When is There Too Much Fracture Intensity?
Bibliographic record
Abstract
Abstract A distinct shift in wellbore fracture stimulation events has occurred within the Western Canadian Sedimentary Basin (WCSB) over the last 5 years. New designs, commonly referred to as "increased fracture intensity designs," are characterized by an increased number of fracture stages, decreased fracture spacing, and resulting increases in water and proppant required per stimulation. Existing technology applied in increased fracture intensity designs include: Open Hole Ball and Seat technology, Coil Activated sleeves, Plug and Perforating, as well as hybrid designs that combine several technologies. Increased fracture intensity designs have contributed to improved production rates and increased reserves and, as a result, have quickly become the preferred approach to hydraulic fracture stimulation of the reservoir. Promising hydraulic fracture designs and decreased spacing designs run the risk of being applied broadly without discrimination. Without proper retrospective or hindsight, there is a risk of over applying this new approach with false assurances of its success rates. It is therefore important to determine whether and at what point increasing fracture intensity generates diminishing returns. This paper provides 3 retrospective case studies within the regions of the greater Montney and Cardium formations where increased fracture intensity designs have led to decreased well production as well as decreased reserve allocation. We further examine the various components of increased fracture intensity designs to pinpoint areas where design optimization may have prevented these outcomes.
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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 | 0.005 | 0.018 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".