Simulating the Effect of the Coal Density on MethaneRecovery for CBM Study
Bibliographic record
Abstract
Unconventional coal bed methane (CBM) reservoirs have a huge future potential to production but difficult to develop due to their complexity. Analyzing production performance and estimating original gas in place somewhat complicated and has led to numerous methods of approximating production performance. Hydrocarbon reservoirs are known to react to changes in their properties, particularly coal density. Therefore, it is important to study the effect of coal density changes towards production and gas in place of CBM. The production rates of four CBM fields which are Qinshui Basin, San Juan Basin, and Western Canada Basin will be simulated and analyzed, with coal density being the manipulated variable. Simulation will be performed using the ECLIPSE E300 model, with several assumptions made. From the result, it is clear to see that coal density enhances the production of methane gas from CBM fields. An increase in coal density leads to higher production rates and a prolonged maximum production time as well as gas in place. High reservoir pressure, Langmuir isotherms, gas content and coal density are favorable for CBM production.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".