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Record W4247259446 · doi:10.2523/77419-ms

The Exploitation Enigma

2002· article· en· W4247259446 on OpenAlexaboutno aff
John Etherington, Patrick Leach, David Slaght

Bibliographic record

VenueProceedings of SPE Annual Technical Conference and Exhibition · 2002
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCitationExhibitionLibrary scienceComputer scienceDownloadWorld Wide WebOperations researchHistoryArt historyEngineering

Abstract

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The Exploitation Enigma John R. Etherington; John R. Etherington PRA International Search for other works by this author on: This Site Google Scholar Patrick E. Leach; Patrick E. Leach APM International Search for other works by this author on: This Site Google Scholar David H. Slaght David H. Slaght Marathon Canada Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. Paper Number: SPE-77419-MS https://doi.org/10.2118/77419-MS Published: September 29 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Etherington, John R., Leach, Patrick E., and David H. Slaght. "The Exploitation Enigma." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. doi: https://doi.org/10.2118/77419-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractEvaluation of exploitation drilling programs raises unique issues around risking logic plus estimated ultimate recoverable (EUR) and project economic uncertainties. Clarification of these issues will assist in managing portfolios of exploration, exploitation, and development investment opportunities.Under USA accounting rules, all wells drilled outside the area of proved reserves are classified as "exploratory wells" for computation of cost of finding. However, companies typically segregate wells into Exploration (targeting prospective resources in undiscovered accumulations) and Exploitation (targeting probable and possible reserves in and around discovered fields).Classification as "discovered" signifies a high degree of confidence that the accumulation's EUR exceeds an internal economic threshold for development. However, there remains significant risk of drilling a dry well within the projected field limits. At the field level, the non-proven area is part of the uncertainty distribution for discovered reserves. At the exploitation well level, risk of failure and success case volume uncertainty both come into play. This multiple level view of risk and uncertainty gives rise to the "exploitation enigma".Exploitation well results change the field/reservoir model, the EUR uncertainty profile and associated cash flow projections. Successful wells not only develop reserves within a drainage area but also modify the total field volume uncertainty distribution. Results redefine the remaining proved undeveloped, probable, and possible reserve volumes and their associated confidence levels. Dry exploitation wells also change the EUR distribution and increase investment costs without an increase in forecast production.This paper compares alternative approaches to the evaluation of exploitation programs. It concludes that the economic analysis to support exploitation drilling decisions should not be based solely on the individual well's anticipated results, but rather on its risk-weighted, incremental effect on the overall project value.IntroductionThe dictionary defines an "enigma" as something obscure and hard to understand or explain. Oil and gas exploitation drilling programs certainly fit this definition.Exploitation wells share attributes with conventional wildcats, exploration appraisal wells, and field development wells. While the definition of an exploitation well varies from company to company, generic characteristics include:drilled into unproven areas in and around an existing fieldhas significant chance of failureif successful can be completed and quickly tied into existing production facilitiesresults may significantly impact field economicsExploration managers often describe exploitation wells as low risk wildcats. However, they are concerned that these ventures, which may impact their performance metrics ("exploration success ratios", cost of finding, dry hole costs), are not within their control and may not fit their corporate strategies. Producing managers view exploitation wells as high risk and are often concerned that these programs take money out of development drilling programs. Further, exploitation well failures will negatively impact cost of development metrics upon which they are judged. Staff assigned to exploitation programs thus suffer distrust from both parts of the organization. Keywords: spe 77419, prospective resource, upstream oil & gas, slaght, project valuation, scenario, reserves evaluation, exploration, project level, etherington Subjects: Reserves Evaluation, Asset and Portfolio Management, Reserves classification, Project economics/valuation This content is only available via PDF. 2002. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.026
Scholarly communication0.0100.024
Open science0.0020.007
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0150.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.254
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2002
Admission routes1
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