NMR-monitored CH4 adsorption/desorption dynamics in shale: Implications for CO2-ESGR and in-situ sequestration
Why this work is in the frame
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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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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