Life span of oil reservoirs: Examples from three old reef pools in Alberta, Canada
Bibliographic record
Abstract
ABSTRACT After reaching a plateau, the production of oil reservoirs starts to decline, which substantially determines their life expectancy. Declining production data from three 58- to 71-yr-old oil pools in Alberta, Canada, have been documented and analyzed to show and predict their life span. Two declining phases of oil production can be distinguished: a short 4–11-yr substantially declining phase and a long 23–35-yr gradually declining phase. Well-fitted power regressions of the two declining phases of the pools have been observed with high correlation coefficients of 0.95–0.99 and used to forecast future annual oil production for the next 30 yr, yielding projected life spans of at least 88 to 101 yr. These pools have been producing oil at high water cuts of greater than 0.9 for 30–38 yr, currently more than 0.980 and 0.996, but still have potential to continue to deliver for at least another 30 yr. Production data from two pools indicate that 11% of original oil in place and 16%–26% of cumulative oil output were produced at a high water cut of greater than 0.9. These observation results can be used to judge other similar reef oil pools’ future production potential. This study provides a physical example to test the Hubbert curve and implies that the Hubbert curve should provide only the minimum future ultimate recoveries and the shortest future production times.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".