Bibliographic record
Abstract
Technology Focus While selecting the papers for this feature, I noticed two things. First, the topic is wide and varied. It includes everything from simulation models for new-field development to case histories of mature operations. In between, there were discussions of numerous types of sophisticated assessment tools presented for consideration and there was a consistent theme of the need for optimization. My second observation was that this group of papers provided fascinating reading and offers a wealth of diversified knowledge from a representative cross section of our industry. I never cease to be impressed by the intelligence and innovation of the members of our global oil and gas industry. I am honored to be part of a group with such capable and committed individuals. I also noticed the difference between the two components of this feature. Reserves have been a dynamic component of our industry over the last few years, with the adoption of the SPE/World Petroleum Council/American Association of Petroleum Geologists/Society of Petroleum Evaluation Engineers Petroleum Resources Management System (SPE-PRMS) in 2007 and changes to reporting regulations in Canada, the USA, and other countries. In many cases, these new systems and regulations have resulted in step changes in the way reserves and resources are categorized and reported. Asset management, on the other hand, has exhibited a more continual growth toward identifying and capturing more-sophisticated processes and more-complex procedures in search of maximizing recovery while minimizing costs. Because of the recent changes, the papers selected this year focus on reserves categorization and reporting, while the papers recommended for additional reading focus more heavily on asset management. As the industry becomes more familiar with SPE-PRMS and the new regulatory requirements, it is likely that future editions of the Reserves/Asset Management feature will focus more heavily on the asset-management component of our industry. Reserves/Asset Management additional reading available at OnePetro: www.onepetro.org SPE 123931 • "Managing a Giant—50 Years of Groningen Gas" by Niels Dijksman, Royal Dutch Shell, et al. OTC 20125 • "I-Field Implementation Enables Real-Time Reservoir Management of Newly Developed Saudi Fields" by Said S. Al-Malki, SPE, Saudi Aramco, et al. SPE 121426 • "Real-Options Analysis in Petroleum Exploration and Production: A New Paradigm in Investment Analysis" by B. Jafarizadeh, SPE, University of Stavanger, 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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.053 |
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".