Bibliographic record
Abstract
Abstract There are three distinguishing phases in the production life of a wet CBM well: a period of inclining gas rate due to dominance of relative permeability effects, a period of rate stabilization during which the gas rate peaks followed by the depletion phase during which gas production rate declines. The declining gas production rate can be attributed to many factors such as: ■Declining reservoir pressure■Loss of productivity due to compaction and permeability loss■Loss of productivity due to fine migration that creates a choke skin near wellbore While the decline in reservoir pressure is inevitable, the loss of productivity due to the choke skin and permeability loss for a declining CBM well can be remedied or slowed down through a multi-stage hydraulic refracturing. The impact of such fracturing on improving well productivity and boosting the gas production rate is investigated. Reworking the well through refracturing could help clean up the zone near wellbore, re-activate or improve the coal network of natural fractures; lead to more effective dewatering and even create new contacts between the reservoir and formation. The area near wellbore will benefit from re-invigoration of fracture network and creation of an area of enhanced permeability. In this study the benefit of completing a new vertical and/or horizontal CBM well with a multi-stage hydraulic fracture is examined. The production of wells with such completions are compared to typical single-stage fracturing and benefits of creating an inner zone of enhanced permeability in short-term and long term are investigated. A numerical dual-porosity reservoir model is used to generate the CBM gas production forecasts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".