New Technique to Accelerate Gas Production on CBM Through Use of Sacrifice Well
Bibliographic record
Abstract
Coal Bed Methane (CBM) is an unconventional resource that shows great promise, particularly in Indonesia, whose CBM reservoir is relatively thick. Gas reserves of CBM are estimated at 450 TSCF in Indonesia, which makes it, the 6th largest CBM-containing country in the world. However, regulatory and technical limitations slow the progression of the exploitation of this resource. One of the fundamental technical problems is related to the length of gas production due to the duration of the dewatering time. Many studies have been developed related to the production of CBM, and this paper discusses several characteristics, patterns and techniques regarding the production of methane gas from coal through the use of a sacrifice well. Several scenarios are analysed with the use of a simulator. The results show that the production of a thick CBM reservoir, with some sensitivity patterns of production through the use of a sacrifice well, have an influence on the production of gas and water for the exploitation of CBM resources. In other words, the use of new techniques that are discussed in this paper have an impact on reducing the dewatering time and are effectively implemented in Indonesia, which has thick CBM formations.
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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 | 0.000 |
| 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 | 0.000 | 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".