Factors and Mechanisms Governing Wettability Alteration by Chemically Tuned Waterflooding: A Review
Bibliographic record
Abstract
Enhanced oil recovery (EOR) techniques are aimed at improving the recovery efficiency of mature oil fields in secondary and tertiary recovery modes. In particular, chemically tuned waterflooding (CTWF) has been a rising EOR technique toward improving oil recovery from rocks that are difficult to produce due to their initial wetting state, e.g., oil-wet and intermediate-wet. With an increasing oil-wetting affinity of a reservoir rock, extraction of oil becomes more challenging. As such, wettability alteration has been identified as the primary mechanism for oil recovery from oil-wet and intermediate-wet rock types. Recently, researchers have attempted to categorize the factors and mechanisms governing wettability alteration by CTWF. Multiple studies have identified the importance of brine salinity and ion composition on promoting wettability alteration to a more favorable water-wet state. Reservoir temperature, the surface charge of the rock, and the surface active components of crude oil are also reported to influence wettability alteration and therefore oil recovery from waterflooding. In this paper, we present an extensive literature review on the subject of wettability alteration, with an emphasis on experimental work conducted on carbonate and sandstone rocks, as they constitute the majority of the oil reserves in the world. The purpose of this review paper is to synthesize the current state of knowledge regarding the factors and mechanisms that govern wettability alteration by CTWF and, through this exercise, set the platform to pose new research questions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".