The Progression of Fracture Stimulations in Horizontal Wells Targeting the Montney Formation in the Heritage Field, British Columbia, Western Canada
Bibliographic record
Abstract
Abstract Producing unconventional resources through hydraulic fracturing is a continuous learning process where the fracture stimulation design can change throughout the life of the field development. The well completion technique can change because of many reasons such as lessons learned from previous stimulations, new technology, and the heterogeneous nature of the reservoir. This paper focusses on real world data of hydraulic fracture stimulation properties sourced from public databases which is reported from wells drilled in the Heritage Field near Dawson, British Columbia. These wells are licensed as horizontal wells targeting gas production from the Montney Formation. By plotting and mapping the fracture stimulation data collected from the area and focusing on a set of operators with major operations in the area, this paper demonstrates how the fracture stimulation has changed over time and illustrates the design changes of the fracture stimulations for each operator over their own development timeline. This paper also discusses the collaboration initiatives that were implemented as the development progressed to highlight how industry and regulators can work together to responsibly produce the resource most effectively while still maintaining healthy competition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".