Stochastic Modelling of Shale Gas Resource Play Economics
Bibliographic record
Abstract
Abstract Unconventional petroleum resource plays present unique assessment challenges. These large, single accumulations cannot be counted and analysed as discrete entities that are delineated by down-dip water contacts. Equally important, the main assessment challenge relates to exploitation risks and uncertainties. This paper presents an integrated stochastic assessment framework for decisions related to shale gas resource plays. The shale gas resource play is modelled as a set of discrete cells that have not been explored (exploited) and that have the potential for economic production. The distinctive aspect of the modelling tool is the use of stochastic simulation to calculate the risks of failure in either the exploration, the appraisal/pilot, or the exploitation phases of the project on the basis of both sub-surface uncertainties and above-surface activity performance, cost and duration uncertainties. The tool also generates stochastic performance metrics that capture alternative outcome scenarios, economic returns and the delivery schedule of production and reserves. The performance metrics support both project-level and portfolio-level decisions related to unconventional resource plays. Project-level application is illustrated using data from a Canadian shale gas resource play.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".