Portfolio analysis of petroleum fields and prospects: a robust statistical method
Bibliographic record
Abstract
Portfolio analysis of several prospects, fields or assets is an important part of economic analysis routinely done by most exploration, development and production companies, often for large dollar amounts. Yet the methods used have not changed for decades, most are done in complicated and difficult to audit spreadsheets, and commonly they are not statistically robust, meaning they sometimes give incorrect results. The Curtin University Petroleum Geology Group has an active research program working with the Curtin Statistical Group to improve assessment of petroleum volumetrics, risking, scenario and portfolio analysis. This note provides a simple case study of portfolio analysis using an area in the North West Shelf in which we have quickly mapped several leads, prospects and drilled traps. They span a range of risk outcomes from undrilled to drilled, of which some were dry and some were successful to varying degrees. The results allow us to demonstrate how to calculate and aggregate the volumes and dependencies for each structure, correctly add the results using a new statistical methodology and formulate the aggregated volumetric distribution for the portfolio. This workflow can be used for any portfolio in the petroleum and other industries.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".