A novel investigation based on the tree‐based machine learning methods on rheological behaviour of waxy crude oils
Bibliographic record
Abstract
Abstract Flow assurance issues concerning wax precipitation in crude oil transportation pipelines make it necessary to predict the flow behaviour at different operating conditions. This work attempted to model measured shear stress and viscosity of waxy crude oils using tree‐based machine learning methods and consider wax content, additives, and solvent concentration as input parameters of models. Amongst all implemented techniques, the Extra trees model performed as a potential estimator in waxy oils rheology studies. Two models were run using the final values collected during hyperparameter tuning for shear stress and viscosity. Results show a root mean squared error value of 14.15 and a coefficient of determination ( R 2 ) of 0.998. RMSE of shear stress training dataset was reduced to 9.50 from 35.46 by adjusting hyperparameters of the model. The assessed techniques encompass decision tree, extra trees, gradient boosting, light gradient boosting machine, linear regression, random forest, ridge regression, and XGBoost. Also, in this research, three rheological models of power law, Dekee and Casson models have been used to correlate apparent viscosity values, and it was concluded that Dekee and Casson models have shown an acceptable match with experimental data. Also, the rheological behaviour of three crude oils in the absence/presence of flow improvers was investigated, and it was concluded that ethylene‐vinyl acetate (EVA) copolymer has performance in changing the flow behaviour from non‐Newtonian to Newtonian even at temperatures below WAT. Moreover, the addition of small quantities of asphaltene solvents, such as toluene, can improve the viscosity of crude oil with high wax content.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".