Multiscale Pore Structure Evolution of Different Rank Coals Induced by Chelating Agent Intrusion
Bibliographic record
Abstract
Summary China’s coalbed methane (CBM) reservoirs are characterized by low permeability (<1 md). Stimulation with conventional acids is facing the problems of secondary precipitation, high corrosion rates, and fines migration. Chelating agent intrusion was proposed as a promising alternative for conventional acids, while the pore structure evolution induced by it needs to be further clarified. In this study, coal samples with three different ranks were selected and treated with L-glutamic acid N, N-diacetic acid (GLDA). Low-temperature Ar and N2 adsorption tests, mercury intrusion porosimetry (MIP), and scanning electron microscope (SEM) analyses were applied to investigate nanoscale to macroscale pore structure changes. X-ray fluorescence (XRF) spectroscopy tests were conducted to determine the mineralogical change of coal. The results show that chelating agent intrusion can widen fracture width, connect micropores, and create void space in macropores by dissolving carbonate minerals, while the nanoscale pore volumes (PVs) showed a slight decrease due to clay minerals collapse. The fractal dimensions Dm calculated by the MIP results of lignite, bituminous coal, and anthracite coal decreased by 0.2735, 0.1734, and 0.1444, respectively. It is indicated that a pore structure with a diameter of >100 nm of the coal became more unified, which favors the seepage of gas/water. The chelating agent intrusion shows a significant effect on lignite, followed by bituminous and anthracite coal. However, the metal element reduction rate of anthracite coal presents the highest, followed by bituminous coal and lignite. There can be a risk that a long intrusion time would loosen the skeleton of lignite, leading to further reservoir damage. Therefore, bituminous and anthracite coal samples are preferred, as the skeletons of higher-rank coals are more compact. These research findings introduced a potential stimulation method for enhancing CBM recovery and provided references for field application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".