Integrated Geological-Engineering-Economic or for Optimization Decision-Making in Unconventional Oil and Gas Horizontal Wells
Bibliographic record
Abstract
Abstract Unconventional oil and gas resources hold immense potential and represent the primary option for alleviating the imbalance between oil and gas supply and demand. However, the high costs associated with engineering operations pose challenges for profitable development. Practical experiences have demonstrated that an integrated approach combining geological and engineering strategies is an effective means to reduce costs and enhance efficiency. Given the multitude of geological conditions and engineering parameters that influence development profit, which surpass the scope that can be comprehensively grasped by expert experience alone, the current integrated optimization decisions primarily relying on expert experience may not necessarily constitute globally optimal solutions. In contrast, the widespread and successful applications of operations research in various sectors of the national economy, including military and engineering fields, have shown its significant potential for quantitatively solving optimal decision-making problems in complex systems. Therefore, this study leverages operations research theory to first construct production models and cost models for unconventional oil and gas resources as functions of all geological conditions and engineering construction parameters. Subsequently, a profit (= production - cost) objective function is established, thereby transforming the practical problem of optimal integrated geological-engineering decision-making into a mathematical problem of maximizing the profit objective function. Following this, computer software is developed to determine the maximum value of the objective function and the corresponding values or matches of geological conditions and engineering parameters, enabling quantitative, scientific, and optimal decision-making in integrated geological-engineering projects. If successful, this approach will facilitate the profitable development of numerous unconventional oil and gas resources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".