Machine learning-based models for predicting permeability impairment due to scale deposition
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Water injection is one of the robust techniques to maintain the reservoir pressure and produce trapped oil from oil reservoirs and improve an oil recovery factor. However, incompatibility between injected water and reservoir water causes an unflavored issue named “scale deposition.” Owing to the deposited scales, effective permeability of a reservoir reduced, and pore throats might be plugged. To determine formation damage owing to scale deposition during a water injection process, two well-known machine learning methods, least squares support vector machine (LSSVM) and artificial neural network (ANN), are employed in the present paper. To improve the performance of the LSSVM method, a metaheuristic optimization algorithm, genetic algorithm (GA), is used. The constructed LSSVM model is examined using real formation damage data samples experimentally measured, which was reported in the literature. According to the obtained outputs of the above models, LSSVM has a high performance based on the correlation coefficient, and infinitesimal uncertainty based on a relative error between the model predictions and the corresponding actual data samples was less than 15%. Outcomes from this study indicate the useful application of the LSSVM approach in the prediction of permeability reduction due to scale deposition, and it can lead to a better and more reliable understanding of formation damage effects through water flooding without expensive laboratory measurements.
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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 | 0.000 | 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 it