Experimental investigation of the effects of SnO<sub>2</sub> nanoparticles and KCl salt on a water base drilling fluid properties
Bibliographic record
Abstract
Abstract Reduction of proven gas and oil reserves and increasing demand for energy forces the petroleum industry to drill deeper and more complicated wells. Drilling in these harsh environments requires drilling fluids with specific characteristics. So, improvement of the drilling fluids that can act as proper fluids at high pressure and high temperature conditions is vital in the drilling industry. The purpose of this study is examining the effects of SnO2 nanoparticles on the properties of drilling fluids and determining of the extent of improvement of water‐based drilling fluids performance. These nanoparticles were added to polymeric water‐based drilling fluids in various concentrations of 1, 2.5, 5, and 7.5 g/L in the presence of KCl at concentrations of 5, 15, 30, 60, and 100 g/L. The experiments were done at temperatures of 30, 50, 70, and 90 °C. Electrical conductivity, thermal conductivity, and thixotropy of the resulting drilling fluids were investigated. Moreover, filtration of the drilling fluids at room temperature, 65, and 95 °C and pressures of 0.6895 MPa (100 psig) and 2.758 MPa (400 psig) and various nanoparticle concentrations were studied. It was found that electrical conductivity and thermal conductivity were increased by 30 % and 15 %, respectively. Finally, in order to have more accurate hydraulic calculations, five rheological models were studied and compared together. It was observed that the Herschel‐Bulkley‐Papanastasiou model showed the highest accuracy with an absolute relative error of 1.1 %.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".