Data Analytics and Machine Learning Predictive Modeling for Unconventional Reservoir Performance Utilizing Geoengineering and Completion Data: Sweet Spot Identification and Completion Design Optimization
Bibliographic record
Abstract
Abstract Performance of wells in an unconventional reservoir are largely diverse due to different geology, reservoir characteristics, and completion design. A comprehensive method of data analytics and predictive Machine Learning (ML) modeling was proposed to identify production zone "sweet spots", and optimize completion designs from reservoir quality (e.g., geological, geophysical, and geomechanical) data and completion quality data (e.g., frac stage spacing, fluid volume, and proppant intensity, in order to enhance performance of production wells in unconventional reservoirs. Typical data analytics and predictive ML modeling approach utilizes all the reservoir quality data and completion quality data together, which mostly leads to domination of the completion quality data over the reservoir quality data because of 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 the 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 modellings. The patent-pending methodology of predictive ML modeling has been exercised in recently developed wells of the Montney unconventional shale gas formation, British Columbia Canada, and identified sweet spots from key controlling reservoir quality data and as well as prescribed optimal completion designs from key controlling completion quality data.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".