Automatic Determination of Well Placement Subject to Geostatistical and Economic Constraints
Bibliographic record
Abstract
Abstract Optimal well placement is a complex problem that requires detailed models of the reservoir structure/geometry and the petrophysical properties such as facies, porosity, permeability, and fluid saturation. The reservoir development team attempts to integrate all of these aspects when devising a well plan for optimal reservoir exploitation. Ideally the well locations would be selected with the assistance of a flow simulator; however, this is impractical due to time and CPU requirements. This paper presents a technique for selecting optimal well locations for fine-tuning with a flow simulator. The technique constructs the well placement problem as an optimization problem to be solved with simulated annealing. The global objective function consists of multiple component objective functions. Each component represents a desirable feature or constraint in the problem. Optimality is defined as the best balance among the component objectives. The format of the technique is flexible and can incorporate 3-D geostatistical models of uncertainty and multiple constraints. The proposed method iteratively refines initial well locations and trajectories until the global objective is maximized. Several examples are shown. Optimal well placement in a steam assisted gravity drainage context is illustrated.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".