Predicting Adsorption of Methane and Carbon Dioxide Mixture in Shale Using Simplified Local-Density Model: Implications for Enhanced Gas Recovery and Carbon Dioxide Sequestration
Bibliographic record
Abstract
Carbon dioxide (CO2) capture and storage have attracted global focus because CO2 emissions are responsible for global warming. Recently, injecting CO2 into shale gas reservoirs is regarded as a promising technique to enhance shale gas (i.e., methane (CH4)) production while permanently storing CO2 underground. This study aims to develop a calculation workflow, which is built on the simplified local-density (SLD) model, to predict excess and absolute adsorption isotherms of gas mixture based on single-component adsorption data. Such a calculation workflow was validated by comparing the measured adsorption of CH4, CO2, and binary CH4/CO2 mixture in shale reported previously in the literature with the predicted results using the calculation workflow. The crucial steps of the calculation workflow are applying the multicomponent SLD model to conduct regression analysis on the measured adsorption isotherm of each component in the gas mixture simultaneously and using the determined key regression parameters to predict the adsorption isotherms of gas mixtures with various feed-gas mole fractions. Through the calculation workflow, the density profiles and mole fractions of the adsorbed gases can be determined, from which the absolute adsorption of the gas mixture is estimated. In addition, the CO2/CH4 adsorption selectivity larger than one is observed, illustrating the preferential adsorption of CO2 over CH4 on shale, which implies that CO2 has enormous potential to enhance CH4 production while sequestering itself in shale. Our findings demonstrate that the proposed calculation workflow depending on the multicomponent SLD model enables us to accurately predict the adsorption of gas mixtures in nanopores based on single-component adsorption results. Following the innovative calculation flow path, we could bypass the experimental difficulties of measuring the multicomponent mole fractions in the gas phase at the equilibrium during the adsorption experiments. This study also provides insight into the CO2/CH4 competitive adsorption behavior in nanopores and gives guidance to CO2-enhanced gas recovery (CO2-EGR) and CO2 sequestration in shale formations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".