Bibliographic record
Abstract
Technology Focus As a member of the JPT Editorial Committee, I am privileged to review papers presented at SPE events during the last year in the area of Reserves and Asset Management. I am always impressed by the highly skilled, innovative members of our Society who address the constant change in our industry in these papers. Recently, many of the reserves papers have focused on changes in reserves and resource estimation resulting from the introduction of the Petroleum Resource Management System (PRMS) and the US Securities and Exchange Commission’s (SEC’s) Modernized Rules. Last year, many of the papers dealt with theoretical aspects of reserves estimation in unconventional plays. This year, most of the papers dealt with unconventional reserves, focusing on integration of theoretical and practical aspects of the engineering principles used to estimate reserves and resources. Several papers went full circle to address how issues around PRMS or the SEC’s Modernized Rules affect reserves and resource estimation in unconventional resources. There was a similar shift in asset-management papers. Prior years were weighted heavily toward theoretical-optimization approaches, primarily focused on surface facilities. This year, there were many excellent papers addressing the practical application of those principles in technically challenging, high-cost environments. Integration of surface and subsurface components to improve efficiency was another recurring theme. The fact that I could select only a few of the many outstanding papers that I reviewed highlights the importance of attending the venues at which these papers are presented. The insight provided during the presentation’s opportunity to ask questions yields valuable information that cannot be obtained by reading the paper alone. I selected the papers for highlighting and those recommended for additional reading with a view to the needs and interests of the membership of our global society. I hope I found something that will benefit each of you. Reserves/Asset Management additional reading available at OnePetro: www.onepetro.org SPE 128542 • “Greater Plutonio—Real-Time Reservoir Management in a High-Cost Deepwater Environment” by Dave Booth, SPE, BP plc, et al. (See JPT, September 2010, page 43.) SPE 147623 • “Estimated Ultimate Recovery as a Function of Production Practices in the Haynesville Shale” by V. Okouma, Shell Canada Energy, et al. SPE 142822 • “Tight Gas Modeling Frameworks for Improved Reservoir Management and Application to the Moxa Development Area” by Felipe Gallego, BP plc. SPE 146788 • “Field-Development Optimization Under Uncertainty: Screening Models for Decision Making” by B.A. Ogunyomi, SPE, University of Texas at Austin, et al.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.078 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".