Optimization of Well Placement and Production for Large - scale Mature Oil Fields
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it