Measurement of Relative Permeability of Coal: Approaches and Limitations
Bibliographic record
Abstract
ABSTRACT A number of laboratory studies on coalbed methane (CBM) have provided data for the relative permeability of coal to gas and water, which are needed to analyze CBM reservoirs, particularly in numerical simulation. The relative permeability curves of coal to gas and water are determined using the two methods used in the petroleum reservoir engineering, namely, the steady or unsteady state displacement methods. In most cases, the unsteady state displacement method is used because this method is relatively fast to carry out. In this method, the non-wetting fluid is displaced by the wetting fluid, and the effluent production and pressure history are used to back out the relative permeability of coal to gas and water. One of the problems encountered in the displacement methods is the question of coal wettability. Many researchers studying this area have considered that coal is water-wet. However, it is a well-known fact that a large amount as much as 95% of methane is adsorbed on the internal surface of coal matrix. Therefore, coal could be regarded at least as initially gas-wet. The wettability of coal will depend on the flow characteristics of coal, that is, whether methane flows in the matrix. This paper investigates a number of wettability of coal scenarios and discusses the approaches used by various investigators, for instance, pre-heating the coal sample before any testing, and using a non-adsorbable gas, pointing out their limitations from the viewpoint of CBM reservoir simulation. The likely dependence of relative permeability of coal on flow pressure is also addressed.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".