A Comprehensive Approach to Modeling Sanding During Oil Production
Bibliographic record
Abstract
Abstract Sand production has been a major dilemma facing operating oil companies over many years, sometimes substantially increasing production costs. On the other hand, a very controlled solid production can enhance oil production. A dependable predictive model is vital for planning production strategies in order to optimize well production. To date, despite several research studies, sand production remains the nightmare of petroleum engineers. Even though many researchers have tried to predict sand production in the past, none of them suggested a comprehensive model that takes care of a variety of mechanisms at different points and different times. Moreover, rare models predict sanding rate and volume along with sanding initiation. This paper presents a comprehensive numerical modeling of sand production that appreciates the different behavior of the medium near and far well-bore from early to late life-time. Sanding criteria were adopted according to the physics of the problem, by taking the sequential nature of sand production into consideration. The numerical model that was used not only assesses sand production qualitatively but can also give the sanding rate at different times. This was used to model the observations of sand production in a large block test, and the sanding rate and volume generated from numerical modeling agreed with experimental results. With each stage of increased drawdown or depletion, a burst of sand took place which enlarged the cavities initiated from the perforations. The expansion of the cavity was soon stabilized and this behavior was predicted by numerical modeling. Moreover, besides considering shear and tensile failure of the material, the possibility of volumetric failure has been discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".