Intelligent identification method of drilling fluid rheological parameters based on machine learning

Authors

LIU Changye, Faculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFollow
YANG Xianyu, Faculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFollow
CAI Jihua, Faculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFollow
WANG Ren, CNPC Engineering Technology R & D Co., Ltd., Beijing 102206, China
WANG Jianlong, CNPC Engineering Technology R & D Co., Ltd., Beijing 102206, China
DAI Fanfei, CNPC Engineering Technology R & D Co., Ltd., Beijing 102206, China
GUO Wanyang, Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
JIANG Guoshe, Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
FENG Yang, Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

Abstract

The rheology of drilling fluid, which characterizes its flow and deformation, is vital for transporting and suspending rock cuttings as well as for enhancing the drilling rate. Precise control of drilling fluid rheological parameters is essential to ensure borehole cleanliness and efficient drilling. This paper proposes an intelligent identification method for drilling fluid rheological parameters based on Convolutional Neural Networks (CNNs). The method employs magnetic stirring to generate stable images of drilling fluid flow, uses various data augmentation methods to increase the number of images and create a database, thereby enhancing the model’s robustness and generalization capabilities. The AlexNet CNN algorithm is optimized to construct a model for identifying the rheological parameters of drilling fluids. The database is divided into a training set, validation set, and test set in a 7:2:1 ratio. Additionally, the model is evaluated through multiple approaches, including the confusion matrix, convolution kernel visualization technique, and Gradient-weighted Class Activation Mapping (Grad-CAM). The results indicate that: (1) The model achieves a macro precision of 95.2%, macro recall of 94.7%, and a macro F1 score of 0.95 for the plastic viscosity test of drilling fluids. (2) For the test of the apparent viscosity of drilling fluids, it achieves a macro precision of 91.6%, macro recall of 91.5%, and a macro F1 score of 0.91. (3) The utilization of convolution kernel visualization and Grad-CAM for feature extraction visualization reveals that the shape and size of drilling fluid ripples influence the accuracy of rheological parameter identification; (4) Indoor testing results demonstrate that the model has a test error of ±2 mPa·s within the allowable design range, indicating high prediction precision and stability. The proposed real-time intelligent identification method for drilling fluid rheological parameters can provide an intelligent technical approach for the safe, rapid, and accurate testing of drilling fluid rheology.

Keywords

drilling fluid, rheological parameter, machine learning, convolutional neural network (CNN), intelligent identification

DOI

10.12363/issn.1001-1986.24.01.0055

Recommended Citation

LIU Changye, YANG Xianyu, CAI Jihua, et al. (2024) "Intelligent identification method of drilling fluid rheological parameters based on machine learning," Coal Geology & Exploration: Vol. 52: Iss. 5, Article 19.
DOI: 10.12363/issn.1001-1986.24.01.0055
Available at: https://cge.researchcommons.org/journal/vol52/iss5/19

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