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Record W2057904907 · doi:10.2118/134602-pa

Using the SPE/WPC/AAPG/SPEE/SEG PRMS To Evaluate Unconventional Resources

2012· article· en· W2057904907 on OpenAlexaff
Phillip Chan, John Etherington, Roberto Aguilera

Bibliographic record

VenueSPE Economics & Management · 2012
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsUnconventional oilTight gasPetroleumPetroleum engineeringTight oilComputer scienceOil shaleFormation evaluationOperations researchGeologyEngineeringHydraulic fracturingPaleontology

Abstract

fetched live from OpenAlex

Summary It is the intention of the SPE/World Petroleum Council (WPC)/ American Association of Petroleum Geologists (AAPG)/ Society of Petroleum Evaluation Engineers (SPEE)/ Society of Exploration Geophysicists (SEG) Petroleum Resources Management System (PRMS) to provide a consistent approach to estimating petroleum quantities, evaluating development projects, and presenting results within a comprehensive classification framework. The reserves and resources definitions and application guidelines are designed to be applicable to both conventional and unconventional petroleum accumulations, regardless of their in-place characteristics, the extraction method applied, or the degree of processing required to yield a marketable product. The fact that unconventional resources are usually pervasive throughout a large area and are not significantly affected by hydrodynamic influences may require different approaches in evaluation. Assessments may include an increased sampling density to define uncertainty of in-place volumes and the variations in quality of reservoir and of hydrocarbons and their detailed spatial distribution for the design of specialized extraction methods. This paper summarizes the special problems in the estimation and evaluation of shale gas. However, similar procedures can be used for other unconventional resources. The material is largely drawn from the recently published SPE Application Guidelines to the PRMS, supplemented with illustrations from actual field examples. The rapidly advancing exploitation of unconventional resources has opened up many development opportunities, especially in North America. Shale gas and bitumen have already caused major impact on energy supply. We anticipate these opportunities will expand rapidly throughout the world. Achieving a better understanding of the special problems in unconventional-resources evaluation will help us build on PRMS to develop a more consistent approach to classification and categorization, accounting for unique project risks and uncertainties.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.035
GPT teacher head0.261
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations6
Published2012
Admission routes1
Has abstractyes

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