What is the Magic of Water in Producing Sand?
Bibliographic record
Abstract
Abstract We performed a comprehensive sand prediction study of several deep, HPHT wells within a large reservoir and the findings revealed that, for these wells, common criteria based on critical drawdown, minimum bottomhole pressure, depletion or fluid velocity failed to predict the outcome by a relatively large margin. All these wells were subjected to relatively high levels of drawdown and also very high fluid velocities and, with the exception of one well, none showed any sanding until water production was encountered. In this paper, we provide a rationale for why water can be highly effective in inducing sand production and we support our argument using advanced numerical modeling. This exercise also ranks the performance of some of the common tools and theories that are conventionally used for sand prediction. We also provide reasons why some of these models do not perform satisfactorily for the cases studied. The originality of the work is in demonstrating that prior to sand production, the dis-aggregated rock (i.e., individual sand particles) around the wellbore is basically held together by the capillary tension which is destroyed by water flow. While the capillary tension appears to be insignificant (as it is in the order of 1 psi or so), it provides a significant resistance against sand mobilization. The importance of this issue has been quantified using advanced numerical modeling. This concept is vastly different from the previous theories that propose water weakens the rock through chemical interaction or changing the relative permeability.
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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".