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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CO2 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 CH4 and CO2 in various clay minerals that are an important constituent of shale at actual formation conditions are still less clear. In this work, we study CO2/CH4 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, CO2/CH4 molar fraction, and selectivity, are discussed in detail. The results demonstrate that the CO2 sorption capacity in the clay mineral follows an order of montmorillonite > illite > kaolinite. CO2 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 CO2/CH4 adsorption ratio so that the first layer of CH4 molecules can be displaced by CO2 molecules. The replacement ratio of CH4 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 CO2–CH4 displacement efficiency. With a higher reservoir initial pressure when injecting CO2, the EGR of adsorbed CH4 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it