Bibliographic record
Abstract
Abstract Traditionally reserves management has been an exclusive function for Reservoir Engineers; however in mature fields the need to involve Production, Facilities, Geology, Operations Engineers and Financial Analysts is critical. This paper presents a suggested workflow to improve the way reserves are evaluated, supported, booked and documented with a concerted effort of a multidiscipline team. This paper is focused on tight gas reservoirs and the central idea is to reduce the uncertainty that is always present when dealing with reserves. Most of the discussion is concentrated in understanding and verifying the hyperbolic decline “b” factor to be used for production decline analysis; we normally understand the mathematical meaning, but very rarely spend the time understanding the physical meaning and the consequences of using the wrong value. Once the “b” factor is defined, the proper documentation must be filed for each reserve record and the focus becomes the economic analysis of those reserves which is critical for future development especially in a very volatile economy in which prices are fluctuating a lot.
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 | 0.070 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".