Artificial Neural Network Model to Predict Filtrate Invasion of Nanoparticle-Based Drilling Fluids

Abstract

Mud filtrate invasion is a vital parameter that should be optimized during drilling for oil and gas to reduce formation damage. Nanoparticles (NPs) have shown promising filtrate loss mitigation when used as drilling fluid (mud) additives in numerous recent studies. Modeling the influence of NPs can fasten the process of selecting their optimum type, size, concentration, etc. to meet the drilling conditions. In this study, a model was developed, using artificial neural network (ANN), to predict the filtrate invasion of nano-based mud under wide range of pressures and temperatures up to 500 psi and 350 °F, respectively. A total of 2,863 data points were used in the development of the model (806 data points were collected form conducted experiments and the rest were collected form the literature). Seven different types of NPs with size and concentration ranges from 15 to 50 nm and 0 to 2.5 wt%, respectively, had been included in the model to ensure universality. The dataset was divided into 70 % for training and 30 % for validation. A total of 6,750 different combinations for the model’s hyperparameters were evaluated to determine the optimum combination. The N-encoded method was used to convert the categorical data into numerical values. The model was evaluated through calculating the statistical parameters. The developed ANN-model proofed to be efficient in predicting the filtrate invasion at different pressures and temperatures with an average absolute relative error (AARE) of less than 0.5 % and a coefficient of determination (R2) of more than 0.99 for the overall data. The ANN-model covers wide range of pressures, temperatures as well as various NPs’ types, concentrations, and sizes, which confirms its useability and coverability. HIGHLIGHTS Artificial neural network (ANN)-model was developed to predict the volume of filtrate of water-based mud (WBM) modified with nanoparticles (NPs) A total of 2,863 data points were collected to build the ANN-model from both experimental work and literature considering 3 types of WBM modified with 7 types of NPs (SiO2, TiO2, Al2O3, CuO, MgO, ZnO, Fe2O3) with size and concentration ranges from 15 to 50 nm and 0 to 2.5 wt%, respectively, under wide range of pressures and temperatures up to 500 psi and 350 °F A total of 6,750 different combinations for the model’s hyperparameters were evaluated to determine the optimum combination and the N-encoded method was used to convert the categorical data into numerical values The ANN-model proofed to be efficient with an average absolute relative error (AARE) of less than 0.5 % and a coefficient of determination (R2) of more than 0.99 for the overall data GRAPHICAL ABSTRACT