A Study of Relevant Parameters To Predict Sand Production in Gas Wells
Bibliographic record
Abstract
Summary A sensitivity analysis was carried out during a course of study to develop a model for predicting sand production from Gulf Coast gas wells that produce free water. A multiple linear-regression analysis was used to incorporate data from producing gas wells and log-derived properties of reservoir rock in a useable model. This model fits the field data of water-producing gas wells. It will be a risky proposition to analyze each individual parameter related to sand production when designing sand-control measures. This study shows that the combined effects of these parameters are making a significant contribution in the process. The results indicate that the volume of water is not relatable, but the presence of free water (uncondensed from the gas phase) increases the sanding tendencies of most gas wells. As a reservoir depletes, its tendency to produce sand increases; efforts to reduce sanding should be directed toward reducing the drawdown across the completion; and many of the log-derived parameters that show merit in correlating the sanding tendencies of dry gas wells have no value in waterproducing gas wells.
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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".