Prediction of Rheological Properties of Drilling Fluids Using Two Artificial Intelligence Methods: General Regression Neural Network and Fuzzy Logic

Document Type : Research Paper

Authors

1 Birjand University of Technology, Birjand, Iran.

2 Department of Petroleum Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran. Stone Research Center, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran

Abstract

The rheological properties of drilling fluids, including viscosity and yield point, are essential for the effectiveness of drilling operations. Inaccurate predictions of these parameters may lead to costly complications during the drilling operation. Among artificial intelligence (AI) methods, the general regression neural network (GRNN) approach and the fuzzy logic method possess a high speed of estimation and fewer adjustable parameters than other methods. Despite the excellent capability of these two methods, they have seldom been used to predict the rheological properties of drilling fluids. Hence, through programming in MATLAB software, the capabilities of these methods in predicting the rheological properties of drilling fluids were investigated by comparison of their predictions against experimental results. The neural network contained one input layer with three inputs (clay mass, Na2Co3 concentration, and Gum Arabic concentration), one hidden layer with 38 neurons, and one output layer with three outputs (apparent viscosity (AV), plastic viscosity (PV), and yield point (YP)). In the fuzzy logic method, the optimal value of the clustering radius was considered to be 0.1 in this research. Based on the two methods designed, the value of R (about 0.99) and RMSE (about 0.5) between predicted values and the measured values of rheological properties in training and testing data were excellent. Our findings indicate that both AI methods can be utilized to predict the rheological parameters of drilling fluids with different compositions.

Keywords

Main Subjects

References

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Journal of Chemical and Petroleum Engineering
Volume 59, Issue 1
June 2025
Pages 127-139

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History

  • Receive Date: 19 August 2024
  • Revise Date: 04 December 2024
  • Accept Date: 05 December 2024

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