P1-2-21
Two-Level Chance Tables for Prospect Risking
Charles Stabell
Decision Resources & Designs, Oslo, Norway
Introduction
Risk assessment in petroleum exploration is largely a subjective exercise where there is a
premium for consistent, calibrated assessments across prospects, projects and teams. Risk
tables have recently been proposed by Milkov (2015) as a means to address biases and
inconsistencies in assessments, particularly for teams in small and medium-sized companies with
prospects in frontier areas. Risk tables provide unambiguous risk estimates based on a clear
categorization of the geological context and level of knowledge concerning a prospect. This paper
argues that the tables need to be extended and adjusted, primarily in order to capture the
distinction between shared play risks and conditional target risks..
The Prospect Assessment Workflow
Prospect assessment is most often performed within a stage gate decision process where the
prospect is matured from lead to drillable prospect. The overall assessment process, however,
can be described as a problem solving process with a set of key activities (Figure 1)..
Fig 1. Prospect assessment workflow (play based exploration).
The workflow defines some key properties of the assessment process. One is that the chance of
success (COS) of the prospect is estimated after the assessment of the success case volumes as
the COS is the chance of obtaining the minimum success case volumes or more.
A second key aspect is that we have distinct assessment of shared play probabilities and
conditional prospect probabilities: the conditional probabilities of a chance factor are conditional on
that the corresponding play factor is adequate.
The Milkov Risk Tables (R-Tables)
After positioning the role of estimates of geological chance of success (COS) in prospect
decisions, Milkov (2015) reviews the current practice of breaking down estimating the prospect
COS into the task of estimating the chance of adequacy (COA) of the geological chance factors
(trap, reservoir, seal and source) that all need to succeed for an exploration success. He then
reviews two common approaches that are designed to assist the risking process - using literals
instead of numerical COA estimates and the precepts of the “chance (of) adequacy matrix” for how
COA estimates should be constrained by the level of knowledge of the exploration situation.
Fig 2. Example of Chance (of) Adequacy Matrix).
Milkov dismisses both approaches because they do not consider explicitly geological attributes of
the exploration situation. The chance of adequacy (COA) matrix is dismissed for two additional
reasons: because it is imprecise (ambiguous) as it provides a range for the COA values. The
other reason is that low knowledge conditions should rather be viewed as random drilling and
therefore correspond to low as opposed to the prescribed intermediate COA estimates..
This is not the place for a detailed review of the uses of the COA matrix. However, the
prescription that intermediate COA values should be associated with low knowledge follows from a
need to counter the common human judgment bias (“the ambiguity bias”) where low knowledge is
deemed high risk (low COA values) -- as it is a situation that we want to avoid – rather than given
more or less a 50-50 chance.
It is interesting that Milkov misses the “ambiguity bias” in his broad review of the human judgment
literature. He argues that chance factor estimation is a challenging task that needs to be assisted
in order to counter and mitigate the effects of human biases and errors. This is particularly true
for small and medium sized companies assessing prospects in frontier areas. He concludes that
there is a need for a process (an algorithm) that automates the judgment task.
Design of Risk Tables
The proposed R-Tables are designed according to three main broad principles:
•
Distinguish between DATA (hard data, how and what we know) and MODEL (how we
envisage things (geological controls) may work). In other words, there is a risk table for each
risk factor; each risk table is multiple-dimensional with respectively one or more MODEL
dimensions and one or more DATA dimensions.
Fig 3. General structure of R-Table for one chance factor
The R-Tables can be reviewed in more detail in the context of five key issues (I1 – I5).
I1 - Shared play risks and conditional target (segment) risks
The R-Tables do not distinguish between shared play risks and conditional target risks. One might
conclude that the tables are meant to be applied in proven plays. However, this does not gel with
the idea that the risk tables are precisely needed for assessing prospect COS in frontier – i.e.,
unproven plays.
I2 - Assessing chance of minimum success case volumes
COS is our estimate of the chance that we get the minimum success case volumes. R-Tables do
not handle the issue.
I3 - Limited argumentation for chance values
The paper proposes a complete set of R-Tables with chance estimates for every combination of
MODEL and DATA categories. However, there is no presentation of the logic or argumentation for
the chance values presented in the tables.
Figure 4 shows R-Table for the probability of reservoir facies presence.
Fig 4. R-Table Reservoir facies presence (Rp) COA.
Figure 5 shows how COA varies across depositional environments (MODEL dimension categories)
for the two highest level knowledge DATA dimensions.
Fig 5. R-Table for Rp over MODEL categories for 2 DATA categories
There is no explicit argument for why COA varies across the MODEL categories for the DATA
category where all wells in the play less than 50 km from the target have reservoir. The implicit
argument would seem to be that as we move from blanket deposits to channels and fans, reservoir
facies presence has more local variability.
What is less obvious is on what basis the COA variations have been defined. It would seem that
the MODEL categories are interpreted as cardinal groups where the value of the COA varies in 5%
intervals. The only exception is for the last category (Fractured basement, porous lava) where
there is a 20% jump.
Similarly, we see a common 10% COA interval when we move between the DATA categories
shown in Figure 4. The only exception is the 5% interval between the “all wells < 50 km have
reservoir” DATA category and the “all wells < 100 km have reservoir” DATA category. The
scaling is not explained or justified.
I4 - Argument that LOK is identical to NO KNOWLEDGE
As noted already, it is not easy to check the low knowledge cases from the risk tables. However,
Milkov has embraced Lowry’s (2005) suggestion that low knowledge corresponds to no knowledge
and therefore to a situation with random drilling with corresponding very low chance estimates.
The argument does not seem to make sense as how can we categorize on MODEL dimensions if
there is NO knowledge?.
I5 - Handling of seismic anomaly data (DHI)
The R-Tables introduce the presence of DHI (or lack of DHI when they are expected) as DATA
dimensions for estimates of the probability of presence of reservoir facies presence and the
probability of migration from a mature kitchen. However, if the DHI is a realistic and credible
indicator of hydrocarbon presence, then it should impact all chance factors.
Two-level Chance Tables
We propose a two-level Chance Tables (C-Tables) approach to prospect risking: Level 1 is for
assessment of the shared play COA and level 2 is for assessment of the conditional segment
(target) COA – conditional on that the corresponding shared play control is adequate.
The C-Tables address the main issues raised with the R-Tables. They build on the R-Tables with
both MODEL and DATA dimensions where the MODEL dimensions cover all conventional
exploration situations. The C-Tables use the same set of chance (risk) factors and produce single
estimates of COA.
The key principles for the Chance Tables (C-Tables) approach beyond that we distinguish
between estimates of the shared play COA and estimates of the conditional target COA are:
•
Some of the MODEL dimensions apply only to the target, conditional level (D in Figure 5), but
most of the dimensions apply to both the play and our target in the play (C). There is a
MODEL dimension (E) that captures the main local geological process that might overwrite or
modify the regional, play-level processes that control the adequacy of the play.
Fig 5. General structure of C-Table for one chance factor
•
There are separate DATA dimensions for the play level chance estimate (A) and for the
conditional target chance level (B). This reflects that the play level chance estimates are
based on regional DATA while the conditional chance estimates are based on more local,
target specific DATA.
•
Where sufficient data is available, the estimates of chance factors are data based. In other
words, the chance estimates reflect quantitative data attributes.
•
With limited data, we follow the “chance (of) adequacy matrix” (Rose, 2001) precept and posit
a COA value that is intermediate (in the 0.4 - 0.6 range).
•
Presence of seismic anomaly information (or CSEM information) is not included in the CTables. This data (when present or when expected, but lacking) is considered in a second
Bayesian Risk Modification step once we have an estimate of the overall COS ignoring the
anomaly information (see Stabell et al, 2003).
MODEL Dimensions
Most chance factors have different MODEL dimensions. This follows from the fact that the chance
factors represent distinct geological processes.
The MODEL dimensions that are relevant to the shared play level chance of adequacy are also
applicable to the conditional target level chance of adequacy; examples are the depositional
environment and the lithology of the reservoir rock. Some of the MODEL dimensions, however,
are only meaningful or relevant at the target level; examples are closure height relative to seismic
resolution, structural style of seal.
There are also a set of MODEL dimensions that are generic modifiers or conditioners of the play
level geological controls (processes). Examples of conditioning controls would be erosion; local
increases in shale fraction; local limitation of accommodation area; local migration “shadow(s)”;
rafting of source rock in migration fetch area; discontinuities in carrier bed for migration; not
enough migrated HC to fill a minimum success case volume; sub-seismic faulting of seal; thinning
due to distal deposition.
DATA Dimensions
The DATA dimensions cover both direct information (such as wells, seismic, CSEM, seeps,
outcrops, etc.) and indirect information (such as based on basin modeling or other numerical
models.
A key discriminating play-level (regional) DATA dimension is whether or not there are wells
(discoveries) that prove that the play works for the chance factor.
Low level knowledge is a situation where we have few wells, sparse 2D lines and no numerical
basin model. In the low knowledge category, delineation of the play fairway is based on a
conceptual basin evolution model and global analogues.
The DATA dimensions for the conditional segment COA cover local controls or gives data on how
local mediating controls are potentially present across the play. The local information will most
often be seismic data that applies to the target; it will be wells in the same play (chance play
element CPE).
Figure 6 shows the illustrative C-Table for COA of reservoir facies presence (Rp) with no local
conditioning process. The resulting COA estimate can be adjusted by a minimum success case
modifier.
Fig 6. Illustrative C-Table for Rp with no local conditioning process
References
Lowry, D.C., Sutill, R.J., Taylor, R.J. (2005) “Advances in risking exploration prospects” APPEA J.,
40, 143–158.
Milkov, A.V. (2015) “Risk tables for less biased and more consistent estimation of probability of
geological success (PoS) for segments with conventional oil and gas prospective resources”
Earth-Science Reviews, 150, 453–476.
Stabell, C.B., Lunn, S. Breirem, K. (2003) “Making Effective Use of a DFI: A Practical Bayesian
Approach to Risking Prospects for Seismic Anomaly Information”, Proceedings of HEES
(Hydrocarbon Economics and Evaluation Symposium), SPE 82020, Dallas, April 5-8, 2003.