Asset Development Drivers in the Bakken and Three Forks
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".