Optimizing drilling fluid rheology: The role of particle size distribution and advanced rheological modelling
Bibliographic record
Abstract
Abstract This study presents the first integrated evaluation of how particle size distribution (PSD) of weighting materials affects both the rheological behaviour of water‐based drilling fluids and the predictive accuracy of advanced rheological models. The originality lies in using complete mud formulations with multiple weighting materials, baryte, haematite, ilmenite, and Micromax, in both raw and milled forms, which enables a broader assessment than previously reported. Practically, the findings offer a pathway to optimize drilling fluid design for better viscosity control, improved wellbore stability, and reduced formation damage. Experimentally, reducing the D50 of baryte from 17.78 to 3.13 μm and haematite from 12.49 to 3.60 μm led to an approximate 50% increase in plastic viscosity and over 70% increase in yield point at a density of 16 ppg. Rheological models including Newtonian, Bingham Plastic, Power Law, Herschel–Bulkley, and Cross were applied, with Herschel–Bulkley and Cross models yielding R 2 values near 1.0 and RMSE as low as 0.23. Notably, finer particle sizes were better captured by advanced models, with Herschel–Bulkley and Cross outperforming simpler models, particularly at higher mud densities. These results demonstrate the critical role of PSD not only in fluid behaviour but also in selecting accurate rheological models for better prediction and formulation strategies. These results demonstrate the critical role of PSD not only in fluid behaviour but also in the selection of accurate rheological models for better prediction and formulation strategies.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".