Optimization of Well Placement and Production for Large - scale Mature Oil Fields
Bibliographic record
Abstract
Optimal oil field development strategies, especially well locations and production strategies for mature oil fields, should be determined to sustain yields.For a large-scale oil field, these problems are nonlinear, nonconvex, and computationally expensive.In this study, an efficient and robust derivative-free computational framework was developed to determine the optimal number, locations, and injection/production rates of infill wells for mature oil fields.The characteristics of mature fields were briefly described; optimization formulation and computational framework were presented.For this problem, the robust and parallelizable PSwarm, a hybrid of a pattern search algorithm and a particle swarm optimization, was investigated.The approach was applied to a large-scale real oil field that currently includes approximately 200 wells.Our optimized results were compared with those of the current plan provided by the oil industry.In particular, a higher oil production with the same amount of water injection and a higher net present value were obtained by our optimized approach than by the current plan.Therefore, the new derivative-free computational framework can efficiently solve well placement and production optimization problems for large-scale mature oil fields.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".