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 SnO 2 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 %.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".