Computation of Sand Production in Water Injectors
Bibliographic record
Abstract
Summary A significant proportion of future oil production is expected to be driven by water injectors in reservoirs that are sand prone. Achieving sweep efficiency and sand control in such formations is challenging. In many cases, the ideal sand control is no sand control [e.g., a cased-and-perforated (C&P) completion] that requires rigorous sanding assessment. Sand production in injectors often goes unnoticed until it is too late (sand covering the pay), making it difficult to ascertain the specific set of conditions resulting in sanding and the severity of the individual sanding episodes. On the basis of physics and mechanisms governing sanding, general non-quantitative factors can be postulated on the causes of sanding. To provide a deeper insight into this matter, a numerical study has been undertaken to model sanding in injectors, accounting for several intercoupled factors, including, among others, injection pressure, crossflow, water hammer (WH) pressure pulses, and degradation of the formation matrix resulting from repeated shutdowns. This paper describes the concepts used for sand-production modeling and shows application of the model to a field problem involving a C&P completion in a sand-prone reservoir. The results show that the mode and magnitude of sanding are influenced by the rock properties, injection operations, and the equipment type and installation. The cases analyzed indicate a correspondence between the rate of shut-in and the onset of sanding. In cases involving unconsolidated sands, the WH effects have a pronounced impact on sanding. Sand control can be omitted in even extremely weak rocks if the injection pressure is optimized, frequency of hard shutdowns is controlled, and hardware is positioned in a manner that reduces the WH-pressure-pulse magnitude. The proposed modeling can be used when determining the sand-sump capacity required over the projected life of the well.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".