Enhancing Oil Recovery by Adding Surfactants in Fracturing Water: A Montney Case Study
Bibliographic record
Abstract
Abstract In this paper, we experimentally investigate the possibility of formation damage reduction during leak-off and flowback processes by adding surfactants to the fracturing fluid. The experiments consist of three phases: In phase I, we conduct compatibility tests on surfactant solutions to select the compatible surfactant solutions with saline reservoir brine. In phase II, we measure interfacial tension between the stable surfactant solutions and the Montney reservoir oil. In phase III, we conduct a series of core flooding tests on the Montney tight core plug to experimentally investigate the leak-off and flowback processes. To evaluate the effects of surfactant solutions on the formation damage reduction we 1) measure the pressure drop across the core plug during both leak-off and flowback processes, and 2) evaluate the oil recovery factor during the leak-off process. The results of the compatibility tests show that the anionic soloterra surfactant solutions with hydrophile-lipophile balance (HLB) numbers higher than 10 are compatible with the saline/reservoir brine. Among all stable soloterra surfactant solutions, the soloterra 983 solution shows the lowest interfacial tension (IFT) values. The results of core flooding experiments indicate that the addition of soloterra 983 surfactant into the saline reservoir brine can reduce the pressure drop during leak-off and flowback processes, and therefore, it decreases the possibility of aqueous phase trapping and formation damage in the Montney tight core plugs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".