The Evaluation, Classification and Reporting of Unconventional Resources
Bibliographic record
Abstract
Abstract The evaluation of unconventional resources presents many challenges, one of which is a need to evaluate all resource classes, not only Proved reserves. The evaluation of hydrocarbons is described as consisting of three steps: Estimation, Classification, and Reporting, with a concomitant requirement for auditing. The nature of estimates is discussed, in particular, that there is always an associated uncertainty and some fundamental principles of classification are reviewed. The classification of unconventional hydrocarbons is discussed, with an emphasis on Discovered Petroleum Initially-In-Place and Contingent Resources, which are formulated as a series of Decision rules. Information on production potential, either from tests or analogs, is of particular importance when classifying unconventional hydrocarbons, and the typical lack of defined pool boundaries requires that careful consideration be given to the area assigned to various resource categories, both of which points emphasise the need for an understanding of the detailed reservoir geology. The Petroleum Resource Management System (PRMS), supplemented by the Canadian Oil and Gas Handbook (COGEH), has provided the basis for the discussion in this paper, but it is impossible in a short paper to capture these systems in full, and reference should be made to the original source documents for details. The emphasis in this paper is on recovery through wells, but some brief mention of mined hydrocarbon recovery is made. The exploitation of unconventional hydrocarbons is in its early stages, and evaluation procedures, including classification, are still under development. Many aspects of the evaluation process are not addressed in this paper, and those that are, are likely to require further development and modification.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".