Application of Flowing p/Z* Material Balance for Dry Coalbed-Methane Reservoirs
Bibliographic record
Abstract
Abstract Material balance analysis is a fundamental technique for estimating gas-in-place. It can be achieved using: 1) Static material balance, using static (shut-in) reservoir pressures, where a plot of static p/z versus cumulative gas production is created to estimate original-gas-in-place (OGIP) or 2) Flowing material balance where gas rates and flowing pressures are used to estimate average reservoir pressure. The flowing material balance concept of Agarwal-Gardner (1999) was extended to dry coalbed methane (CBM) reservoirs by Clarkson et al. (2007a) and Gerami et al. (2007) and to 2-phase (gas and water) CBM wells by Clarkson et al. (2007b). The present study further enhances the flowing material balance for dry CBM reservoirs by presenting a p/z* implementation of the concept. This application, while accounting for the distinguishing characteristics of a CBM reservoir, uses the industry-standard practice of p/z material balance to calculate original-gas-in-place. As with the Agarwal-Gardner approach, the flowing p/z* method can be applied to variable gas rates and/or flowing pressures conditions. In the present work, the derivation and iterative procedure of calculations are explained. Several test cases based on dry/immobile water saturation using real and synthetic data were generated. The resulting estimates of OGIP calculated from implementation of flowing p/z* material balance show excellent agreement and the estimated OGIP's are reliable.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".