Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 175937, “Designing an Optimized Surfactant Flood in the Bakken,” by Matthew Dawson, Statoil; Duy Nguyen, Nalco Champion; and Huina Li, Statoil, prepared for the 2015 SPE/CSUR Unconventional Resources Conference, Calgary, 20–22 October. The paper has not been peer reviewed. The Bakken is one of the most prolific plays in North America, but, even with the deployment of horizontal wells and hydraulic fracturing, anticipated recovery factors under primary depletion are usually in the range of 10 to 20%. Waterflooding has been a commonly deployed technology in conventional reservoirs to enhance recovery beyond primary depletion. However, the Bakken’s ultratight, largely oil-wet nature limits the potential of waterflooding. As an alternative, an optimally spaced well-to- well surfactant-flooding technology is proposed. Introduction Recent studies focusing on wettability alteration by use of surfactant in the Bakken have shown strong potential. Spontaneous-imbibition tests in Bakken cores show recovery factors that can exceed 30% and sometimes achieve up to 60%. However, in an ultralow-permeability system, the rate of surfactant imbibition is perhaps more important than the ultimate recovery factor. Initial studies show potential, but to achieve an economical surfactant recovery process, ultrahigh imbibition rates must be achieved. In addition to the technical challenges associated with stability, compatibility, and injectivity, economical deployment of surfactant in a marginally profitable play such as the Bakken is another major challenge. To minimize the surfactant required for a successful process, a surfactant would ideally have an ultralow critical micelle concentration (CMC) and low adsorption while maintaining key performance indicators that are discussed in the complete paper.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".