Well Placement Optimization Based on Pressure Gradient Distribution; Applicable to CO2 Sequestration
Bibliographic record
Abstract
Summary In well location optimization, the appropriate selection of initial guess for the optimization algorithms can reduce the required number of simulation runs. In this research, the idea of well location optimization based on pressure gradient distribution in the reservoir for CO2 injection is presented. Targets are those with low absolute pressure gradients leading to the areas minimally influenced by the existing injection wells. The pressure profile of a steady-state case is applied to define the objective function based on the pressure gradient and superposition principle. The numerical active set method is implemented for the optimization algorithm as it can include the effect of multiple wells and linear boundaries. In a simple reservoir of fixed properties, this corresponds to the optimum well location for injection or production, whereas in a reservoir with variable properties, the result is an initial guess for the optimization process. The optimization algorithm is addressed for two scenarios including two and four CO2 injection wells.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".