SURFACTANT AND ALKALINE SCREENING FOR ASP FLOODING IMPLEMENTATION IN THE UZEN FIELD IN KAZAKHSTAN
Bibliographic record
Abstract
Alkaline-Surfactant-Polymer (ASP) flooding is a tertiary oil recovery technique that has been increasingly utilized to enhance oil production rate and recovery factor while reducing chemical adsorption. The ASP flooding process involves the injection of a mixture of alkaline, surfactant, and polymer solutions into an oil reservoir to reduce the interfacial tension between the oil and water phases by alkaline-surfactant and improve the sweep efficiency by a polymer. This method has shown great global potential in various oil fields and has been implemented in countries such as USA, Canada, China, Russia, and India. The Uzen field in Kazakhstan, one of the oldest fields in the country, has been waterflooded for decades and has not produced even 50% of the original oil in place (OOIP). Due to high paraffin content causes oil is waxy. Currently, the water cut of the field became more than 90%, necessitating the use of new production methods. Therefore, ASP is proposed to enhance oil recovery in the Uzen field as a tertiary recovery method. The goal of this study is to design an effective ASP flooding program for enhancing oil recovery in the Uzen field. The study focuses on conducting laboratory experiments to identify the most effective alkaline and surfactant by designing their compatibility and stability under reservoir conditions. The performance of the screened chemicals in the porous media was analyzed by sequences of experiments such as coreflooding. The findings of the study indicate that alkali agents, particularly sodium sulfite, negatively impact on surfactant’s performance. The most effective surfactant combination was found which gave Winsor type III microemulsion and the lowest IFT. The polymer did not impact on surfactant performance, so both sequential and combined SP coreflodings demonstrated high RF and microemulsion production. Additional studies and corefloodings are recommended for ASP project in the Uzen field, especially with alkali since it behaves as unstable with Caspian seawater.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".