NMR-monitored CH4 adsorption/desorption dynamics in shale: Implications for CO2-ESGR and in-situ sequestration
Bibliographic record
Abstract
Addressing the inherent challenges of low recovery rates and difficulties in shale gas extraction, this study investigates the application potential of CO 2 -enhanced shale gas recovery (CO 2 -ESGR) coupled with carbon sequestration (CS). Utilizing low-field nuclear magnetic resonance (NMR) technology, we conducted real-time monitoring of methane adsorption and desorption processes within collected shale samples. Through the analysis of T 2 spectra and corresponding peak areas, we achieved quantitative differentiation among adsorbed CH 4 , free CH 4 within pore spaces, and free CH 4 within fractures. The results demonstrate that within a pressure range of 0.01–10 MPa, the total methane volume increased progressively from 79.4 to 177.83 cm 3 /g. Following CO 2 injection, a significant weakening of the short- T 2 signal (representing adsorbed CH 4 ) was observed, accompanied by a concomitant enhancement of the long- T 2 signal (representing free-phase CH 4 ). Furthermore, depressurization desorption experiments revealed that CO 2 injection increased the methane desorption rate by approximately 10%, while simultaneously facilitating the long-term, stable sequestration of CO 2 within the shale matrix. These findings not only validate the mechanism of competitive adsorption, whereby CO 2 enhances shale gas recovery, but also highlight the significant carbon sequestration potential of shale reservoirs. Consequently, this research provides a crucial theoretical basis and technical support for advancing both shale gas development and carbon emission reduction strategies.
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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".