Regression-Based Machine Learning Approach for Predicting Viscosity and Gel-Strength of Drilling Fluids through Analysis of Drilling Mud Formulation Parameters

The drilling process for onshore and offshore oil and gas wells requires drilling fluid, often known as drilling mud. Drilling fluid serves several major drilling functions, including cooling and lubricating the drill bit, carrying rock cuttings to the surface, and maintaining the formation pressure in the wellbore to prevent blowouts. The parameters of the drilling fluid, such as its viscosity, density, and pH, must be strictly controlled and modified during drilling operations. In the oil and gas sector, the use of machine learning in drilling fluid is becoming more and more crucial. Machine learning can be used to evaluate vast amounts of data from drilling fluid formulation operations, including data collected on the properties of the drilling fluid, such as its viscosity and rheological properties, as well as data on the geology of the wellbore. By using this data, regression machine learning algorithms can predict the behavior of drilling fluid under different conditions and formulations, optimize the characteristics of the fluid, and expected to improve drilling performance, save cost and time.