Olefin-Based Synthetic-Drilling-Fluids Volumetric Behavior Under Downhole Conditions
Bibliographic record
Abstract
Summary Usual practice in drilling engineering is to determine drilling-fluid density at surface conditions assuming that drilling-fluid density does not change with changing downhole conditions. This assumption may result in inaccuracy while estimating static and dynamic pressures in the wellbore, especially when synthetic-based drilling fluids are used. Inaccurate estimation of the pressure profile in the annulus can lead to severe well problems such as kicks, drilling-fluid losses, and wellbore instability. In addition, inaccurate pressure-profile estimation can affect the success of managed-pressure-drilling (MPD) operations, which require real-time knowledge of wellbore pressures to keep wellbore pressure between formation pore and fracture pressures using a control choke placed on the return line of the annulus. Effects of pressure and temperature on volumetric behavior of two olefin-based synthetic oils are investigated in this study using a mercury-free pressure/volume/temperature (PVT) system. The olefin-based synthetic oils used in this study are C16C18 internal olefin (IO) and C12C14 linear alpha olefin (LAO). To simulate deep offshore situations, the temperature is ranged between 25 and 175°C, while the pressure is ranged between 0 and 14,000 psig. In addition, volumetric performances of olefin-based synthetic-oil systems under investigation are compared with those of water, brine solution, mineral oil, diesel oil, and n-paraffin-based oil under similar conditions. The study shows that the volumetric properties of synthetic-based oils are more sensitive to pressure and temperature conditions compared to water, brine solutions, mineral oil, and diesel oil. Once emulsion systems containing synthetic-based oils are used, density change with respect to downhole conditions should be modeled to increase the reliability of pressure-profile calculations.
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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 | 0.000 |
| 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".