Imperial College London

Dr Craig Smalley

Faculty of EngineeringDepartment of Earth Science & Engineering

Visiting Professor
 
 
 
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Contact

 

c.smalley

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Smalley:2018:10.1306/06051717018,
author = {Smalley, PC and Walker, CD and Belvedere, PG},
doi = {10.1306/06051717018},
journal = {American Association of Petroleum Geologists (AAPG) Bulletin},
pages = {429--445},
title = {A practical approach for applying Bayesian logic to determine the probabilities of subsurface scenarios: example from an offshore oilfield},
url = {http://dx.doi.org/10.1306/06051717018},
volume = {102},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - During appraisal of an undeveloped segment of a producing offshore oilfield, three well penetrations revealed unexpected complexity and compartmentalization. Business decisions on whether and how to develop this segment depended on understanding the possible interpretations of the subsurface. This was achieved using the following steps that incorporated a novel practical application of Bayesian logic.1. Scenarios were identified to span the full range of possible subsurface interpretations. This was achieved through a facilitated cross-disciplinary exercise including external participants. The exercise generated 12 widely differing subsurface scenarios, which could be grouped into 4 types of mechanisms: slumping, structural, depositional, and diagenetic.2. Prior probabilities were assigned to each scenario. These probabilities were elicited from the same subsurface team and external experts who performed step 1, using their diverse knowledge and experience.3. The probabilities of each scenario were updated by evaluating them sequentially with 21 individual pieces of evidence, progressively down-weighting belief in scenarios that were inconsistent with the evidence. For each piece of evidence, the likelihood (chance that the scenario could produce the evidence) was estimated qualitatively by the same team using a “traffic-light” high-medium-low assessment. Offline, these were converted to numerical likelihood values. Posterior probabilities were derived by multiplying the priors by the likelihoods and renormalizing to sum to unity across all of the scenarios.4. The most probable scenarios were selected for quantitative reservoir modeling, to evaluate the potential outcomes of business decisions, given each scenario.Of the 12 scenarios identified in step 1, most were strongly down-weighted by the sequential revisions against evidence in step 3; after this, only scenarios in the “slumping” group retained significant posterior probabilities. The
AU - Smalley,PC
AU - Walker,CD
AU - Belvedere,PG
DO - 10.1306/06051717018
EP - 445
PY - 2018///
SN - 0149-1423
SP - 429
TI - A practical approach for applying Bayesian logic to determine the probabilities of subsurface scenarios: example from an offshore oilfield
T2 - American Association of Petroleum Geologists (AAPG) Bulletin
UR - http://dx.doi.org/10.1306/06051717018
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000430928900004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/79016
VL - 102
ER -