The Role of Geomechanics Behaviors on Wellbore Collapse and Sanding Prediction in Gas Reservoir
Bibliographic record
Abstract
Abstract Critical plastic strain and stress at a wellbore wall are identified and commonly utilized in sanding and wellbore collapsing pressure prediction. Extensive experimental testing results suggest that the critical pressures of wellbore collapsing and sanding onset calculated based on statically determined stresses, however, generate large margin when compared to the testing results. These existing models may suffer various shortcoming and an accurate prediction on sanding or wellbore collapsing pressure may require a model which reflects the fundamental behavior. An elasto-plastic model with non-associated plastic flow and stress or strain-dependent failure/sanding criteria are re-visited, simplified analytical solutions of stresses, strain and critical pressure, solution procedure for sanding by incorporating all these fundamental mechanical behavior are developed. A critical effective plastic strain model is utilized and CEPS subject to different external/internal loading are calculated and validated against hollow cylinder testing results. Analytical solutions with nonlinear and linear yielding criteria are applied. These simulating results are compared to those results of THC tests and a geomechanical model with consideration of stress-deformation rather than with stress only is recommended for wellbore collapse and sand predictions.
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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 | 0.000 | 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".