Prediction of Sand Production Rate in Oil and Gas Reservoirs
Bibliographic record
Abstract
Abstract Most sand production prediction models to date have the capability to indicate whether initial sand production may take place somewehere during the lifetime of a reservoir. However, these models are unable to predict whether the sand production will be ‘problematic’ (e.g. in terms of erosion, plugging, well sand-up, separator fill, etc.), in particular for systems that have some tolerance towards sand production. In order to predict whether sand production will be ‘problematic’, one needs to be able to estimate sand production volumes and rates as a function of, amongst others, bottom-hole load conditions, drawdown, and time. This paper presents a model for the prediction of sand production volumes and rates for any type of clastic oil or gas reservoir. This work builds on successful previous efforts to predict sand production rate in the Canadian heavy oil sands. The current model is a validation of the previous model and its further generalisation to sandstones of any strength (not just unconsolidated) with any reservoir fluid (not just heavy oil), and involves features such as failure of intact sand, post-failure cavity stabilisation, and non-associated viscoplasticity. The model has been calibrated to laboratory experiments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".