Fracture Stimulation Treatment Best Practices in the Bakken Oil Shale
Bibliographic record
Abstract
Abstract This paper summarizes fracture stimulation treatment best practices determined by a three-year study of production results of several thousand fracture treatments in more than 460 wells in the Bakken Shale formation in Saskatchewan, Canada. Treatment variables include: proppant type, proppant concentration, fracturing fluid formulation, treatment size, well-bore azimuth, and lateral frac density. Treatment effectiveness is based on four month cumulative production comparisons that include: oil, water, and total fluid. The Bakken is a low-permeability oil and water-producing formation at a depth of about 1500 m (4900 ft). The study area covers about 390,000 hectares (1500 mile2) in the northern region of the Williston Basin. Wells in this region require propped fracture stimulations for economic production. All wells in this study are drilled horizontally, normally completed open-hole in the Middle Bakken. Most completions consist of isolated multistage treatments. One of the significant challenges encountered during fracture treatment design is the presence of an overlaying water aquifer, known as the Lodgepole. Fracture treatments that breach the Lodgepole aquifer are considered less than optimum because of excess water production. This paper will also review design and operational considerations for the area, including treatment volumes, pump rates and fluid chemistry. Wells in the study were primarily completed with two types of water based fluids. Treatment size and treatment rates are important considerations. The requirement to limit fracture growth conflicts with the theoretical need for a large, conductive propped fracture. However, the study shows that proppant concentration and the resulting fracture conductivity impact fluid production. Lateral length, number of frac stages, and distance between stages are also addressed in the paper. Results of this study will be presented as best practices that may be used to refine completion techniques to maximize hydrocarbon production without excessive water production in this important oil-producing basin.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".