Bibliographic record
Abstract
Abstract The introduction of large, multi-stage fracturing to the shale gas industry has made previous uneconomic shales economic to complete. These large scale stimulations present a new challenge in planning and logistics in order for the completion operation to be successful. This paper provides an overview of the planning, execution, and improvement of logistics that went into the first two shale gas well completions in Northeast British Columbia, Canada for a large Canadian Energy Company. The logistical requirements taken into consideration when designing the first multi-stage horizontal well completion were: Fracture Stimulation, Lease, Fluid, Proppant, Equipment, and Completion Procedure. Planning these requirements started six months before completion operations commenced in order to meet the target pumping date of the stimulation and avoid costly delays. The learning from the first shale gas well completion improved the logistics on the second shale gas well completion and the experience from the first two completion operations will shape future logistics as the project proceeds into the future. This paper shows that proper planning on large shale gas completions can pay off in well run, efficient completion operations that meet timing and cost objectives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".