Rheological Modeling of Sustainable Drilling Fluid Optimized for Electromagnetic Geophysical Exploration
Bibliographic record
Abstract
Abstract In borehole environments, the use of optimized imaging fluids is critical for enhancing Ground Penetrating Radar (GPR) data quality by mitigating the adverse electromagnetic effects caused by water. The novel imaging fluid developed at the Drilling Technology Laboratory (DTL) of Memorial University of Newfoundland offers a unique solution, utilizing optimized electromagnetic properties to significantly improve signal clarity during borehole electromagnetic surveys. Despite the promise of this fluid, there has been a notable gap in the detailed study of its rheological behavior, which is essential for effective application, particularly in fluid circulation and placement in borehole operations. This study addresses this gap by conducting an in-depth analysis of the rheological properties of this E-M compatible imaging fluid, formulated with weighting agents of different grain sizes. Results from experimental measurements were utilized to model the fluid’s behavior using several mathematical models, including Bingham Plastic, Power Law, Herschel-Bulkley, Casson, and Robertson-Stiff models. By comparing the experimental results with theoretical predictions using statistical techniques such as RMSE, absolute average percentage error (ϵAAP), and standard deviation of average percentage error (SDϵAAP), the Herschel–Bulkley and the Robertson and Stiff rheological models accurately predicts fluid rheology for API Barite and Micro Barite weighting agent fluids. This research not only fills the critical gap in understanding the rheology of the developed E-M compatible imaging fluids but also establishes a foundation for selecting the optimal rheological model for hydraulic calculations, improving the overall efficacy of electromagnetic surveying in borehole environments.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".