Performance and mechanism of <scp>DL‐alanine</scp> ionic liquid shale inhibitor
Bibliographic record
Abstract
Abstract Shale hydration and expansion during drilling can lead to wellbore instability, sticking, and frequent leakage accidents. To prevent these issues, an ionic liquid (IL) inhibitor solution was synthesized using DL‐alanine and 98% concentrated sulphuric acid as raw materials. The optimum composition and effect of the synthesized inhibitor in oilfield water‐based drilling fluids were evaluated through anti‐expansion, linear expansion rate, water washing resistance, and clay hydration dispersion experiments. The inhibition mechanism was investigated by thermogravimetric analysis, infrared spectroscopy, zeta potential, contact angle, and X‐ray diffraction analysis. The results show that the optimum IL consists of a 1:1 molar ratio of DL‐alanine to concentrated sulphuric acid synthesized under a reaction temperature and time of 70°C and 18 h, respectively. The resulting DL‐alanine IL exhibited the best inhibitory effect on the hydration, dispersion, and expansion of clay at a concentration of 0.5% in aqueous solution, and its linear expansion rate was only 18.49%. After adding a quantitative amount of clay to different concentrations of DL‐alanine IL aqueous solution, the expansion volume of the clay decreased and the water washing resistance significantly improved. These results provide a theoretical basis for improving the safety and efficiency of shale horizontal well drilling operations.
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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) | 0.001 | 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".