Effect of Volumetric Failure on Sand Production in Oil-Wellbores
Bibliographic record
Abstract
Abstract Sand production is a major problem being faced by operating oil companies for many years. Sand problems are known to have increased production costs considerably. To-date, the roles of shear and tensile failure modes have been well thought out in studying sand production, but the contribution of volumetric failure mechanism in this phenomenon has been ignored. Even though volumetric failure mechanism has been well studied in looking into the phenomenon of land subsidence, but its role in sand production has been neglected in the past. In this paper, the contribution of this mechanism in sand production is addressed. In numerical studies, a finite difference based program was used. This program captures the fluid/solid interaction with complete rigor. In the experimental part, uniaxial strain condition was applied on some synthetic samples to examine volumetric failure mechanism, while traditional triaxial tests were performed on the same type of samples to learn about shear characteristic of the material. The mechanical parameters obtained from these tests were used in the numerical model that covers all tensile, shear and volumetric failure mechanisms. By using numerical and experimental means, it was discovered that volumetric failure mode, i.e. pore collapse, has a significant contribution in sanding. Initially at the well face, only shear and tensile failure may take place, but pore collapse can be triggered in a zone very close to the cavity hole. Subsequent to production of shear or tensile failed material from the open face, pore collapse induced disintegrated material may be exposed to cavity face, and potentially massive sanding may take place.
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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 | 0.001 | 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".