Sand-Production Prediction: A New Set of Criteria for Modeling Based on Large-Scale Transient Experiments and Numerical Investigation
Bibliographic record
Abstract
Summary This paper introduces a predictive tool that forecasts the drawdown associated with the onset of sanding as well as it predicts the sanding rate in real time. Experimental data on hollow cylinder samples (HCS) are used to support the validity of the numerical model. Experiments on hollow-cylinder synthetic-sandstone specimens were conducted, involving real-time sand-production measurement under various conditions. A numerical approach was used for simulating the experimental results. The material behavior was simulated using an elastoplastic stress-strain relationship. The model simulated the interaction between fluid flow and mechanical deformation of the medium in predicting sand production. The model simulated strain softening of the material accompanied with shear-bands formation as well as tensile failure. In the post-disaggregation phase, additional features were considered, including allowing for the removal of the disaggregated elements that have satisfied the sanding criteria and, consequently, making the necessary adjustments to the size and properties of the domain under consideration. The model can be used for time-dependent analysis of wellbore stability as it undergoes disaggregation and sand production induced by depletion, drawdown, and water cut. Such numerical tools can be used in designing the completion by identifying the critical operational conditions associated with severe sanding over the lifetime of the wellbore. The model showed a reasonable agreement with experimental results in terms of rock deformation and sanding rate. Further validation of the model against experimental and field data is necessary for its potential field applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".