Joint Well Placement and Control Optimization
– Path Towards a Real Field Application –
M. Bellout† , D. Echeverrı́a Ciaurri‡
L.J. Durlofsky, B. Foss and J. Kleppe
‡
†
Department of Petroleum Engineering and Applied Geophysics, NTNU
Department of Petroleum and Energy Analytics, T.J. Watson Research Center, IBM
8th International Conference on
Integrated Operations in the Petroleum Industry,
Trondheim, September 25 – 26, 2012
Table of Contents
1
Part One: Motivation and Methodology
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
2
Part Two: Integrated Implementation
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
3
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Table of Contents
1
Part One: Motivation and Methodology
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
2
Part Two: Integrated Implementation
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
3
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
Well Placement, Control Optimization Problem
Problem:
max f (x, u)
x,u
f: commonly net present value, or cumulative oil
x: discrete well placement variables; vertical or horizontal well coordinates
u: continuous well control variables; BHP or rates
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
Intro to Joint, Sequential Approach
Joint approach / embedded optimization
an approach which does not handle well location and controls jointly is
unlikely to yield optimal solutions due to the interdependency between a well
configuration and its associated controls.
we propose a joint approach where the control optimization is embedded
within the search for the optimal well placement
Sequential approach
a sequential approach refers to using a fixed or reactive well control strategy,
(e.g., shut in of producer at water-cut threshold), during the optimization for
well placement
at well placement solution, well controls are optimized
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
Basic Methodologies, Building Blocks
Well placement and control problem have distinct characteristics; therefore,
traditionally have been treated with different methologies
Well controls (e.g., BHP and/or rates) stated in continuous variables, can be
optimized efficiently by gradient-based techniques (e.g., SQP [SNOPT]), due
to the computation of gradients through adjoints (GPRS, MRST); proper to
handle with local search approaches
Well placements formulated as integer variables (gradients not defined),
usually solved by derivative-free methods; rough optimization landscapes,
local search probably not suited, global search features desired (pattern
search to some extent, GA, PSO, etc.)
We use derivative-free methods based on pattern-search procedures
(GPS/HJDS/HOPSPACK), which are easy to implement (e.g., no
population sizes to determine), mathematically sound, and easily
parallelizable (here, distributing complete control optimizations)
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
Joint vs Sequential Fixed, Reactive Approaches
Find optimal position of injector, among four producers with fixed location
Since only 2 discrete variables, we perform an exhaustive search
1st comment: optimal injector location dependent on control strategy under
well placement search
2nd comment: smoothing of NPV response surface for when control
optimization embedded, and it’s also clear for reactive approach
1100
1
1
1
2
900
20
30
500
500
40
300
50
3
10
4
20
30
40
100
(a) NPV, sequential fixed
300
50
3
10
50
500
40
300
50
700
700
30
40
900
900
20
700
30
2
10
10
20
1100
1100
2
10
4
20
30
40
100
50
(b) NPV, sequential reactive
M. Bellout, D. Echeverrı́a Ciaurri
3
10
4
20
30
40
100
50
(c) NPV, joint approach
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
Tabulated Results One Well Case
At the well configuration found, an additional control optimization
(indicated by asterisk in table) is performed
The joint approach yields a final increase of 4.2% and 5.9% in NPV,
$46 MM and $63 MM, respectively, with respect to the sequential fixed and
reactive approaches, respectively
Table: Injector well location with the highest NPV obtained for the three exhaustive
explorations
Approach
Location [x,y]
fixed
sequential fixed∗
reactive
sequential reactive∗
joint
joint∗
M. Bellout, D. Echeverrı́a Ciaurri
[18,26]
[17,42]
[12,36]
NPV [$MM]
976
1091
1061
1074
1135
1137
Joint Well Placement and Control Optimization
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Table of Contents
1
Part One: Motivation and Methodology
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
2
Part Two: Integrated Implementation
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
3
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
Challenges Towards Real Field Implementation
Main task is bridging the gap from research results to an application that
works in a real field situation
New challenges:
larger number of blocks
more complex geometry / grid structures, corner point gridding
deviated / horizontal wells
boundary constraints, distance between wells
well alignment with respect to platform
advanced production techniques, e.g., gas lift
New solution:
enhance each of the parts of our methodology
add new functionality where needed
integrate all parts into a coherent implementation
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
Enhancement and Development of Tools and
Methodologies
Enhancements:
Larger emphasis on parallelization for well placement optimization; involves
multicore programming on a larger scale (distributed computing of
simulations / optimizations)
Introduction of newer version of reservoir simulator for more efficient runs
and computation of gradients needed for optimization; ADGPRS (Automatic
Differentiation General Purpose Research Simulator), Stanford University
New functionality:
Horizontal wells require more demanding geometrical calculations, e.g.,
regarding well connection factor calculations; solved using MRST
(MATLAB Reservoir Simulation Toolbox), SINTEF Applied Mathematics
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
Example of Using MRST Toolbox
Data describing model grid geometry available in Matlab
Use of MRST functions to perform well connection factor calculations,
applying boundary constraints on well coordinates
Close collaboration with developers (e.g., S.Krogstad at SINTEF Applied
Mathematics)
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
IO Center Platform for Integrated Solutions
Notice the integration of people, tools and methodologies from different IO
Center research partners, e.g., Stanford University, SINTEF Applied
Mathematics, IBM ...
... into a coherent solution to ...
... solve a problem provided by IO Center industry partner Total E&P Norge
Huge advantage of the IO Center to function as a platform to develop
integrated solutions that benefit all partners
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
The Integration of Tools and Methodologies
The Lego analogy
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
The Integration of Tools and Methodologies
The Lego analogy
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
14 / 26
Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
The Integration of Tools and Methodologies
The Lego analogy
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
15 / 26
Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
The Integration of Tools and Methodologies
The Lego analogy
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
16 / 26
Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
The Integration of Tools and Methodologies
The Lego analogy
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
17 / 26
Table of Contents
1
Part One: Motivation and Methodology
Well Placement, Control Optimization Problem
Intro to Joint, Sequential Approach
Joint vs Sequential Fixed, Reactive Approaches
2
Part Two: Integrated Implementation
Challenges Towards a Real Field Implementation
Enhancement and Development of Tools and Methodologies
3
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Model Introduction
North Sea field
Operator Total E&P Norge AS
Reservoir in Frigg formation
Depth ∼ 1750 m
M. Bellout, D. Echeverrı́a Ciaurri
21m oil rim, gas cap
Pressure support by aquifer
Four wells planned
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Problem Description and Constraints
Four horizontal wells planned
General constraints on trajectories
Each well described by six coordinates:
welli = [(x y z)heel (x y z)toe ]i
In total 24 discrete well placement
variables
Implementation currently in testing
phase: test runs to study constraints on
well placement solutions, general
algorithmic performance
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Production Test Results
Very short production time, 200 days actually planned production time
frame, which is 15 ∼ 20 years
Very high pressure drawdown set to wells
Increase in FOPT > 90% for this very limited test case
3
2150
FOPT / FWPT [1e6 Sm3]
NPV [1e6 $]
2.5
1900
1650
2
1.5
1
oil − init.wplc.
wat − init.wplc.
oil − fin.wplc.
wat − fin.wplc.
0.5
1400
0
96
test run
192 288 384 480 576 672 768 864
# reservoir simulations
M. Bellout, D. Echeverrı́a Ciaurri
0
0
20
40
60 80 100 120 140 160 180 200
production time [days]
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Well Placement Test Results
Constraint handling philosophy: introduce constraints as need becomes
evident, based on test results
Here, found solution presents crossing well trajectories that are not
implementable in practice
Base case well positions (left), final well positions for test run (right)
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Further Work, Challenges
Introduce geometric constraints to prevent crossing well trajectories
Load balancing of computing nodes in parallel implementation (e.g.,
asynchronous configurations)
Introduce control optimization into well placement algorithm
Continue bridging-the-gap process: introduce more realism into case (e.g.,
improved production settings, more realistic time frames)
Continue collaborative effort and establish work processes with industry
partner
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Forthcoming Enhancements
Optimal joint well placement and control under geological uncertainty
collaboration with H. Wang from Rutgers University
H. Wang et al, Optimal Well Placement Under Uncertainty Using a
Retrospective Optimization Framework, SPE Journal, 17(1), 2012
Model-based derivative-free methods for optimal well placement part
collaboration with A.R. Conn from IBM
Surrogate for well control optimization
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
References
For more on this work and references, see:
Bellout, M.C., Echeverrı́a Ciaurri, D., Durlofsky, L.J., Foss, B.A., Kleppe, J.
‘Joint Optimization of Oil Well Placement and Controls’, Comput. Geosci.,
16(4), 1061–1079 (2012)
Bellout, M.C., Echeverrı́a Ciaurri, D., Durlofsky, L.J., Foss, B.A., Kleppe, J.
’Joint well location and production optimization’ presentation at the Oil and Gas
Production Optimization Conference organized by Petrobras and NTNU/IO Center,
Rio de Janeiro, Brazil, May 14-15, 2012
Bellout, M.C., Echeverrı́a Ciaurri, D., Durlofsky, L.J., Foss, B.A., Kleppe, J.
’Joint Optimization of Oil Well Placement and Production Controls’ presentation at
the Smart Fields 6th Annual Affiliate Meeting, Stanford, November 14 - 15, 2011
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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Part One: Motivation and Methodology
Part Two: Integrated Implementation
Part Three: Application to a Real Field Case
Real Field Case
Production, Well Placement Test Results
Further Work, Challenges
Acknowledgements
Total E&P Norge AS (Stavanger, Norway)
S. Krogstad, PhD (SINTEF Applied Mathematics, Oslo, Norway)
Oleg Volkov, PhD (Research Associate, Stanford, CA)
Dr. A. Conn (IBM, NY)
M. Bellout, D. Echeverrı́a Ciaurri
Joint Well Placement and Control Optimization
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