Performance evaluation of different types of surfactants to inhibit clay swelling during chemical enhanced oil recovery
Bibliographic record
Abstract
Abstract In most cases, sandstone reservoirs contain clay particles. When a fluid with low salt content is injected into the reservoir, clay particles swell, and the rock's permeability decreases. Since surfactant flooding with water that has lower salinity than formation water is used to improve oil recovery, this study examines the swelling inhibitive strength of various surfactants. The study investigates the effect of surfactants on clay swelling inhibition through linear swelling and sedimentation experiments which showed that cetyl trimethyl ammonium bromide (CTAB) inhibits clay swelling. The optimum CTAB concentration was found to be 1 wt.%. However, sodium dodecyl sulphate (SDS) and Triton X‐100 (TX‐100) did not inhibit clay swelling. Sandpack experiments confirmed that CTAB prevented clay swelling and sandpack permeability reduction, while SDS and TX‐100 did not. The injection pressure results for the different surfactants were as follows: CTAB—39 psi, SDS—203 psi, TX‐100—223 psi, deionized water (DW)—324 psi, and KCl—78 psi. The mechanism of surfactant performance in affecting clay swelling was investigated based on thermal gravimetric analysis (TGA), zeta potential, and scanning electron microscope (SEM) images. The results showed that CTAB compensates for the surface charge of clays through a cation exchange mechanism. The amount of absorbed water for clays modified with CTAB was lower than that for other surfactants. The SEM images confirmed that the CTAB‐modified clays were larger and did not disintegrate during dispersion; however, in other cases, the particle size was smaller.
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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) | 0.000 | 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 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".