Mechanisms of Acid- and Chelating Agent-Induced Coal Permeability Response Considering the Stress Sensitivity Effect
Bibliographic record
Abstract
Chemical stimulation is a promising method for enhancing coalbed methane (CBM) recovery. Since conventional acids may cause formation damage and severe corrosion, this paper investigated the possibility of using GLDA (l-glutamic acid N, N-diacetic acid)─a chelating agent─and fluoroboric acid (HBF4) as alternatives to HCl. In addition, the stress sensitivity effect of coal during chemical stimulation was considered. A comparative evaluation from the perspectives of mineral dissolution/precipitation, pore structure changes, micromorphology change, and permeability variations with effective stress was done using Qinshui Basin coal samples. The results show that GLDA is preferable for CBM reservoir stimulation. Coal permeability increased 9.73 times and 17.09 times after injecting 2.5 wt % HCl and 5 wt % GLDA, respectively. However, 27% reduction in permeability was reported for the 4 wt % HBF4-acidized coal sample due to fluoride precipitation. With the effective stress increased from 1.5 to 5.5 MPa, the permeability reduction rates of raw coal and HCl-, GLDA-, and HBF4-treated coal samples were 76.92, 39.55, 38.18, and 91.61%, and the stress sensitivity coefficients were 0.374, 0.126, 0.123, and 0.613, respectively. Coal permeability enhancement after GLDA stimulation benefits from increased proportions of seepage pores and macropores and a more unified pore structure. Although HBF4 has a stronger dissolving ability to silicate minerals, it will loosen the coal skeleton, weaken the compressive strength, and then lead to cleat closure. GLDA can decrease fracture compressibility (Cf) because of its limitation of dissolving ability, and robust minerals such as quartz and feldspar can be preserved for supporting coal cleats. 5 wt % GLDA at pH = 2 exhibits 0.69 g/m2·h corrosion rate, which is 9.06% of that of HCl. This study shows that GLDA effectively enhances coal permeability and decreases coal stress sensitivity, which provides references for field applications.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".