Nanoparticle‐stabilised invert emulsion drilling fluids for deep‐hole drilling of oil and gas
Bibliographic record
Abstract
Abstract Invert emulsions are used to drill for oil and gas when good wellbore stability and high temperature tolerance are required. These drilling fluids contain a solid phase and two immiscible liquid phases stabilised with a polymeric surfactant. In ultra deep drilling, due to high temperature, the surfactant degrades causing phase separation. However, fine particles can be used as stabilisers, and the result is a Pickering emulsion. Here, we demonstrate that the use of a combination of hydrophobic nanoparticles and organically modified nanoclay results in stable water‐in‐oil invert emulsions model drilling fluids. These gel‐like model fluids have the desired plastic viscosity and yield stress suitable for drilling fluid applications that can be modified by adjusting the nanoparticle‐content. Aging experiments at 225°C showed that they also have high‐temperature stability for demanding drilling operations.
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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".