Production characteristics and sweet-spots mapping of the Upper Devonian–Lower Mississippian Bakken Formation tight oil in southeastern Saskatchewan, Canada
Bibliographic record
Abstract
A workflow that helps identify potential production sweet spots in the Middle Bakken tight oil play is proposed based on analysis of large amounts of production data. The proposed approach is a multivariate statistical model that extracts relevant information from a training dataset of production wells to facilitate geological similarity comparison between economic and sub-economic production wells. The model is applied to the Middle Bakken tight oil play in southeastern Saskatchewan. Data screening for diagnostic geological indicators for sweet spots reveals that several geological factors indicative for conventional oil reservoirs seem to work for the Middle Bakken tight oil play as well. These factors include: a) the NE Torqunay-Rocanville Trend serving as a preferred regional migration path for connecting mature source rock in southern Williston Basin and the Middle Bakken tight reservoir in southeastern Saskatchewan; b) the oils in the Bakken tight reservoirs along the U.S. and Canada border are more likely from local matured Bakken source rocks; c) subtle structural components enhancing the convergence of dispersed hydrocarbons over a large area; d) top seal and lateral barrier improving preservation, thus favouring oil productivity; e) orientation of maximum horizontal stress coincident with the direction of the variogram spatial continuity in ultimate recoverable reserves, so the direction of horizontal well has a significant impact on the oil productivity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".