Evolving a SVM-based grey wolf concept model to predict yield point and plastic viscosity of water-based drilling fluids

Document Type : Original Article

Authors

1 Department of Chemical and Petroleum Engineering, Fouman Faculty of Engineering, University of Tehran, P.O.Box 43581-39115, Fouman, Iran

2 Politecnico di Torino, Department of Environment, Land and Infrastructure Engineering (DIATI), Turin, 10129, Italy

3 Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran

4 Department of Petroleum Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran

10.22091/jemsc.2026.15084.1339

Abstract

Application of soft computing (SC) proves instrumental in optimizing drilling costs through pre-estimations of incidents. This study presents a novel approach to develop a robust model for estimating the yield point and plastic viscosity of water-based drilling fluids. Leveraging the grey wolf optimization (GWO) algorithm, the support vector machine (SVM) framework is fine-tuned to analyze data and discern correlations among mud density, molecular weight, solid percent, Funnel viscosity, and temperature with SVM outputs. The resulting model exhibits the coefficients of determination of 0.990 & 0.938 and a root-mean-square errors (RMSE) of 1.5821 & 1.2261 for yield point and plastic viscosity data, affirming its efficiency and effectiveness. Pioneering the application of sensitivity analyses in this context, Marsh funnel viscosity and molecular weight emerge as key factors for yield point and plastic viscosity, boasting a relevancy factors of 74% and 47%, respectively. Comparative assessments reveal the superior accuracy of the proposed model over existing machine learning frameworks in the literature. The study enhances drilling methods by demonstrating the importance of sensitivity analysis for better predictions.

Keywords

Main Subjects

References

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