than
specified
Leakage to
annulus
Fluid flow
restricted
or blocked
Spurious
closure of
solenoid
Page:
Effect of failure
Failure
rate
Severity
ranking
Detection of On components
failure
in the
subsystem
On the
system
function
None
Loss of
control
function
Loss of
hydraulic
fluid
Low
Marginal
Seal problem/
Corrosion/
Wear
Monitoring /
Failure on
demand
Monitoring /
Failure on
demand
Low
Marginal
Seal problem/
Corrosion
Failure on
demand
Short circuit of
electric
components
None
Loss of
control
function
Not
optimised
production
Not
optimised
production
Loss of
control
function
Loss of
control
function
Low
Critical
Low
Marginal
Low
Critical
Medium
Critical
Low
Marginal
Low
Critical
Low
Critical
Low
Critical
Electric signal
Monitoring
failure/
Corrosion
Seal Problem /
Monitoring
Corrosion/
Vibration
No electric
Failure on
signal/
demand
Condemnation
Unclean
Monitoring /
hydraulic fluid/ Failure on
Corrosion
demand
Corrosion
Wear
Seal problems
Condemnation/
Plugged /
leakage
Electric signal
failure/
Corrosion/
Spring failure
None
None
None
Monitoring/
Failure on
demand
Failure on
demand
None
Monitoring/
Failure on
demand
None
None
Loss of
control
function
Loss of
control
function
Not
optimised
production
4
of:
Comments
No redundancy
of this function
7
FMECA
System:
Ref. drawing no.:
Sensor and Control Module
System Breakdown
Description of Unit
ID
Component
Function
Performed by:
Date:
Description of failure
Operational
mode
Close flow
Keep flow
closed
(stand by)
Failure
mode
Provide hydraulic
flow to solenoids
Open
Detection of On components
failure
in the
subsystem
Failure
rate
Severity
ranking
On the
system
function
Loss of
control
function
Medium
Critical
None
Low
Marginal
Leakage to
annulus
Monitoring
None
Low
Marginal
Monitoring /
Failure on
demand
Monitoring
None
Low
Critical
None
Low
Marginal
Monitoring
None
Low
Critical
Failure on
demand /
periodic
testing
Unclean
Monitoring /
hydraulic fluid/ Failure on
Corrosion
demand
None
Loss of
control
function
Loss of
control
function
Loss of
control
function
Not
optimised
production
Not
optimised
production
Loss of
control
function
Medium
Critical
Leakage
through
valve
Not
opening at
all
Flow less
than
specified
Manifold
unit
Effect of failure
None
Spurious
opening
Open Flow
Failure
mechanism
Page:
Periodic
Not closing
No signal,
testing /
at all
Internal leakage
Failure on
of hydraulic
demand
fluid
Corrosion
Wear
Partly
Corrosion
Monitoring
closing
Wear
Leakage to
solenoid
4.1.1
ID_H
2
Knut Eivind Borg
16/01/01
No flow/
Leakage
Seal problem/
Corrosion/
Wear
Seal problem/
Corrosion
Electric signal
failure/
Corrosion
Corrosion/
Vibration
No electric
signal/
Condemnation
Plugged lines/
Broken lines/
Chips from
machinery
Failure on
demand
None
Loss of
control
function
Low
Marginal
Malfunction of
solenoid valves
Loss of
control
function
Low
Critical
5
of:
Comments
7
FMECA
System:
Ref. drawing no.:
Sensor and Control Module
System Breakdown
Description of Unit
Performed by:
Date:
Knut Eivind Borg
16/01/01
Description of failure
Page:
Effect of failure
ID
Component
Function
Operational
mode
Failure
mode
Failure
mechanism
4.1.1
ID_E
1.1.1
I-Wire A
input
(connector)
Electric connector
between I-Wire
and ICV module
Connected
Short
Circuit
Hydraulic fluid
leakage/
Vibration/
Stresses
Vibration/
Stresses/
Corrosion
Monitoring
Short
Circuit
I
Vibration/
Fluid ingress/
Temperature
Monitoring
Short
Circuit
II
Vibration/
Fluid ingress/
Temperature
Monitoring
Failure of AEM
B
No
communica
tion
Wear out/
Electro
migration
Monitoring
No data
transmission for
sensors
Short
Circuit /
Wear out
Temperature/
Vibration Wear
Out/
Electro
migration
Monitoring
None
Unwanted
Disconnect
4.1.1
ID_E
1.2
4.1.1
ID_S
1.1.1.3
AEM A
Pressure
sensor A
(annulus)
Control function
and digital
communication
within ICV
Measure pressure
of the reservoir
flow in the annulus
Monitor and
control
Monitoring
Detection of On components
failure
in the
subsystem
Monitoring
No
communication
with AEM A in
this zone
No
communication
with AEM A in
this zone
None
6
of:
7
Failure
rate
Severity
ranking
Comments
High
Critical
Both connectors
(I-wire) need to
fail before loss of
function
Low
Critical
Medium
Marginal
Low
Critical
Low
Marginal
High
Critical
On the
system
function
Loss of
redundant
electric line
to next zone
Loss of
redundant
line to next
zone
Loss of
redundant
control and
monitoring
in current
zone
No
communicati
on with
current and
below zones.
Loss of
redundant
control and
monitoring
in current
zone
Loss of
redundant
pressure
measurement
in the current
zone
Both connectors
I-wire need to
fail before loss of
function
Both pair of
sensors need to
fail before loss of
function
FMECA
System:
Ref. drawing no.:
Sensor and Control Module
System Breakdown
Description of Unit
Performed by:
Date:
Knut Eivind Borg
16/01/01
Description of failure
ID
Component
Function
Operational
mode
Failure
mode
Failure
mechanism
4.1.1
ID_S
1.1.2.3
Pressure
Sensor A
(tubing)
Measure pressure
of the flow in the
tubing
Monitoring
Short
Circuit/
Wear Out
Temperature/
Vibration Wear
Out/
Electro
migration
Page:
Effect of failure
Detection of On components
failure
in the
subsystem
Monitoring
None
Failure
rate
Severity
ranking
High
Marginal
On the
system
function
Loss of
redundant
pressure
measurement
in the current
zone
7
of:
Comments
7
Intelligent Well
1
2
Z1
Z2
Z3
Z4
Wellhead
Between Zone 1
and Wellhead
Zone 1
Zone 2
Zone 3
Zone 4
4
4
4
4
SCRAMS
Module
SCRAMS
Module
SCRAMS
Module
SCRAMS
Module
1.1
Tubing
Hanger
2.1
2.2
2.3
Umbilical
Tubing
SSSCV
2.3.1
2.3.2
Valve
HP line
2.1.1
H1
E1
2.1.2
WH
penetration
Hydraulic
line
Electric
line
Cable
clamps
5
6
3
5
6
7
Tubing
between
zone 1
and 2
Tubing
between
zone 2
and 3
Tubing
between
zone 3
and 4
Tubing
below
zone 4
1
TITLE:
DATE:
FILENAME:
1
1
1
Intelligent Well - System Breakdown
16/01/01
TIME :
16:57
SCRAMS_SB_nummer.vsd
PG
1
OF
6
PGS
1
4.1
4.2
4.3
Inflow device
FT Packer
Cable
4.1.1
4.1.2
Sensor/Control
Module
ICV
4.2.1
4.2.2
4.2.3
4.2.4
4.2.5
Connector
Seal
element
Actuator
Slips
Housing
ID_H
ID_E
ID_S
4.1.2.1
4.1.2.2
4.1.2.3
Hydraulic
Electric
Sensor
Actuator
Connector
Sleeve
2
3
H2
Hydraulic
line
Electric
line
5
6
4
TITLE:
DATE:
FILENAME:
4.3.1
E2
Cable
clamps
Intelligent Well - System Breakdown
16/01/01
TIME :
16:58
SCRAMS_SB_nummer.vsd
PG
2
OF
6
PGS
2
1
2
3
4
Solenoid Valves
Manifold
Filters
Check Valves
1.1
1.2
1.3
1.4
1.5
2.1
4.1
4.2
Solenoid
Hydraulic output A
Solenoid
Hydraulic output B
Packer setting
Solenoid valve
Stroke up ICV
Solenoid valve
Stroke down ICV
soleoid valve
Not broken
down further
Shuttle Check
Valve
Check Valve to
annulus
3
1.1
I-Wire
Input A
1
2
ICV electric A
ICV electric B
1.2
1.3
I-Wire
Output A
AEM "A"
2.1
I-Wire
Input B
2.2
2.3
I-Wire
Output B
AEM "B"
1.1.1
1.1.2
1.3.1
1.3.2
2.1.1
2.1.2
2.3.1
2.3.2
Connector
Line
Connector
Line
Connector
Line
Connector
Line
TITLE:
DATE:
FILENAME:
Intelligent Well - System Breakdown
16/01/01
TIME :
16:58
SCRAMS_SB_nummer.vsd
PG
3
OF
6
PGS
5
1
2
Hydraulic line A
Hydraulic line B
1.1
1.2
1.3
1.4
2.1
2.2
2.3
2.3
FT in Packer
Connectors
Hydraulic line
Filter
Filter
Hydraulic line
Connectors
FT in Packer
6
1
2
Electric line A
Electric line B
1.1
1.2
1.3
2.1
2.2
2.3
FT in Packer
Electric line
Connector
FT in Packer
Electric line
Connector
TITLE:
DATE:
FILENAME:
Intelligent Well - System Breakdown
16/01/01
TIME :
16:58
SCRAMS_SB_nummer.vsd
PG
4
OF
6
PGS
4
1
2
3
Pressure
Temperature
Position
1.1
1.2
2.1
2.2
Pressure Sensors
connected to
AEM A
Pressure Sensors
connected to
AEM B
Temperature
Sensors connected
to AEM A
Temperature
Sensors connected
to AEM B
10
11
8
9
7
7
3.1
Positions
Sensors
3.1.1
3.1.2
3.1.3
Position
sensor closed
Position
sensor 1/3 open
Position
sensor 2/3 open
TITLE:
DATE:
FILENAME:
3.1.4
Position
sensor fully
open
Intelligent Well - System Breakdown
16/01/01
TIME :
16:59
SCRAMS_SB_nummer.vsd
PG
5
OF
6
PGS
9
8
1.1.1
Pressure
Annulus
1.1.1.1
Electric
Line
1.1.1.2
Connector
1.1.1.3
Sensor
1.1.2.1
1.1.2
1.2.1
1.2.2
Pressure
Tubing
Pressure
Annulus
Pressure
Tubing
1.1.2.2
Electric line
Connector
1.1.2.3
Sensor
1.2.1.1
Electric
Line
1.2.1.2
1.2.1.3
1.2.2.1
1.2.2.2
1.2.2.3
Connector
Sensor
Electric line
Connector
Sensor
10
2.1.1.1
Electric
Line
11
2.1.1
2.1.2
2.2.1
2.2.2
Temperature
Annulus
Temperature
Tubing
Temperature
Annulus
Temperature
Tubing
2.1.1.2
2.1.1.3
2.1.2.1
2.1.2.2
2.1.2.3
Connector
Sensor
Electric line
Connector
Sensor
TITLE:
DATE:
FILENAME:
2.2.1.1
Electric
Line
2.2.1.2
2.2.1.3
2.2.2.1
2.2.2.2
2.2.2.3
Connector
Sensor
Electric line
Connector
Sensor
Intelligent Well - System Breakdown
16/01/01
TIME :
16:59
SCRAMS_SB_nummer.vsd
PG
6
OF
6
PGS
Active Functions
Control
Energy
Functional requirements
Input
Materials
Function
Resources and equipment
Output
Active functions
Electric
Electric
Hydraulic
Electric power
Flow regulation
Flow from
reservoir
Measure
Position
Flow Control
Sleeve
(Actuator
Solenoid,
Sensor,
Interface Card)
Valve position
SCM
AEM
Umbilical
Sensor
Electric
Electric power
Measure
Temperature
Flow from
reservoir
SCM
AEM
Umbilical
Sensor
Temperature data to surface
Position data
to surface
Active functions
Electric signal in
Electric signal in
Electric signal in
Electric signal
Open solenoid
valve
Close Solenoid
Valve
Open Valve
Hydraulic fluid
Hydraulic fluid
Solenoid Valve
Umbilical
Hydraulic
fluid
Hydraulic fluid
Actuate
Sleeve
Actuator piston
Solenoid Valve
Umbilical
Actuate sleeve
Close Solenoid Valve
Solenoid Valve
Umbilical
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Appendix C
Calculation of MTTF
91
Determination of MTTF for type 1 and type 2 failure
Analytical approach
Failure data for SCRAMS system
Failure data
Hydraulic line (connectors included)
λ SV
1 . 10
SINTEF
6
1 . 10
λ Hydraulic_line
λ CV
3 . 10
6
SINTEF
6
SINTEF
Electric line (connectors included)
0.01 .10
λ Open_short_line
0.5 . 10
λ Open_short_connection
λ Electric_line
SINTEF
6
SINTEF
6
λ Open_short_line
λ Open_short_connection
λ Electric_line = 5.1 10 7
Inflow device
λ Sleeve
0.16 .10
6
MTTF sleeve
1
λ Electric_line .8760 MTTF sleeve = 223.83
Type 1 failure
λ type1
2 . λ SV
λ type1 = 2.16 10 6
λ Sleeve
MTTF TOTAL_hours_1
MTTF TOTAL_years_1
1
5
MTTF TOTAL_hours_1 = 4.63 10
λ type1
MTTF TOTAL_hours_1
8760
1
MTTF TOTAL_years_1 = 52.85
Type 2 failure
Hydraulic line between Inflow devices
λ hydraulic_line
λ SV
λ Hydraulic_line
Redundancy of hydraulic lines
λ hydraulic_lineA
λ hydraulic_line
λ hydraulic_lineB
λ hydraulic_line
1
λ Redundant_lines
1
1
λ hydraulic_lineA
λ hydraulic_lineB
λ Redundant_lines = 1.33 10
1
λ hydraulic_lineB
λ hydraulic_lineA
6
Failure Rate for Hydralic
λ HYDRAULIC
λ Redundant_lines
λ CV
λ HYDRAULIC = 4.33 10
6
Electric lines
λ Electric_lines
λ Electric_line
λ Electric_lines = 5.1 10
7
Redundancy of electric lines
λ Electric_lineA
λ Electric_lines
1
1
λ Electric_lineA
λ Electric_lineB
λ ELECTRIC = 3.4 10
λ ELECTRIC
λ HYDRAULIC
MTTF TOTAL_years_2
λ type1
1
λ Electric_lineB
λ Electric_lineA
7
MTTF TOTAL_hours_2
λ type2
λ Electric_lines
1
λ ELECTRIC
λ type2
λ Electric_lineB
λ type2 = 4.67 10
1
5
MTTF TOTAL_hours_2 = 2.14 10
λ type2
MTTF TOTAL_hours_2
1
MTTF TOTAL_years_2
1
MTTF TOTAL_years_1
6
8760
λ type2 = 0.04
λ type1 = 0.02
2
MTTF TOTAL_years_2 = 24.43
λ type1
z type1( t )
z type2( t )
λ type2
z type1( t )
z type2( t )
0.05
0
0
5
10
t
The survival function for the exponential function
15
20
R( t ) e
Type 1 failure
R type1_phase1
e
R type1_phase2
e
R type1_phase3
e
R type1_phase4
e
R type1_phase5
e
λ type1 .1
λ type1 .2
λ type1 .5
λ type1 .8
λ type1 .10
R type1_phase1 = 0.98
R type1_phase2 = 0.96
R type1_phase3 = 0.91
R type1_phase4 = 0.86
R type1_phase5 = 0.83
Type 2 failures
R type2_phase1
e
R type2_phase2
e
R type2_phase3
e
R type2_phase4
e
R type2_phase5
e
λ type2 .1
λ type2 .2
λ type2 .5
λ type2 .8
λ type2 .10
R type2_phase1 = 0.96
R type2_phase2 = 0.92
R type2_phase3 = 0.81
R type2_phase4 = 0.72
R type2_phase5 = 0.66
3
λt
University License
Not for commercial use
Type 1 failure
Not possible to
move sleeve
Or 1
Solenoid Valve
stroke up bot
functioning
Basic 1
Solenoid Valve
Stroke Down not
functioning
Basic 2
Sliding Sleve failure
Basic 7
University License
Not for commercial use
Type 2 failure
No electric signal or
hydraulic fluid
between two zones
Or 1
No electric signal
between zones
No hydraulic fluid
between zones
And 1
Failure of Electric
line A
El u A
And 2
Failure of Electric
line B
Check Valve failure
EL l B
No hydraulic fluid
from solenoids to
Check Valve
CV F
And 5
Failure of hydraulic
line B
Failure of hydraulic
line A
Or 5
Failure of Solenoid
Valve B
SV B
Or 4
Failure of Hydraulic
line B
HL P u B
Failure of Solenoid
Valve A
SV A
Failure of Hydraulic
line A
HL P u A
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Appendix D
Calculation of expected costs for safety
related failures
98
Determination of cost for safety related failures
Expected costs for failures concerning safety:
It is assumed that three failures will lead to a full workover:
- Critical failure of the tubing
- Critical failure of the DHSV
- Leakage of the Production Packer
The cost associated with these failures are the same for all incidents.
The different cost groups:
- Intervention
- Equipment
- Deferred production
The expected cost each year can be calculated with the following expression
n
Ci
E( cost )
i=1
(1
r)
i
.P
i
Where:
C i : Cost of failure in year i
i : year
r : discount rate
P i : Probability of failure in year i
Assumptions:
- The length of the intervention is 4 weeks
- The cost of the intervention vessel is US$ 150 000 per day
- Oil price is US$ 20 /barrel
- All failure data taken from SINTEF
1
Failure data
Length of tubing in OSEBERG above tubing
l tubing
Failure rate adjusted to failure rate [x10-4] per km-year] F Rate
Failurerate tubing
MTTF tubing
l tubing .F Rate
1
Failurerate tubing
α tubing
2.5
MTTF DHSV
30
α DHSV
1
MTTF Packer
340.1
α Packer
1
λ tubing
1
.Γ
MTTF tubing
λ DHSV
1
.Γ
MTTF DHSV
1
.Γ
MTTF Packer
1
1
α DHSV
1
α Packer
λ tubing = 0.016
1
α tubing
1
1
λ DHSV = 0.033
λ Packer = 2.94 10
Plotting Interval
z tubing( t )
α tubing .λ tubing . λ tubing . t
z DHSV( t )
α DHSV .λ DHSV . λ DHSV . t
z Packer( t )
α Packer .λ Packer . λ Packer . t
Z s( t )
71.2 . 10
Failurerate tubing = 0.018
MTTF tubing = 56.18
λ Packer
2.5
z tubing( t )
z DHSV( t )
z Packer( t )
2
α tubing
1
α DHSV
1
α Packer
1
3
km
4
0.04
z tubing( t )
z DHSV( t )
z Packer( t )
0.02
Z s( t )
0
5
10
t
1
1
0
e
3
Z s( t )d t
P year2
P year1 = 0.036
1
1
e
6
Z s( t )d t
P year5
1
4
e
P year8
1
7
e
P year8 = 0.037
e
P year11
1
e
P year11 = 0.038
13
Z s( t )d t
P year13
1
e
1
e
12
Z s( t )d t
9
P year10 = 0.038
P year9
11
Z s( t )d t
1
Z s( t )d t
8
P year9 = 0.037
10
P year10
9
Z s( t )d t
6
P year7 = 0.037
e
8
Z s( t )d t
e
1
5
P year6 = 0.037
7
1
Z s( t )d t
P year6
P year5 = 0.036
P year4 = 0.036
P year7
e
5
3
e
1
2
P year3 = 0.036
Z s( t )d t
1
Z s( t )d t
P year3
P year2 = 0.036
4
P year4
20
2
Z s( t )d t
P year1
15
12
P year13 = 0.039
3
10
Z s( t )d t
P year12
1
e
P year12 = 0.039
11
Res
Res =
P year1 P year2 P year3 P year4 P year5 P year6 P year7 P year8 P year9 P year10 P year11
0
1
2
3
4
5
6
7
8
9
10
11
12
0 0.036 0.036 0.036 0.036 0.036 0.037 0.037 0.037 0.037 0.038 0.038 0.039 0.039
4
Calculation of expected cost due to safety related failures
Assumptions
Dayrate
Intervention time
Value
Unity
150000 US$/day
4 weeks
Discount rate
20
Field Life
12 years
Oil Price
25 US$
Cost item
Intervention
Man hour cost
Year
Probability of failure
2850000 US$
0 US$
1
2
3
4
5
6
7
8
9
10
11
12
13
0.036
0.036
0.036
0.036
0.036
0.037
0.037
0.037
0.037
0.038
0.038
0.039
0.039
Production
1500
4000
5000
5000
5000
5000
5000
5000
4300
4200
3100
2300
1500
NPV factor
0.913
0.761
0.634
0.528
0.440
0.367
0.306
0.255
0.212
0.177
0.147
0.123
0.102
Cost of deferred production
1050000
2800000
3500000
3500000
3500000
3500000
3500000
3500000
3010000
2940000
2170000
1610000
1050000
Intervention costs
2850000
2850000
2850000
2850000
2850000
2850000
2850000
2850000
2850000
2850000
2850000
2850000
2850000
Man hour costs
0
0
0
0
0
0
0
0
0
0
0
0
0
NPV total costs
3560197
4298101
4025507
3354589
2795491
2329576
1941313
1617761
1244105
1024370
740117
547962
399300
128167
154732
144918
120765
100638
86194
71829
59857
46032
38926
28124
21371
15573
NPV*probability of failure
Total cost during field life
$1,017,126
MAROS results for model based on safety related failures
PERFORMANCE SUMMARY ( with respect to Potential Efficiency )
-----------------------------------------------------------potential efficiency
: 78.308 %
average efficiency
: 99.679 % +/- 0.451 %
availability of max capacity : 46.006 % +/- 0.294 %
total shutdown time
:
0.314 % +/- 0.418 %
actual volume produced
: 0.18519E+08 units
total production losses
: 0.59592E+05 units
average loss/annum
: 0.45840E+04 units
gains from recovery operations: 0.00000E+00 units
0.00 %
average nr. outages/annum
: 0.411538452E-01
average duration of outages : 0.27864E+02 (dys)
longest duration of outages : 0.28000E+02 (dys)
shortest duration of outages : 0.13450E+02 (dys)
repair service losses
:
0.19 (dys)/(yrs)/item
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Appendix E
Dynamic programing and analytical
approach for prod. failures
104
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
Phase 6
0.04
Not possible to control sleeve in any zones
F
0.04
0.02
2
S
0.08
F
1
0.96
Not possible to move sleeve in zone 1
F
S
Zone 1
0.753
0.04
2
S
0.98
0.192
Not possible to control sleeve in any zones
0.92
0.19
F
1
Not possible to move sleeve in zone 1
0.28
F
S
2
0.34
F
S
0.96
2
Not possible to move sleeve in zone 1
Not possible to control sleeve
F
S
0.81
0.346
Not possible to control sleeve in any zones
0.1885
0.1648
2
Total success in zone 1
S
0.72
0.1579
0.3198
0.66
0.0784
Z1 2
S
Not possible to control sleeve in zone 2 and below
F
0.784
0.02
2
S
0.9216
Not possible to control sleeve in zone 2 and below
F
0.0184
1
S
0.1536
Z1 2
S
0.98
Not possible to control sleeve in zone 2 and below
F
0.1387
2
S
0.3439
Z1 2
S
0.8464
Zone 2
Not possible to control sleeve in zone 2 and below
F
0.2629
2
S
0.4816
Z1 2
S
0.6561
Not possible to control sleeve in zone 2 and below
F
0.2415
0.14
2
S
Not possible to move sleeve in zone 2
F
0.0364
1
0.5184
S
0.5644
Z1 2
S
0.86
0.66
F
2
S
Not possible to move
sleeve in zone 2
0.1262
Total success in zone 1
and 2
0.0974
0.4356
Z1 2
S
Z2 2
S
Not possible to control zone 3 and below
F
2
Zone 3
S
Z1 2
S
Z2 2
S
Not possible to control zone 3 and 4
F
2
Not possible to control any zones
F
S
Z1 2
S
Not possible to control zone 2 and below
F
Z2 2
S
F
2
S
Not possible to control zone 3 and 4
Perl code for the dynamic program approach
#!/usr/bin/perl -w
@pt1 = ( 0.98, 0.96, 0.91, 0.86, 0.83 ); # 1-dim table for type 1 failures
@pt2 = ([ 0.96, 0.92, 0.81, 0.72, 0.66 ]); # 2-dim table for type failures
# Determination of where sleeve is due to be moved
@ms = ([ 1, 1, 0, 0, 0 ],
[ 1, 0, 0, 1, 0 ],
[ 0, 0, 1, 0, 0 ],
[ 0, 0, 0, 0, 1 ]);
for $z (0 .. 3) {
# The number of zones
$temp = 1;
$sum = 0;
print "\nZone " . ($z + 1) . "\n======\n";
for $ph (0 .. 4) {
# The number of phases
# Calculation of type 2 failure for the the uppermost zone
if ($z == 0) {
printf("Phase shift %d (Type 2 error): %.4f\n", ($ph+1), ((1 $pt2[$z][$ph]) * $temp));
$sum += (1 - $pt2[$z][$ph]) * $temp;
$temp = $temp * $pt2[0][$ph];
}
# Calculation of type 2 failure for the other zones
else {
$pt2[$z][$ph] = $pt2[$z-1][$ph] * $pt2[0][$ph];
printf("Phase shift %d (Type 2 error): %.4f\n", ($ph+1), ((1$pt2[$z][$ph])*$temp));
$sum += (1 - $pt2[$z][$ph]) * $temp;
$temp = $temp * $pt2[$z][$ph];
}
#
$temp = $temp * $pt2[$z-1][$ph];
#Calculation of type 1 failure when applicable
if ($ms[$z][$ph] == 1) {
printf("Phase shitf %d (Type 1 error): %.4f\n", ($ph+1),((1 $pt1[$ph]) * $temp));
$sum += (1 - $pt1[$ph]) * $temp;
$temp = $temp * $pt1[$ph];
}
}
printf("P(success): %.4f \n",$temp);
print "Sum = " . ($sum + $temp) . "\n";
}
Results from the program
Zone 1
======
Phase shift
Phase shitf
Phase shift
Phase shitf
Phase shift
Phase shift
Phase shift
P(success):
Sum = 1
Zone 2
======
Phase shift
Phase shitf
Phase shift
Phase shift
Phase shift
Phase shitf
Phase shift
P(success):
Sum = 1
Zone 3
======
Phase shift
Phase shift
Phase shift
Phase shitf
Phase shift
Phase shift
P(success):
Sum = 1
Zone 4
======
Phase shift
Phase shift
Phase shift
Phase shift
Phase shift
Phase shitf
P(success):
Sum = 1
1 (Type
1 (Type
2 (Type
2 (Type
3 (Type
4 (Type
5 (Type
0.3198
2
1
2
1
2
2
2
error):
error):
error):
error):
error):
error):
error):
0.0400
0.0192
0.0753
0.0346
0.1579
0.1885
0.1648
1 (Type
1 (Type
2 (Type
3 (Type
4 (Type
4 (Type
5 (Type
0.0974
2
1
2
2
2
1
2
error):
error):
error):
error):
error):
error):
error):
0.0784
0.0184
0.1387
0.2629
0.2415
0.0364
0.1262
1 (Type
2 (Type
3 (Type
3 (Type
4 (Type
5 (Type
0.0358
2
2
2
1
2
2
error):
error):
error):
error):
error):
error):
0.1153
0.1958
0.3228
0.0330
0.2088
0.0886
1 (Type
2 (Type
3 (Type
4 (Type
5 (Type
5 (Type
0.0111
2
2
2
2
2
1
error):
error):
error):
error):
error):
error):
0.1507
0.2409
0.3465
0.1915
0.0570
0.0023
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
Appendix F
Acronyms and Abbreviations
Word
Explanation
AEM
Actuator Electronic Module
CAPEX
Capital Expenditures
CBS
Cost Breakdown Structure
DHSV
Down Hole Safety Valve
FMECA
Failure Modes, Effects and Criticality Analysis
HAZID
Hazard Identification
HAZOP
Hazard and Operability
ICV
Interval Control Valve
LCC
Life Cycle Cost
LCP
Life Cycle Profit
MTTF
Mean Time To Failure
OPEX
Operational Expenditures
Phase
The period between a ICV is moved until another ICV is moved in an
intelligent well system
Phaseshift
A point of time where one of the Sliding Sleeves in the well is moved
Production related failures
Failures that only influences the production
REGEX
Regularity Expenditures
Safety related failures
Failures that influences the safety
SCRAMS
Surface Controlled Reservoir Analysis and Management System
Zone
A separate producing reservoir
110
Diploma Thesis: Reliability and Life Cycle Cost/Profit Assessment of Intelligent Well Systems
Stud. Techn. Knut Eivind Borg
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Stud. Techn. Knut Eivind Borg
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