The Effect of High Pressures and High Temperatures on the Properties of Water Based Drilling Fluids
Bibliographic record
Abstract
Tremendous amounts of hydrocarbons are located in deeper formations where higher pressures and temperatures are experienced. Designing a proper drilling fluid that can tolerate such high-pressure, high-temperature (HP/HT) conditions is a challenging task. The work presented in this paper is focused on investigating the rheological behavior of water-based drilling fluids with different properties at extremely high pressure and temperature conditions using a state-of-the-art viscometer capable of measuring drilling fluids properties up to 600 °F and 40,000 psig. The results of this study show that the viscosity, yield point and gel strength decrease exponentially with increasing temperature until the mud samples fail. This behavior is the result of the thermal degradation of the solid, polymers, and other components of the mud samples. Increase in intermolecular distances due to high temperature will lower the resistance of the fluid to flow and, hence, its viscosity, yield point, and gel strength will reduce. Moreover, conducting this study led to the conclusion that viscosity, yield point, and gel strength increase linearly as the pressure increase. Pressure’s effect on these parameters, however, is more apparent at lower temperatures. Ultimately, the study concluded that the mud samples that were used, which are standard industrial types, failed at a temperature of 250 o F and that the combined effect of temperature and pressure on mud’s rheology is complex. Key words: Drilling fluids; HPHT; Rheology; Water based mud
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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) | 0.000 | 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".