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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".