Enhancing Well Stimulation with Improved Salt Tolerant Surfactant for Bakken Formation
Bibliographic record
Abstract
Abstract The Bakken formation is located in parts of Montana, North Dakota, and Saskatchewan, Cananda. North Dakota alone produces more than 700,000 barrels per day and still growing, which accounts for 11 percent of the domestic total crude oil production, while the oil recovery factor of 1% in Bakken is necessary to be improved to a higher level. Current practice involves fracture stimulation of the mature Upper and Lower Bakken Shales above and below the middle layer (pay zone in carbonate or siltstone). The fractures penetrate and connect natural fractures within the reservoirs and promote more efficient drainage of the oil. This situation suggests that a surfactant technology incorporating in the well stimulation fluids at low dosage can be a fit in Bakken formation to enhance oil recovery. If properly designed, such additives in the fracture fluids will penetrate into the highly oil-saturated matrix or natural fracture region and accelerate the extraction of the oil in place by rapid imbibition. Extracted oil can readily flow from the matrix into the propped fracture system. Another benefit of this surfactant technology is its engineered property to leave the matrix or natural fracture face water-wet to facilitate oil movement during production. This paper presents a study of a series of such stimulation fluid additives developed for enhanced oil recovery. Crude oil samples from the Bakken formation were analyzed for several properties pertinent to surfactant formulation including organic constituents, total acid number, and viscosity. Compatibility tests were carried out including surfactant/produced water interaction, emulsion tendency, and surfactant compatibility with proposed fracturing fluids. A spontaneous imbibition process was employed to mimic surfactant additives penetrating the tight matrix and microfractures to recover more hydrocarbons. The results show that more than one of these products improved recovery of Bakken crude oil by spontaneous imbibition. The best of these products is recommended for field application.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".