Bibliographic record
Abstract
Petroleum resource evaluations have been performed by geologists, geophysicists, geochemists, engineers, and statisticians for many decades in an attempt to estimate resource potential in a given region. Because of differences in the geological and statistical methods used for assessment, and the amount and type of data available, resource evaluations often vary. Accounts of various methods have been compiled by Haun (1975), Grenon (1979), Masters (1985), Rice (1986), and Mast et al. (1989). In addition, Lee and Gill (1999) used the Michigan reef play data to evaluate the merits of the log-geometric method of the U.S. Geological Survey (USGS); the PETRIMES method developed by the Geological Survey of Canada (GSC); the Arps and Roberts method; Bickel, Nair, and Wang’s nonparametric finite population method; Kaufman’s anchored method; and the geo-anchored method of Chen and Sinding–Larson. Information required for petroleum resource evaluation includes all available reservoir data and data derived from the drilling of exploratory and development wells. Other essential geological information comes from regional geological, geophysical, and geochemical studies, as well as from work carried out in analogous basins. Any comprehensive resource evaluation procedure must combine raw data with information acquired from regional analysis and comparative studies. The Hydrocarbon Assessment System Processor (HASP) has been used to blend available exploration data with previously gathered information (Energy, Mines and Resources Canada, 1977; Roy, 1979). HASP expresses combinations of exploration data and expert judgment as probability distributions for specific population attributes (such as pool area, net pay, porosity). Since this procedure was first implemented, demands on evaluation capability have steadily increased as evaluation results were increasingly applied to economic analyses. Traditional methods could no longer meet the new demands. A probabilistic formulation for HASP became necessary and was established by Lee and Wang (1983b). This formulation led to the development of the Petroleum Exploration and Resource Evaluation System, PETRIMES (Lee, 1993a, c, d; Lee and Tzeng, 1993; Lee and Wang, 1983a, b, 1984, 1985, 1986, 1987, 1990). Since then, new capabilities and features have been added to the evaluation system (Lee, 1997, 1998). A Windows version was also created (Lee et al., 1999).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.518 | 0.339 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".