Adopting Aphron Fluid Technology for Completion and Workover Applications
Bibliographic record
Abstract
Abstract The literature describes several applications where Aphron fluid technology has been applied in both drilling and re-entry scenarios and includes an extensive description of how this fluid system works. A highly efficient leak-off prevention mechanism makes aphron based fluid systems beneficial for certain completion and workover applications as well, where formation damage could be avoided by the practical elimination of fluid-fluid or fluid-rock interaction or where simply the workover objectives can be achieved by obtaining efficient circulation of fluid to surface. Completion and workover applications for this fluid system have not been extensively reported. This paper reviews three applications of Aphron fluid technology in different completion and workover scenarios. The selected cases were reviewed to present some of the technical and operational lessons learned and to some extent discuss the observed formation cleanup behavior. The following three applications were reviewed: completion of a dual string sour gas well, using an oil based aphron system for kill fluid, with practically no kill fluid loss to a hydraulically fractured formation; the completion of additional zones within a depleted dolomitic limestone formation on two wells where the method of Aphron fluid placement was found to significantly affect fluid losses; and finally, the enabling of the provision of annular pressure support at pressures which approached the hydraulic fracture opening pressure of a shallow zone while hydraulically fracturing a deeper zone through tubing with a packer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".