<scp> Al <sub>2</sub> O <sub>3</sub> </scp> and <scp>CuO</scp> nanoparticles as promising additives to improve the properties of <scp>KCl</scp> ‐polymer mud: An experimental investigation
Bibliographic record
Abstract
Abstract Drilling fluid technology is one of the most targeted and developed technologies worldwide due to the increasing demand for deeper drilling and more complicated wells. Several studies have shown numerous improvements in the mud characteristics when using nanoparticles (NPs) as additives. This study aims at examining the influence of using aluminium oxide (Al 2 O 3 ) and copper oxide (CuO) NPs on the characteristics of KCl‐polymer mud, which is mainly used while drilling shaly formations. Two sizes of Al 2 O 3 ‐NPs (15 and 40 nm) in addition to CuO‐NPs (40 nm) were investigated at different concentrations (0.1, 0.3, 0.5, 0.7, and 1.0 wt.%) using a standard viscometer and API filter press. Zeta potential (ζ‐potential), scanning electron microscopy (SEM), and energy‐dispersive X‐ray spectroscopy (EDX) were used to elaborate the effect of NPs on the properties of NPs‐based KCl‐polymer mud. The results showed higher potential of Al 2 O 3 ‐NPs and CuO‐NPs to enhance the mud properties when used at small concentrations of 0.3–0.5 wt.%. Furthermore, NPs were found to play a key role in building efficient filter cakes with time during filtration (up to 90 min). Moreover, smoother surface morphologies and less porous structures of filter cakes were observed when using NPs with some agglomeration of CuO‐NPs due to higher density. The Herschel‐Bulkley model was found to provide a better fitting of the rheological data of NPs‐based KCl‐polymer mud than the Bingham plastic model. This approach would be able to virtually overcome any shale issue encountered by proactively plugging nanopores while inhibiting the water absorption in shale formations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".