Study on a reusable surfactant fracturing fluid and its application in Chang‐6 oil reservoir
Bibliographic record
Abstract
Abstract In order to improve the utilization of fracturing fluid and reduce the environmental pollution risk in large‐scale fracturing of oil reservoirs in Ordos Basin, a type of reusable surfactant fracturing fluid is developed. Its formula composition is 15 × 10 −3 g/mL of the synthesized surfactant and 6 × 10 −3 g/mL of composite acid. The synthesized surfactant is formed by using 13‐docosaenoic acid, 3‐dimethylaminopropylamine, sodium chloroacetate, and other raw materials. The dissolution time of the fracturing fluid is less than 1 min. Due to the reversible entanglement interactions between the surfactant micelles, it is a viscosity‐elastic fracturing fluid with good sand carrying capacity. It can remain basically unchanged after shearing. The surfactant fracturing fluid can be automatically broken to contact with the crude oil in the oil reservoir. The breaking time can be controlled within 100 min by adjusting the concentration of crude oil. After gel breaking, the viscosity is less than 2.9 mPa ∙ s and the residue content is less than 1.25 mg/L. The interfacial tension of oil and the breaking liquid is below 0.1 mN/m. Subsequently, the field application of the fracturing fluid in N190 oil well has been carried out successfully. The backflow fluid of the surfactant fracturing fluid is successfully reused three times, and the performance of the fracturing fluid prepared in the field is basically consistent with the results of laboratory experiments. The fracturing construction by using the developed surfactant fracturing fluid can achieve a good fracturing stimulation effect and save a large amount of water resources.
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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 | 0.001 | 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".