Prediction of sand production onset in petroleum reservoirs using a reliable classification approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Controlling sand production in the petroleum industry has been a long-standing problem for more than 70 years. To provide technical support for sand control strategy, it is necessary to predict the conditions at which sanding occurs. To this end, for the first time, least square support machine (LSSVM) classification approach, as a novel technique, is applied to identify the conditions under which sand production occurs. The model presented in this communication takes into account different parameters that may play a role in sanding. The performance of proposed LSSVM model is examined using field data reported in open literature. It is shown that the developed model can accurately predict the sand production in a real field. The results of this study indicates that implementation of LSSVM modeling can effectively help completion designers to make an on time sand control plan with least deterioration of production.
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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 | 0.001 | 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 it