Characterization of CO<sub>2</sub>/CH<sub>4</sub> Competitive Adsorption in Various Clay Minerals in Relation to Shale Gas Recovery from Molecular Simulation
Bibliographic record
Abstract
CO 2 sequestration and enhanced gas recovery (CS-EGR) is a viable option with enormous potentials to produce shale gas. However, the microscopic competitive sorption behaviors of CH 4 and CO 2 in various clay minerals that are an important constituent of shale at actual formation conditions are still less clear. In this work, we study CO 2 /CH 4 binary mixture competitive sorption in various clay minerals (montmorillonite, illite, and kaolinite) by using grand canonical Monte Carlo simulations. The effects of the clay mineral types and possible stratigraphic conditions, including temperature, pressure, CO 2 /CH 4 molar fraction, and selectivity, are discussed in detail. The results demonstrate that the CO 2 sorption capacity in the clay mineral follows an order of montmorillonite > illite > kaolinite. CO 2 molecules are prone to be adsorbed on the surfaces of montmorillonite and illite nanopores with cation exchange than on the surface of the kaolinite nanopore without cation exchange. Moreover, cation exchange could distinctly increase the CO 2 /CH 4 adsorption ratio so that the first layer of CH 4 molecules can be displaced by CO 2 molecules. The replacement ratio of CH 4 is related to the type of adsorbent, which is independent of the original formation pressure. In addition, a case study is designed to quantify the enhanced gas recovery (EGR) and CO 2 –CH 4 displacement efficiency. With a higher reservoir initial pressure when injecting CO 2, the EGR of adsorbed CH 4 gas could increase up to 28.97%. Our findings provide insights into gas mixture sorption in shale reservoirs and provide important guidelines for CS-EGR projects.
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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 | 0.000 |
| 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".