Case Studies in Production Optimization Using Chosen Information Sources and Information Technology
Bibliographic record
Abstract
Abstract Well data for oil and gas wells drilled in the Western Canadian Sedimentary Basin (WCSB) exist in many places and multiple formats with ranging detail. Multiple data sources, both internal and external, are needed to optimize the well stimulation and the production optimization process. Public production and well data in the WCSB is considered high in quantity and quality for reservoir description. This data repository exists due to royalty and tax reasons and is strictly enforced by government legislation. Unfortunately, detailed completion data are not part of the public database records. However, companies whose core business include drilling and completion field services usually keep a comprehensive and detailed collection of data. These pumping service companies often have data that are unavailable in the public databases. This paper outlines the first steps in developing a synergistic approach to integrating publicly available production and well data with detailed completion data from private sources. The case study presented shows the effectiveness of combining internal detailed completion data with external third party data. Case study #1 evaluates various fracture fluid systems and size on production in the Medicine Hat gas of southeast Alberta, Canada. Case study #2 evaluates stimulations in the Horseshoe Canyon Coal Bed Methane (CBM), fairway in south central Alberta, Canada. Production impact of current industry stimulation practise and some alternatives are examined.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".