Assessment Of Several Sand Prediction Models With Particular Reference To HPHT Wells
Bibliographic record
Abstract
Abstract HPHT wells represent one class of problems where the performance of current sand prediction models has not been properly evaluated. We applied three different sanding models to a cluster of six HPHT wells. In all these wells, no sanding was observed under considerable levels of drawdown, certainly far surpassing conditions required for sand failure. Moreover, under failed conditions, the wells were producing high rates of gas for very long periods. The approaches used included popular analytical shear failure and tensile failure based models which showed an unusually high level of conservatism in their prediction of sanding in high pressure wells. We provide a plausible explanation for this behavior which is attributed to the underlying proposition that sand production occurs when sand fails. While this supposition is used in all applications of such models, in deep, high stress and pressure systems, the problem is magnified due to the fact that failure occurs relatively early in the operating life of the reservoir. We propose an alternate approach where sand production criterion is extended to include not only sand failure but also adequate seepage forces to liquefy the sand and hence mobilize it. This approach is shown to better capture the observed response in the field. As an added bonus, the proposed approach quantifies the volume of sand rather than simply give indications of the onset of sand production. This information is helpful in developing the optimal production strategy throughout the life of field. In this paper, we discuss the pros and cons of the commonly used models for sand prediction and provide examples to validate the newly proposed concepts for quantifying sand production.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".