Practical Technique to Identify Infill Potential in Low-Permeability Gas Reservoirs Applied to the Milk River Formation in Canada
Bibliographic record
Abstract
Abstract This paper describes the application of a practical technique to determine infill potential when faced with little time, large data sets, and complex geology. Using this technique, we determined where newer wells are encountering potentially depleted reservoir and the infill potential for the Milk River formation within a 900-well, 200,000-acre area in the Western Canada Sedimentary Basin. We obtained these results in a minimal amount of time and used only monthly production and wellbore location data. We validated our technique by "history matching" the production performance of recently drilled wells. We correlated well quality with historical well densities in order to predict the infill well potential from 160-acre spacing to an 80-acre well spacing. We estimated ultimate recoveries for all existing wells and infill candidates and show their reserve distributions. We identified 896 infill candidates with 8.9 × 109 m3 of gas reserves. The results of this study are presented in this paper using tables, graphs, and maps. The results of a study applying this analysis technique can be used when budgeting and planning near-and long-term drilling programs. The analysis techniques described in this paper could be applied by operators in other areas and reservoirs to evaluate their own acreage position or infill drilling potential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".