An Experimental Study of the Pore-Blocking Mechanisms of Aphron Drilling Fluids Using Micromodels
Bibliographic record
Abstract
Abstract Drilling fluid containing colloidal gas aphron (CGA) microbubbles can bridge the pores of the reservoir rock in the near wellbore region and reduce the risks of lost circulation and formation damage. The advantage of using CGA systems is that the CGA's can be easily removed during the initial stages of production thereby reducing the costs associated with stimulation processes such as acidizing. Although there has been some work done on the flow of the microbubbles through porous media, little is known about the optimal conditions for blocking the pores. Specifically, the effect of the interaction between microbubbles and reservoir fluid on the microbubble stability and pore blocking ability has not been determined. This study is focused on determining the pressure at which the microbubbles invade the porous medium and the CGA/reservoir fluid interaction for pore blocking. The pore blocking ability of various CGA compositions and flow rates is also discussed. In order to determine the effect of reservoir fluid interaction, experiments were carried out by injecting the microbubbles into a micromodel cell saturated with various fluids. The effect of water, brine, mineral oil and crude oil on the microbubble stability and pore blocking ability are investigated.
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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 | 0.001 |
| 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".