Numerical simulation of CO2 storage with enhanced gas recovery in depleted highly heterogeneous carbonate gas reservoir
Bibliographic record
Abstract
Injecting CO2 into depleted gas reservoirs facilitates CO2 storage and enhances natural gas recovery rate (CSEGR), offering significant environmental benefits. Carbonate gas reservoirs are widely distributed globally and are characterized by large reservoir thicknesses, abundant reserves, poor reservoir physical properties, and high heterogeneity. In the depletion state, considerable amounts of natural gas remain in the matrix pores, and numerous natural cavities provide ample space for CO2 storage. Consequently, carbonate gas reservoirs hold great potential for implementing the CSEGR program. To accurately model the flow behavior of CO2 in highly heterogeneous carbonate gas reservoirs and assess the feasibility of implementation the CSEGR program, a three-dimensional numerical model was developed at the reservoir scale using geological data from the fourth section of the Dengying Formation (Deng-4) in the Anyue Gas Field. The study investigates the CH4 production and CO2 storage characteristics of four typical reservoir bodies, tracks the spatial migration of CO2, and explores the effects of CO2 injection rate and interlayer permeability on the CSEGR process. Numerical results show that the cavity-type reservoir is ideal for the CSEGR scheme, with an enhanced gas recovery (EGR) rate exceeding 20%, whereas the fracture-cavity reservoir is unsuitable due to premature CO2 breakthrough caused by natural fractures. The cavity-type reservoir serves as the primary site for CO2 injection and storage, with some CO2 migrating into the pore-type reservoir through interlayer crossflow. For both pore-type and cavity-type reservoirs, the migration distance of the CO2 front is proportional to the square root of the injection time. Lower CO2 injection rates and smaller interlayer permeabilities delay CO2 breakthrough, leading to higher EGR rates. However, reduced injection rates also lower CH4 production rates and extend CO2 injection duration.
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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".