A comprehensive investigation on the performance of durian rind as a lost circulation material in water based drilling mud
Bibliographic record
Abstract
Oil and gas operators worldwide are expecting service companies to deliver integrated techniques to minimize, if not prevent, drilling problems. Drilling fluids perform vital functions to ensure the success of drilling operations. The technical challenges often associated with water-based drilling fluids are loss of critical properties, such as fluid loss control and rheology, under demanding conditions, such as in drilling deeper, high-temperature and high-pressure wells. Fluid loss during drilling operations has a very significant effect in both reservoir formation damage and monetary terms. The use of durian rind (DR) as a new additive in controlling lost circulation would provide another opportunity to reduce waste and avoid pollution. Therefore, DR was used to improve the rheological properties of water-based mud, and it was prepared for use as a fluid loss additive. For a better understanding of the influence of pectin on drilling mud properties, the rheological evaluation of untreated DR was compared to that of mud samples containing treated DR. The pectin in DR was extracted using four different solvents, namely, ethanol, methanol, sodium hydroxide and hydrogen peroxide, and the most effective solvent to remove the pectin was then determined. The Fourier transform infrared spectroscopy (FTIR) results showed that NaOH was the best solvent for removing pectin from DR. Thermogravimetric analysis (TGA) was used to determine the thermal stability of DR before and after treatments. The TGA results demonstrated that the treated DR had improved thermal stability compared to untreated DR. The sizes of DR used were coarse, medium, and fine. The untreated DR presented better rheological properties than the treated DR. The experimental investigation showed that a concentration of 20 lb/bbl of intermediate-sized DR was the best concentration among the tested 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".