A Machine Learning Prediction of Oil-Brine Relative Permeability Utilizing Effective Characteristics of Reservoir Rocks and Fluids
Bibliographic record
Abstract
Summary In this study, an extensive database was collected by mining open literature of oil-water relative permeability experiments and related rock and fluid characterizations. Various supervised machine learning (ML) models, such as Linear, Support Vector Machine (SVM), Nearest Neighbors, Gaussian Process, and Tree-based models, were developed and evaluated using this dataset. To assess the performance of the machine learning models, an unsteady state (USS) relative permeability measurement test was designed and conducted. Notably, Tree-based models, including Decision Tree, Gradient boosting, XGBoost, and Random Forest, demonstrated accuracy exceeding 95% in predicting relative permeability values. Furthermore, the study validated the models through a comparison of the predicted curves with experimental relative permeability data obtained from the USS test. The high accuracy and reliability of the Tree-based models were affirmed, emphasizing their efficacy in predicting oil-water relative permeability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".