The Comprehensive Overview of Large-Volume Surfactant Slugs Injection for Enhancing Oil Recovery: Status and the Outlook
Bibliographic record
Abstract
Despite the development of alternative energy sources, oil and gas still remain the predominant energy sources in most countries in the world. Due to gradual hydrocarbon reserve depletion and the existing downward trend in the production level, there is a need to search for methods and technical approaches to level off the falling rates. Chemically enhanced oil recovery methods (EOR) by surfactant solution injections are one of the possible approaches for addressing this issue in already developed fields. Most often, surfactants are injected together with polymers or alkalis. These technologies are called surfactant–polymer (SP) and alkali–surfactant–polymer (ASP) flooding. Basically, SP and ASP have been distributed in China and Canada. In this article, in addition to these countries, we paid attention to the results of pilot and full-scale tests of SP and ASP in Russia, Hungary, and Oman. This study was a comprehensive overview of laboratory and field tests of surfactant solutions used for oil displacement in SP and ASP technologies. The first part of the article discussed the physical fundamentals of the interaction of oil with surfactants. The second part presented the main chemical reagents used to increase oil recovery. In the third part, we described the main facilities used for the preparation and injection of surfactants. Further, the results of field tests of SP and ASP in the abovementioned countries were considered. In the discussion part, based on the considered results, the main issues and uncertainties were identified, based on which some recommendations were proposed for improving the process of preparation and injection of surfactants to increase oil recovery. In particular, we identified an area of additional laboratory and scientifically practical research. The outcomes of this work will provide a clearer picture of SP and ASP, as well as information about their limitations, current challenges, and potential paths forward for the development of these technologies from an economic and technological point of view.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".