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Alain P. Tchameni,Lin Zhao,Joseph X. F. Ribeiro,Ting Li 한국자원공학회 2019 Geosystem engineering Vol.22 No.2
Oil-based drilling muds have the greatest preference for drilling operations. However, utilization of environmentally friendly components in drilling mud is fast becoming a requirement prompting production of different types of drilling mud. While there is abundance of prediction models for the rheological properties of oil-based drilling mud, there is scarcity of the same for drilling mud with environmentally friendly additives. In this work, an artificial neural network (ANN) and a multiple nonlinear regression (MNLR) model were developed aimed at predicting the apparent viscosity, plastic viscosity and yield point of waste vegetable oil biodiesel-modified water-based mud. The mean squared errors and correlation coefficient were the key parameters to evaluate and compare the performance of both models. The results indicate that prediction of the ANN perfectly matched the experimental values better than those of MNLR, reflecting its superior performance.