Effects of Moisture Contents on Shale Gas Recovery and CO<sub>2</sub> Sequestration
Bibliographic record
Abstract
Abstract Enhanced recovery of shale gas with CO2 injection has attracted extensive attention as it combines the advantages of improved efficiency of shale gas recovery and reduced greenhouse gas emissions via CO2 geological sequestration. On the other hand, the microscopic mechanism of enhanced shale gas recovery with CO2 injection and the influence of the subsurface water confined in the shale nanopores remain ambiguous. Here, we use grand canonical Monte Carlo (GCMC) simulations to investigate the effect of moisture on the shale gas recovery and CO2 sequestration by calculating the adsorption of CH4 and CO2 in dry and moist kerogen slit pores. Simulation results indicate that water accumulates in the form of clusters in the middle of the kerogen slit pore. Formation of water clusters in kerogen slit pores reduces pore filling by methane molecules, resulting in a decrease in the methane sorption capacity. For the sorption of CH4/CO2 binary mixtures in kerogen slit pores, the CH4 sorption capacity decreases as the moisture content increases, whereas the effect of moisture on CO2 sorption capacity is related to its mole fraction in the CH4/CO2 binary mixture. Furthermore, we propose a reference route for shale gas recovery and find that the pressure drawdown and CO2 injection exhibit different mechanisms for gas recovery. Pressure drawdown mainly extracts the CH4 molecules distributed in the middle of kerogen slit pores, while CO2 injection recovers CH4 molecules from the adsorption layer. When the water content increases, the recovery ratio of the pressure drawdown declines, while that of CO2 injection increases, especially in the first stage of CO2 injection. The CO2 sequestration efficiency is higher under higher water content. These findings provide the theoretical foundation for optimization of the shale gas recovery process, as well as effective CO2 sequestration in depleted 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) | 0.000 | 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) | 0.001 | 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".