Relative Permeability Curve Shapes in Coalbed Methane Reservoirs
Bibliographic record
Abstract
Abstract Relative permeability curves in coalbed methane reservoirs (CBM), acquired by analysis of production data, can differ from laboratory-measured curves due to complications such as stress-desorption dependent permeability and cross-formational flow. This paper aims to derive relative permeability curves for coalbed methane reservoirs using production data analysis, as well as discuss curve characteristics and shapes. Field examples from the San Juan Basin in the US and the Qinshui and Ordos Basins in China are presented to provide a worldwide view of relative permeability curve shapes. These field examples are analysed using a tank type model, a common production data analysis tool, and the influential factors on curve shapes are discussed. The results and analysis indicate that permeability enhancement during the life of the well, and cross-formational flow between the coal seam and adjacent formations, can strongly control curve shapes. These effects, when not detected, can result in irregular relative permeability curve shapes obtained by analysis of production data. Direct measurement of permeability enhancement requires time-lapse production tests while investigation of cross-formational flow of water into coal seams requires hydraulic connectivity assessment, which are time consuming and expensive to conduct. The signatures of relative permeability curves presented in this study allow indirect determination of permeability enhancement and cross-formational flow in coal seam gas reservoirs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".