Surfactant Partitioning and Adsorption in Chemical EOR: The Neglected Phenomenon in Porous Media
Bibliographic record
Abstract
Abstract During chemical EOR, surfactants encounter significant losses when injected into porous media mainly due to retention. The key mechanisms of surfactant retention are adsorption onto the rock surface and partitioning into the oil phase. The significant losses due to adsorption and partitioning will not only result in poor displacement efficiency but also great financial increased costs. In this review, a comprehensive assessment on the importance of understanding and quantifying surfactant partitioning and adsorption data is presented. The study explains the surfactant flooding process and the related challenges at harsh reservoir conditions. The surfactant partitioning and adsorption mechanisms throughout the surfactant flooding process, as well as the most influential parameters affecting their behaviors in porous media are comprehensively addressed. Surfactant partitioning and adsorption studies at different operating conditions are then covered considering laboratory, modeling, and simulation studies. Lastly, the measurement procedure and the measurement techniques of surfactant partitioning and adsorption are comprehensively discussed. Laboratory and simulation studies have concluded that the misinterpretation of surfactant partitioning and adsorption data will affect the main function of surfactants (lowering oil–water interfacial tension). The reported studies have highlighted that surfactant partitioning and adsorption are affected by many factors such as surfactant concentration, pH, salinity, temperature, brine/oil ratio, and rock mineralogy. In contemporary research practice, there is no established method to quantify the surfactant losses due to partitioning in dynamic conditions owing to the occurrence of both adsorption and partitioning simultaneously. However, using static tests, adsorption and partitioning can be distinguished, quantified, and qualitatively verified with dynamic test results. The partitioning effect can be separated, since the test is performed with and without residual oil saturation (oil flood), and by comparing those tests, the effect of partitioning can be detected. The novelty of this review is based on the importance of understanding the mechanisms of surfactant partitioning and adsorption, which have not been fully covered in the literature. This paper gives more insight into the successful application of surfactant flooding and how it can be optimized with minimal surfactant losses. Findings elucidated in this paper can contribute to minimizing the experimental time and operating cost of future studies in the field of surfactant-based EOR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".