The Rheological Properties of Oil-Based Mud under High Pressure and High Temperature Conditions
Bibliographic record
Abstract
Designing a proper drilling fluid that can function properly under the conditions of High-Pressure, High-Temperature (HP/HT) operations is very challenging. Among these challenges is the alteration of the rheological properties of drilling fluid due to the high temperature and high pressure (Ibeh et. al, 2007). This work investigates the rheological behavior of oil-based drilling fluids with different properties at Ultra-HP/HT conditions using a state-of-the-art viscometer capable of measuring drilling fluids properties up to 600°F and 40,000 psi. For this purpose, two actual oil based mud samples used by industry with the same mud weight (12.5 ppg) were chosen to carry out a matrix of experiments. The results of this study led to concluding that the viscosity, yield point and gel strength decrease with increasing temperature (until the mud sample fails, for oil-based mud with regular formulation). This behavior is the result of the thermal degradation of the solid, polymers, and other components of the mud samples and the expansion of the molecular distances which will lower the resistance of the fluid to flow and, hence, its viscosity, yield point, and gel strength. Moreover, it is concluded that the viscosity and yield point increase as the pressure increases. Pressure’s effect on these parameters, however, is more apparent at low temperature (below failure point, for oil-based mud with regular formulation). Key words : High pressure high temperature; Oil-based mud; Rheology; Rheological properties
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".