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

被引：0
|
作者：
Liu C. [1 ]
Yang X. [1 ]
Cai J. [1 ]
Wang R. [2 ]
Wang J. [2 ]
Dai F. [1 ]
Guo W. [1 ]
Jiang G. [1 ]
Feng Y. [1 ]
机构：
[1] Faculty of Engineering, China University of Geosciences, Wuhan
[2] CNPC Engineering Technology R & D Co., Ltd., Beijing
来源：
Meitiandizhi Yu Kantan/Coal Geology and Exploration | 2024年 / 52卷 / 05期
关键词：
convolutional neural network (CNN); drilling fluid; intelligent identification; machine learning; rheological parameter;
D O I：
10.12363/issn.1001-1986.24.01.0055
中图分类号：
学科分类号：
摘要：
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. © 2024 Science Press. All rights reserved.
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页码：183 / 192
页数：9
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