Coalbed Methane: Current Field-Based Evaluation Methods
Bibliographic record
Abstract
Summary Coalbed methane (CBM) produced from subsurface coal deposits has been produced commercially for more than 30 years in North America, and relatively recently in Australia, China, and India. Historical challenges to predicting CBM-well performance and long-term production have included accurate estimation of gas in place (including quantification of in-situ sorbed gas storage); estimation of initial fluid saturations (in saturated reservoirs) and mobile water in place; estimation of the degree of undersaturation (undersaturated coals produce mainly water above desorption pressure); estimation of initial absolute permeability (system); selection of appropriate relative permeability curves; estimation of absolute-permeability changes as a function of depletion; prediction of produced-gas composition changes as a function of depletion; accounting for multilayer behavior; and accurate prediction of cavity or hydraulic-fracture properties. These challenges have primarily been a result of the unique reservoir properties of CBM. Much progress has been made in the past decade to evaluate fundamental properties of coal reservoirs, but obtaining accurate estimates of some basic reservoir and geomechanical properties remains challenging. The purpose of the current work is to review the state of the art in field-based techniques for CBM reservoir-property and stimulation-efficiency evaluation. Advances in production and pressure-transient analysis, gas-content determination, and material-balance methods made in the past 2 decades will be summarized. The impact of these new methods on the evaluation of key reservoir properties, such as absolute/relative permeability and gas content/gas in place, as well as completion/stimulation properties will be discussed. Recommendations on key surveillance data to assist with field-based evaluation of CBM, along with insight into practical usage of these data, will be provided.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".