Improving the performance of oil based mud and water based mud in a high temperature hole using nanosilica nanoparticles
Bibliographic record
Abstract
Oil-based mud (OBM), a non-Newtonian fluid, is known for its superior performance in drilling complex wells as well as combating potential drilling complications. However, the good performance may degrade under certain circumstances especially because of the impact of chemical instability at an elevated temperature. The same phenomenon occurs for water-based mud (WBM) when it is used in drilling under high temperature conditions. To prevent this degradation from occurring, numerous studies on utilizing nanoparticles to formulate smart fluids for drilling operations are being conducted worldwide. Hence, this study aims to evaluate the performance of nanosilica (NS) as a fluid loss reducer and a rheological property improver in both OBM and WBM systems at high temperature conditions. This study focuses on the impacts of different nanosilica concentrations, varying from 0.5 ppb to 1.5 ppb, and different mud weights of 9 ppg and 12 pg as well as different aging temperatures, ranging from ambient temperature to 300 °F, on the rheological performance of OBM and WBM. All the rheological properties are measured at ambient temperature, and additionally tests, including lubricity, electrical stability, and high-pressure high-temperature filtration measurements, are conducted, and rheological models are obtained. The performance of nanosilica is then studied by comparing each of the nanosilica-enhanced mud systems with the corresponding basic mud system, taking the fluid loss and rheological properties as the benchmark parameters. Nanosilica shows a positive impact on OBM and WBM, as the presence of nanosilica in the mud systems can effectively improve almost all their 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 | 0.000 |
| 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) | 0.000 | 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".