Production-Data and Pressure-Transient Analysis of Horseshoe Canyon Coalbed-Methane Wells, Part II: Accounting for Dynamic Skin
Bibliographic record
Abstract
Summary In a previous study (Clarkson 2009), advanced production-analysis techniques, including production type curves and flowing material balance (FMB), were applied to Horseshoe Canyon (HSC) coal wells to establish the applicability of these techniques and to resolve the importance of multilayer behaviour for production analysis. The field examples chosen for analysis included single wells that exhibited production declines analogous to shallow gas wells, with decline in production occurring from day 1, and in one case exhibiting transient-flow characteristics. There are many more HSC wells that exhibit less-straightforward production characteristics, including flat or even inclining production. These wells have production profiles qualitatively similar to two-phase coalbed-methane (CBM) wells, yet lack water production. There are several possible explanations for the flat or inclining production behaviour, including changing skin associated with near-wellbore cleanup of drilling fluids over time, and increase in absolute permeability associated with matrix shrinkage. Regardless of the cause, these effects need to be accounted for in quantitative production analysis. In this follow-up paper, we continue to perform comparisons between multilayer and single-layer-equivalent production analysis of HSC wells, but focus on wells that exhibit inclining gas production. We develop a methodology to correct for changing skin in both type-curve and FMB analyses that allows this more-complex well behaviour to be analyzed. We validate the new methodology using a simulated example, and then apply it to actual field cases. Changing skin and absolute permeability by layer/coal zone was quantified through periodic shut-in/buildup testing of isolated coals throughout the life of the wells, and these dynamic properties were used in production-data analysis and analytical simulation. Comparing single-layer-equivalent and multilayer analysis, we have found differences in estimated-ultimate-recovery (EUR) values of less than 15% because the existence of one or two dominant (high-kh) coal zones. Additionally, we investigated the impact of free-gas storage on the results of productiondata analysis of HSC wells. The production-analysis methodology introduced in this work, while useful for analyzing HSC coal wells with dynamic skin/permeability, is expected to be applicable to a broader range of reservoir types that exhibit this complex behaviour.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".