Experimental investigation on the effects of deep eutectic solvents (DES) on the wettability of sandstone samples
Bibliographic record
Abstract
Recently, deep eutectic solvents (DES) have received great attention in assisting water flooding and surfactant flooding to improve oil recovery because they can reduce the interfacial tension (IFT) between oil and water, inhibit surfactant adsorption, and change the wettability of rock. However, the effects of DES on the wettability of rock surface have not been thoroughly investigated in the reported studies. In this study, the effects of various DES samples on the wettability of sandstone samples are investigated using the Amott wettability measurement method. Three DES samples and several DES solutions and DES-surfactant solutions are firstly synthesized. Then, the wettability of the sandstone samples is measured using pure saline water, DES solutions, and DES-surfactant solutions, respectively. The effects of the DES samples on the wettability of the sandstone samples are investigated by comparing the measured wettability parameters, including oil displacement ratio ( I o ), water displacement ratio ( I w ), and wettability index ( I A ). The Berea rock sample used in this study is weakly hydrophilic with I o , I w , and I A of 0.318, 0.032, and 0.286, respectively. Being processed by the prepared DES samples, the wettability of the Berea sandstone samples is altered to hydrophilic (0.7> I A >0.3) by increasing I w but lowering I o . Similarly, DES-surfactant solutions can also modify the wettability of the Berea sandstone samples from weakly hydrophilic to hydrophilic. However, some DES-surfactant solutions can not only increase I w but also increase I o , suggesting that the lipophilicity of those sandstone samples will be improved by the DES-surfactant solutions. In addition, micromodel flooding tests confirm the promising performance of a DES-surfactant solution in improving oil recovery and altering wettability. Moreover, the possible mechanisms of DES and DES-surfactant solutions in altering the wettability of the Berea sandstone samples are proposed. DES samples may improve the hydrophilicity by forming hydrogen bonds between rock surface and water molecules. For DES-surfactant solutions, surfactant micelles can capture oil molecules to improve the lipophilicity of those sandstone samples.
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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) | 0.001 | 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) | 0.001 | 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".