Novel Nanoparticle-Based Drilling Fluid with Improved Characteristics
Bibliographic record
Abstract
Abstract The success of drilling operations is heavily dependent on the drilling fluid. Drilling fluids cool down and lubricate the drill bit, remove cuttings, prevent formation damage, suspend cuttings and also cake off the permeable formation, thus retarding the passage of fluid into the formation. Typical micro or macro sized loss circulation materials (LCM) show limited success, especially in formations dominated by micropores, due to their relatively large sizes. In the current work, a new class of nanoparticle (NP) loss circulation materials has been developed. Two different approaches of NP formation and addition to oil-based drilling fluid have been tested. All NPs were prepared in-house either within the oil-based drilling fluid (in-situ), or within an aqueous phase (ex-situ), which was eventually blended with the drilling fluid. Under low pressure low temperature API standard test, more than 70% reduction in fluid loss was achieved in the presence of NPs compared to only 9% reduction in the presence of typical LCMs. The filter cake developed during the NP-based drilling fluid filtration was thin, which implies high potential for reducing the differential pressure sticking problem and formation damage while drilling. Moreover, at the level of NPs added, there was no material impact on drilling fluid viscosity and the fluid maintained its stability for more than 6 weeks.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".