Machine Learning Sweet Spot Identification and Performance Validation Utilising Reservoir and Completion Data from Unconventional Reservoir in British Columbia, Canada
Bibliographic record
Abstract
Abstract Production in an unconventional reservoir varies widely depending on reservoir characteristics (e.g., thickness, permeability, brittleness, natural fracturing), and completion design (e.g., well spacing, frac spacing, proppant volume). A comprehensive method of data analytics and predictive Machine Learning (ML) modeling was developed and deployed in the Montney unconventional siltstone gas reservoir, British Columbia, Canada to identify production zone "sweet spots" from reservoir quality data (i.e., geological, geophysical, and geomechanical) data and completion quality data (e.g., frac spacing, fluid volume, and proppant intensity), which were utilized to enhance and optimize production performance of this unconventional reservoir. Typical data analytics and predictive ML modeling utilizes all the reservoir quality data and completion quality data together. The completion quality data tends to dominate over the reservoir quality data, because of a higher statistical correlation (i.e., weight) of the completion data to observed production. Hence, resulting predictive ML models commonly underestimate the effects of the reservoir quality on production, and exaggerate the influence of the completion quality data. To overcome these shortcomings, the reservoir quality data and the completion quality data are separated and normalized independently. The normalized reservoir and completion quality data are utilized to identify sweet spots and optimize completion design respectively, through predictive ML modelling. This novel methodology of predictive ML modeling has identified sweet spots from key controlling reservoir quality data and as well as prescribed optimal completion designs from key controlling completion quality data. The trained predictive ML model was tested by a blind test (R2=79.0%) from 1-year of cumulative production from 6 Montney wells in the Town Pool, which was also validated by recent completions from 6 other Town Montney Pool wells (R2=78.7%).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".