Investigations of methane adsorption characteristics on marine-continental transitional shales and gas storage capacity models considering pore evolution
Bibliographic record
Abstract
Methane adsorption is a critical assessment of the gas storage capacity (GSC) of shales with geological conditions. Although the related research of marine shales has been well-illustrated, the methane adsorption of marine-continental transitional (MCT) shales is still ambiguous. In this study, a method of combining experimental data with analytical models was used to investigate the methane adsorption characteristics and GSC of MCT shales collected from the Qinshui Basin, China. The Ono-Kondo model was used to fit the adsorption data to obtain the adsorption parameters. Subsequently, the geological model of GSC based on pore evolution was constructed using a representative shale sample with a total organic carbon (TOC) content of 1.71%, and the effects of reservoir pressure coefficient and water saturation on GSC were explored. In experimental results, compared to the composition of the MCT shale, the pore structure dominates the methane adsorption, and meanwhile, the maturity mainly governs the pore structure. Besides, maturity in the middle-eastern region of the Qinshui Basin shows a strong positive correlation with burial depth. The two parameters, micropore pore volume and non-micropore surface area, induce a good fit for the adsorption capacity data of the shale. In simulation results, the depth, pressure coefficient, and water saturation of the shale all affect the GSC. It demonstrates a promising shale gas potential of the MCT shale in a deeper block, especially with low water saturation. Specifically, the economic feasibility of shale gas could be a major consideration for the shale with a depth of <800 m and/or water saturation >60% in the Yushe-Wuxiang area. This study provides a valuable reference for the reservoir evaluation and favorable block search of MCT shale gas.
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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) | 0.000 | 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 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".