Uncertainty in Proved Reserves Estimates by Decline Curve Analysis
Bibliographic record
Abstract
Abstract Reserves estimation is crucial to the petroleum business as it involves all financial aspects of the petroleum companies. Among reserves classification, proved reserves always capture the most attention because most value is attached to it. By the SPE definition, proved reserves must be estimated by reliable methods that have a high-at least a 90% probability (P90) - that the actual quantities recovered will equal or exceed the estimate. Decline curve analysis (DCA) is one of the most commonly used methods for proved reserves estimation throughout the industry. Through the DCA, a production history is fitted with a trend line, then the line will be extrapolated to an economic limit for the reserves estimation. If linear regression is used, the line is the "best estimate" that represents the performance, which corresponds to the 50th percentile value (P50). This practice, therefore, conflicts with the proved reserves definition. In this paper a method is derived to estimate the variation of the reserves spread (the difference between P50 and P90). Compared to Monte Carlo, the method gives good results. The analytical solution is then used to study the sensitivity analysis of the spread and a field application. The study covers all decline models (exponential, hyperbolic and harmonic models) and both cases where the decline exponent is known and unknown.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".