Production Data Analysis of Coalbed-Methane Wells
Bibliographic record
Abstract
Summary Recent advances in production data analysis (PDA) techniques have greatly assisted engineers in extracting meaningful reservoir and stimulation information from well-production and flowing-pressure data. Application of these techniques to coalbed-methane (CBM) reservoirs requires the unique coal storage and transport properties to be accounted for. In recent work, the authors [ex. Clarkson et al. (2007a) and Jordan et al. (2006)] and others [ex. Gerami et al. (2007)] have demonstrated how new techniques such as the flowing material balance (FMB) and production type curves may be adapted to account for CBM storage mechanisms (i.e., adsorption), but, to date, the focus has been on relatively simple CBM reservoir behavior such as single-phase (gas) reservoirs with static effective permeability. The major contribution of the current work is the adaptation of modern PDA techniques (by use of modified material balance time/pseudotime and pseudopressure definitions) to analyze producing wells completed in CBM reservoirs exhibiting several possible flow characteristics: single-phase flow of gas in dry CBM reservoirs, single-phase flow of water (in undersaturated reservoirs), and two-phase (gas and water) flow (in saturated reservoirs). The latter reservoir type commonly exhibits effective permeability changes during depletion (because of relative and/or absolute permeability changes) and changing gas composition caused by relative adsorption effects, both of which have been accounted for in the current work. Specifically, the FMB technique is modified to include several complex CBM reservoir characteristics, and production type curves are applied to some scenarios. Although dry-CBM-well analysis was covered previously [ex. Clarkson et al. (2007a)], we will also discuss FMB development in these reservoirs for completeness. Several synthetic and field examples are given to demonstrate how FMB, type-curve analysis, and analytical simulation can be used in parallel to provide a particularly useful data-analysis toolset and workflow. These techniques were used successfully to extract quantitative reservoir information from single- and two-phase CBM-simulated and field-production pressure data. The PDA techniques developed for two-phase CBM require further evaluation, however.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".