Use of the Rate-Transient Analysis Method to Evaluate Permeability to Multiple Gases in a Fractured Coal Sample
Bibliographic record
Abstract
Deep coal seams can be suitable candidates for CO 2 storage due to their high adsorption capacity. However, CO 2 injection into coal is known to be affected by CO 2 adsorption-induced swelling, offset to a certain degree by the reduction in effective stress near the injection well during injection. To evaluate the net result of these competing mechanisms, as well as the impact of coal properties and fabric on CO 2 transport and storage, a systematic laboratory study was conducted on a coal sample from the Mannville Formation of Alberta, Canada. For the first time, the rate-transient analysis (RTA) permeability and porosity (‘RTAPK’) method, a new core analysis approach developed for low-permeability reservoir samples, was applied to examine the evolution of pressure- and stress-dependent permeability of a coal sample containing fractures. Using a coal core plug obtained from the Cretaceous Mannville Formation in western Canada, helium (He), argon (Ar), methane (CH 4 ) and carbon dioxide (CO 2 ) permeabilities were measured using the RTAPK method. The permeability measured with CO 2 was consistently lower than CH 4 due to its stronger adsorption onto coal and greater coal swelling in the presence of CO 2 . The baseline permeability, measured with He and Ar (which do not adsorb to a significant degree on the coal), was reduced substantially (>50%) after CH 4 injection, indicating significant matrix swelling and associated fracture aperture reduction after CH 4 injection. However, He permeability (i.e., baseline permeability) after CO 2 injection was comparable to that after CH 4 injection. This is likely due to (1) the coal sample reaching its maximum deformation (swelling and fracture closure) during CH 4 injection and/or (2) the interaction with CO 2 causing the coal sample to become structurally weaker, generating more fractures during the gas injection/soaking period. The findings of this study will be of interest to operators working on carbon capture and sequestration in deep coal seams, offering valuable insights for field-scale pilot testing of the technique.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".