How Can Sand Production Yield a Several-Fold Increase in Productivity: Experimental and Field Data
Bibliographic record
Abstract
Abstract Centrifuge physical model tests were performed to study the mode of failure during sand production and its concomitant impact on the productivity index. The tests simulated seepage-induced failure around a vertical well. Results indicate that in the presence of a competent cap rock (1) sand production results in the formation of a cone-shaped enlarged cavity; (2) surface subsidence of the reservoir due to loss of sand mass which may result in opening of flow channels under the cap rock; (3) for a given wellbore pressure, sand production ceases once the enlarged cavity lowers the flowrate to sub-critical level; (4) flow becomes diverted towards the upper perfs where the cavity radius is largest; (5) flow rate increase varies between 5 to 50 times depending on whether the mode and volume of sanding is sufficient to result in the formation of flow channels. The study performed shows that (1) the location of perfs affects the mode and magnitude of sand production and the concomitant productivity, and (2) long-term productivity can be improved through managed sand production. Presence of a competent cap rock is the key for maximizing the productivity via sanding. These findings are consistent with some field cases where extraordinary increases in production were noted as a result of sanding. Sand production, if properly managed, can reduce the completion costs (e.g., by omitting or delaying installation of sand exclusion measures) and improve the long term productivity by removing the skin damage and also through creating voids and zones of higher porosity around the well and under the caprock.
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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 | 0.002 |
| 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 | 0.001 |
| 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".