Numerical Simulation of CO <sub>2</sub> -Enhanced Gas Recovery and Sequestration in Heterogeneous Shale Gas Reservoirs
Bibliographic record
Abstract
As a key component of global carbon neutrality strategies, injecting CO 2 into shale gas reservoirs to enhance natural gas recovery (CO 2 -EGR) while simultaneously achieving geologic sequestration is a promising approach that offers the dual benefits of increased production and reduced emissions. However, the pronounced heterogeneity of shale reservoirs exacerbates uncertainties in both the displacement process and storage efficacy. To understand the coupled effects of geological uncertainty and engineering controls, we developed a three-dimensional, dual-continuum, heterogeneous model based on the Changning shale gas field (Sichuan Basin, China) using TOUGH3. A multiparameter sensitivity analysis was subsequently conducted to assess how matrix and fracture heterogeneity, operational parameters, and initial reservoir pressure influence methane recovery and CO 2 storage volumes. The results indicate that reservoir heterogeneity, particularly within the matrix, is the principal factor limiting macroscopic sweep efficiency and ultimate recovery, causing a 10% reduction in cumulative methane production in strongly heterogeneous scenarios compared to homogeneous baselines. Aggressive injection and production strategies amplify instabilities driven by fluid property contrasts, such as gravity override. These strategies promote preferential channeling along the reservoir top, thereby reducing displacement efficiency. In contrast, moderate and steady operational parameters stabilize the advancing front and enhance sweep efficiency with an optimized production pressure, yielding an 11.3% increase in final recovery. For medium- to high-pressure reservoirs developed under an inject-to-deplete strategy, a higher initial pressure correlates with extended operational periods and greater cumulative CO 2 storage. This study highlights the principle of “geology-constrained potential with engineering-driven optimization” and provides practical guidance for designing and implementing CO 2 -EGR in heterogeneous shale gas reservoirs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".