Asset Development Drivers in the Bakken and Three Forks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Home to one of the largest North American deposits discovered in the last few decades, the Bakken, spanning 200,000 square miles along the borders of Saskatchewan, North Dakota and Montana is rivaling some of the largest proven reserves. As the use of long horizontal wells and multi-stage fracturing technology has significantly increased productivity and activity in the basin, the challenges associated with infill-completions, depletion and controlled fracture growth must be addressed to ensure efficient and effective practices, encouraging long-term planning without hindering investment. In this paper, models are built to replicate well performance (fracturing and production-numerical & rate-transient) and to understand the impact of key technologies (multi-stage/completion type and multi-laterals) across the basin to demonstrate why completion strategies must be modified based on reservoir quality and stress state. Confusion between the success of sliding sleeves/plug and-perf and what drives the optimal number of stages is also addressed using fracture modeling and production modeling with emphasis on key parameters (fracture length, connectivity, number of fractures) influencing productivity. The recent focus on data acquisition and modeling in the Three Forks has presented a range of challenges and opportunities due to the laminations in this reservoir. Log up-scaling methods and simulator engines were crucial to modeling and thus evaluating propagation behavior. This paper also presents how the use of data gathering (log, routine and specialized core) and modeling has enabled us to understand how in-fill drilling can alter drainage patterns and influence production success.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it