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Record W1999035071 · doi:10.2118/1214-0084-jpt

Technology Focus: Reserves/Asset Management (December 2014)

2014· article· en· W1999035071 on OpenAlexaboutno aff
Delores Hinkle

Bibliographic record

VenueJournal of Petroleum Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsAsset (computer security)Value (mathematics)Reading (process)Executive summaryTheme (computing)Work (physics)Computer scienceOperations researchMarketingBusinessEngineeringPolitical scienceWorld Wide WebComputer securityLawFinanceMechanical engineering

Abstract

fetched live from OpenAlex

Technology Focus When I joined the petroleum industry almost 40 years ago, integration of the various aspects of our work seemed like something out of a science-fiction movie. As an industry, we have come a long way. More than half of the roughly 120 abstracts of papers presented this year in the category of reserves and asset management addressed integration in some fashion. Mercifully, advances in recording instruments and computers have released the handcuffs of limited data and cumbersome calculations. I fear the handcuffs may have been replaced by data overload, but I will leave that discussion for my colleagues who present the information-management section in this magazine. One popular theme was something we sometimes lose sight of: how to integrate investments and production into portfolios without destroying value. Both technical and planning advances have been addressed at length in the presentations from which the papers summarized and those recommended for additional reading were selected. Optimization was also a very popular topic among the papers I reviewed. With the current emphasis on the development of unconventional properties, both the technical and economic optimization of well spacing and stimulation techniques was addressed in numerous papers. Several authors used different approaches, but most came to the same conclusion: The balance between net present value and overdrilling or fracturing to maintain rate is a tricky one. In addition to the highly technical discussions, there were several papers addressing the maturing regulatory environment. Because the Modernized SEC Rules, Canada’s NI-51, and the SPE Guidelines for Application for Petroleum Resources Management System have been around for several years now, there were numerous papers on the handling of specific situations within those regulations. Resource estimation and reporting continue to be topics of much interest, specifically in unconventional reservoirs. I always enjoy reviewing papers for this section. When I sat back to reflect on what would be the most useful information to present, it occurred to me that the theme of growth and maturity was reflected in almost all of the papers. Our understanding of how to estimate unconventional reserves and resources properly has definitely grown over the last few years. Our realization that we should not look at any individual aspect of our business such as drilling or production without considering its place in the project life cycle, including its economics, reflects a maturity within the industry, as does learning how to reflect our assets effectively in a changing regulatory environment. It is satisfying to see the depth and strength of the advances in our industry, no longer the stuff of science fiction. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 170616 Interpretation of Recent SEC Reserves-Reporting Guidelines by Enrique Morales, SGS Horizon, et al. SPE 169984 Optimized Shale-Resource Development: Balance Between Technology and Economic Considerations by U. Ahmed, Baker Hughes SPE 169564 Estimation of Stimulated Reservoir Volume Using the Concept of Shale Capacity and Its Validation With Microseismic and Well Performance: Application to the Marcellus and Haynesville by A. Ouenes, Sigma Cubed, et al. SPE 170681 Integrating Unconventional- Resource Opportunities Into an Exploration-and-Production Portfolio by Larry Chorn, Halliburton, et al.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.248
Teacher spread0.241 · 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 teacher head, 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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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