Mannville CBM: Integrated Approach in the Mikwan Area
Bibliographic record
Abstract
Abstract Historical analog data indicates that production from Mikwan area Mannville CBM wells can differ substantially from the typical production profile, resulting in significant uncertainty with respect to expected well performance. Encana, through the applied integration of the key disciplines of Geoscience and Engineering, was able to reduce the economic and technical challenges inherent in developing this resource, and optimize the gas production potential from the 100/13-25-36-21W4 Mannville CBM well. To maximize the drilling success and production potential of the well, specific strategies are employed to optimize the placement of these wells to ensure reservoir access is in the driest portion of the fairway. First, hydro-geological data was considered on a regional level to define the Basin Centered Gas System envelope that defines the Mannville CBM Dry Fairway. Petrophysical assessment was utilized to address the risk associated with water influx from adjacent lithologies and to characterize the coal quality by integrating core analysis with petrographic study data. Through integration of geological and geophysical data the well is placed in an optimal structural setting that promotes nature fracture development leading to enhanced permeability. The well was subject to several differing completion programs leading to a refinement of completion techniques. In addition, production and production logging methods were used to qualify the completion results. This paper will demonstrate the successful characterization of the Mannville CBM reservoir in the Mikwan area of Central Alberta. The paper will highlight the integration of these methods to maximize the production potential through a case study of the 100/13-25-36-21W4 well. The lessons to date indicate Mannville CBM well potential can be predicted with some confidence in this Mikwan dry gas fairway area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".