Advancements in Efficiency in Horn River Shale Stimulation
Bibliographic record
Abstract
Abstract In the remote Horn River Shale of North East British Columbia, Canada the key challenge operators’ face is high costs related to completions in the horizontal wells. A strategy was developed to focus on efficiency improvements to make a positive impact on the economics. Previous projects were examined and a two prong approach was developed: first we needed to procure as many extra resources as could be anticipated to ensure continuous operations, and second we would have to have more than one critical path operation simultaneously so that costly activities could continue uninterrupted. The resource planning includes the drilling of multiple wells on the pad and having most of the wells available for operations during the stimulation campaign. It also includes specially designed equipment such as bulk sand handling equipment, water handling, slurry handling, wellhead protection, SCADA systems and custom flow back equipment. In addition, new ways of managing the human resources at the site were implemented. To manage the critical path activities, a protocol was developed to manage surface and down hole interactions. A safety system was developed to support the integration of these challenging operations. An offsite real time operations room was employed were all the sensor data from the site was available, including frac data, water and sand supply, pressure and H2S. The resulting impact on efficiency will be discussed in detail, including reaching a field frac efficiency record for the Horn River.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".