The impact of particle size distribution of haematite‐based drilling fluid on perlite functionality
Bibliographic record
Abstract
Abstract Drilling fluid additives are essential in formulating an optimal mud composition for the desired geological formation. They provide different functions that enable the drilling process to reach a target zone. However, the effectiveness of these additives may be limited under certain conditions. The physical attributes of the weighting material additives such as solid particle size (particle size distribution [PSD]). The PSD can influence multiple drilling fluid properties, including the rheology, filtration, and filter cake properties. This work examined the performance of perlite when incorporated into water‐based mud with three different grades of haematite particle sizes. The investigation focused on the rheological properties, filtration, and filter cake characteristics utilizing two distinct filtration mediums: the ceramic disk and core sample. The results indicated that the PSD minimally impacts the filtration and the filter cake properties when the pore distribution of the filtration medium is uniform at low drilling mud density. However, a more pronounced effect was observed when core samples were utilized during the filtration test, reflecting the variation in pore distribution. Remarkably, perlite exhibited exceptional effectiveness in improving the drilling fluid properties. Its influence became particularly evident when core samples were employed as the filtration medium, demonstrating resilience against changes in both filtration medium pore distribution and haematite‐varied particle size grades. In this case, maximum improvements of 69%, 84%, and 86% were achieved in filtration volume, filter cake thickness, and filter cake permeability, respectively. Perlite showed an excellent performance at varied conditions represented by filtration medium and weighting material PSD.
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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".