A biosurfactant for inhibiting clay hydration in aqueous solutions: Applications to petroleum industry
Bibliographic record
Abstract
Abstract The development of high performance and environmentally‐friendly clay stabilizers is still crucial to the drilling industry. This study aims to evaluate the impact of mulberry leaf extract (MLE), a biosurfactant as a clay stabilizer, on hydration of sodium bentonite in aqueous solutions. Several experimental techniques including sodium bentonite inhibition, sedimentation, filtration, zeta potential, particle size, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) are utilized in this study. Based on the results, MLE aqueous solution loads a larger amount of sodium bentonite in contrast to deionized water, leading to a low rheological profile. Sodium bentonite is not able to form a stable dispersion in the MLE aqueous solution. The capability of sodium bentonite to control the fluid loss is considerably lost upon exposure to the aqueous solutions of MLE. In the presence of MLE, the zeta potential of sodium bentonite dispersion shifts towards positive values, implying the instability of the water film surrounding the sodium bentonite particles. Sodium bentonite particles are larger in the MLE aqueous solution, representing a lower degree of particle delamination. A lower water content is observed in TGA for sodium bentonite modified by MLE. In SEM analysis, sodium bentonite particles show larger aggregates in the MLE aqueous solution. MLE aqueous solutions also have larger shale cuttings, in contrast to deionized water. All experiments confirm the promising features of MLE as an excellent clay stabilizer during drilling through clay rich shales. In addition to the superior performance in the drilling industry, MLE exhibits an environmentally‐friendly characteristic which is an asset for this biosurfactant.
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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) | 0.000 | 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 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".