Machine learning-based surrogate correlation for prediction of wax deposition rate in oil and gas pipelinesOA

This study developed a data-driven surrogate-box correlation to predict wax deposition rates in crudeoil pipelines and help address the limited generalizability of empirical and mechanistic models under variable flow and thermal conditions.Literature-derived dataset of 215 experimental and field observations was compiled with inputs as oil temperature T_(o),wall temperature T_(w),dynamic viscosity μ,wall shear stress σ,flow velocity v_(f),wall temperature gradient ΔT,and wall concentration gradient ΔC,and target as wax deposition rateδW(g·m^(-2)·h^(-1)).After outlier control,standardization,and ANOVA-based feature engineering,Five supervised black-box models;SVR,Random Forest,MLP,Gradient Boosting,and KNN,models were trained with an 80/20 train–test split,5-fold cross-validated,hyperparametertuned and evaluated using MSE,RMSE,MAE,R^(2),and AAPRE.The top performing model,KNN,was modeled into an interpretable surrogate via elastic net(benchmarked against polynomial Ridge degree-2/3)to yield a closed-form correlation.KNN achieved excellent test performance(R^(2)=0985,RMSE~0.101,AAPRE~5.09%),outperforming alternative models.The Elastic Net surrogate preserved predictive ability with balanced generalization(R^(2)≈0.61–0.65 for train/validation/test)while exposing parameter level effects and nonlinear interactions among temperature,viscosity,shear stress,velocity,and wall-scale gradients.When compared to a physics-based correlation,the surrogate exhibited tighter clustering to measurements,which indicates improved field relevance.The principal contribution of this study is a hybrid workflow that couples high-accuracy black-box learning with transparent surrogate modeling to help enable real-time monitoring and control,proactive pigging scheduling,and optimized chemical/thermal treatments of oil flow in pipelines.The resulting correlation offers a deployable,interpretable alternative to nontransparent machine learning models or assumption-heavy empirical relations,with clear value for flow-assurance planning and operational reliability.