Numerical Investigation of Optimal Well Spacing Configuration for Enhanced CH <sub>4</sub> Recovery and CO <sub>2</sub> Sequestration: Geological Implications Analysis
Bibliographic record
Abstract
Within the framework of the CO 2 -ECBM projects, the optimal arrangement of well spacing serves as a pivotal factor in maximizing CH 4 production and optimizing the CO 2 sequestration performance. This study centers on the Panyi mining area in Huainan city as its primary research focus. Initially, a comprehensive analysis was carried out employing fully coupled THMC mathematical models to simulate the CO 2 -ECBM process. Subsequently, the investigation systematically assessed the impact of well spacing on key performance indicators. To rigorously evaluate the engineering outcomes of the CO 2 -ECBM process, objective functions were meticulously formulated to quantify performance. Following this, an in-depth analysis was conducted to identify the optimal well spacing configurations corresponding to four distinct engineering objectives under varying injection pressures. Based on the findings, a dynamic design framework for well spacing and injection pressure was proposed, adhering to the principles of multiobjective optimization. For well spacings less than 210 m, both CH 4 production and the CO 2 injection volume demonstrate a positive correlation with increasing well spacing. Conversely, when well spacing exceeds 280 m, both volumes exhibit a decline as well spacing expands. Within the intermediate range of 210–280 m, variations in CH 4 production and CO 2 injection volumes with respect to well spacing are relatively minimal. As the CO 2 injection pressure intensifies, the optimal well spacing required to achieve the four engineering objectives increases proportionally. Under identical injection pressures, the optimal well spacings, when ranked from smallest to largest, align with the following objectives: time-efficiency optimization, comprehensive consideration, production prioritization, and storage prioritization. It is worth noting that time-efficiency optimization should not serve as the primary engineering objective in the layout of CO 2 -ECBM technology due to potential trade-offs with other critical performance metrics. For projects with a strong emphasis on CH 4 production, a strategy that combines a low injection pressure with moderate well spacing is recommended to strike a balance between production efficiency and operational feasibility. Conversely, for projects prioritizing CO 2 sequestration, a high injection pressure paired with large well spacing is advisible to enhance sequestration capacity while maintaining system stability. The research findings provide robust theoretical support for the precise regulation of CO 2 -ECBM engineering parameters under complex geological conditions, thereby advancing the field’s capacity to optimize both energy recovery and environmental mitigation outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| 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".