Production Data Analysis of CBM Wells
Bibliographic record
Abstract
Abstract 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 that the unique coal storage and transport properties be accounted for. In recent work, the authors and others 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. Although adaptations of PDA to include more complex CBM reservoir characteristics were introduced, the focus of the current work is advancement of modern PDA techniques to incorporate reservoir behavior such as single-phase flow of water in undersaturated reservoirs, two-phase (gas+water) flow in saturated reservoirs, effective permeability changes during depletion, and changing gas composition due to relative adsorption. Specifically, the FMB technique is modified in this work to include several complex CBM reservoir characteristics. FMB can be a powerful diagnostic technique when relative and absolute permeability changes are apparent during depletion. 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 tool. The adapted PDA techniques used in this work make use of the pseudotime and pseudopressure concepts, modified to account for CBM reservoir behavior, to linearize the (constant-rate) diffusivity equation for CBM. Material balance pseudotime was used to account for variable operating conditions. These techniques were used successfully to extract quantitative reservoir information from single- and two-phase CBM simulated and field production and pressure data. The techniques for two-phase CBM require further evaluation, however. Several key assumptions were used in deriving the PDA techniques including (but not limited to) instantaneous desorption (small sorption times) and single-layer behavior. Although the former is not considered a serious limitation, as most commercial reservoirs analyzed to date by the authors have exhibited single-porosity behavior during production, the latter may be quite important for some producing fields. PDA of multi-layered CBM reservoirs will be discussed at length in a future paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".