A New Approach for Fast Evaluations of Large Portfolios of Oil and Gas Fields
Bibliographic record
Abstract
Abstract This paper presents a new systematic process for the evaluation of large portfolios of oil and gas fields where the performance and economic value of an entire portfolio is very rapidly derived. The automation of this workflow relies on some key technological developments, namely an automated algorithm for decline-curve analysis, and data mining studies of workovers and new well performance. The automated decline-curve analysis tool presented here uses an event detection algorithm combined with quantile regression technique design to provide a robust probabilistic estimate of future PDP (proved-developed-producing) reserves on a well-by-well basis. Individual well behaviors are then aggregated stochastically to provide expected field and portfolio declines, with uncertainty ranges. Future well trends are estimated using probabilistic type-curves computed by data mining algorithms. Wells and fields are then individually assessed and ranked in terms of reserves and production metrics and financial information can be used to assess the value of the portfolio with a high-level of granularity. A large portfolio of oil and gas fields in Texas and Louisiana is analyzed in this paper using the proposed methodology. For this portfolio computed decline rates, PDP reserves and cash flows are provided. Analysis of expected production from new wells and estimates of workover performance are also presented. The analytical approach presented in this work is being used daily for comprehensive portfolio evaluations in the US and represents a significant change in the way divestitures and acquisitions evaluations are currently performed in the industry.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".