Pore Structure Characteristics and Adsorption and Desorption Capacity of Coal Rock after Exposure to Clean Fracturing Fluid
Bibliographic record
Abstract
adsorption method and scanning electron microscopy (SEM) were used to characterize coal samples. Using gas adsorption/desorption tests, high-, medium-, and low-rank coal samples before and after the clean fracturing fluid treatment were systematically studied. According to the relationship between coal pore structure parameters and gas adsorption/desorption characteristics, a correlation between the microscopic pore structure and the macroscopic gas adsorption/desorption characteristics of coal was obtained. The results show that the number of closed pores in high-, medium-, and low-rank coal samples increased after the clean fracturing fluid treatment. The micropore volume increased by 0.0009, 0.00143, and 0.0035 mL/g, respectively, and the specific surface area increased by 4.87, 9.06, and 57.60%. The fractal dimension also increased compared with that of raw coal. SEM analysis indicated that the influence degree of clean fracturing fluid treatment on the pore structure of different-rank coal samples was Gengcun low-rank coal > Pingba middle-rank coal > Jiulishan high-rank coal. The experimental results of methane adsorption and desorption showed that the adsorption capacity of the coal samples after clean fracturing fluid treatment was enhanced, which is related to increases in the micropore proportion, micropore volume, and specific surface area of the coal. The desorption capacity of the coal samples was also enhanced. The desorption rate of medium- and high-rank coal samples increased after the clean fracturing fluid treatment but that of low-rank coal samples decreased. The main reason is the increase in the number of micropores in low-rank coal, which enhances the gas adsorption ability and makes gas desorption difficult. Therefore, clean fracturing fluid is suitable for medium- and high-grade metamorphic coalbed methane mines. These research results provide a theoretical basis for the application of clean fracturing fluid in different coalbed methane wells.
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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 | 0.001 | 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".