Rigorous Integrated Evolutionary Workflow for Optimal Exploitation of Unconventional Gas Assets
Bibliographic record
Abstract
Applications of stochastic evolutionary algorithms in engineering are gaining more attention in practical applications in the oil and gas industry. An important factor to consider when implementing stochastic algorithms is its ability to find the global optimum efficiently. In this study the authors formulate, implement, and test a genetic algorithm with strong elitism to solve a critical problem in the upstream oil industry: how to develop economically an unconventional gas asset. This problem involves finding the optimal number of horizontal wells, the number of transverse hydraulic fracture stages along them, and stage half-length. The described problem is inherently discrete or mixed optimization problem for which the authors develop a conceptually new evolutionary integrated framework that addresses all production design questions. They outline the range of applicability of their workflow and provide ample test cases and results. Their rigorous formulation performs well for a given problem statement and finds the optimal solution that is consistent with the industry accepted optimum.
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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".