Application of Artificial Intelligence in Prediction of Wellbore Stability Using Well Logging and Drilling Data

支持向量机 人工神经网络 理论(学习稳定性) 钻井液 井筒 钻探 非线性系统 计算机科学 石油工程 工程类 人工智能 机器学习 机械工程 物理 量子力学
作者
Juntao Wu,Wei Liu,Hai Lin,Hailong Liu,Chengyong Peng
出处
期刊:50th U.S. Rock Mechanics/Geomechanics Symposium
标识
DOI:10.56952/arma-2023-0598
摘要

ABSTRACT Wellbore instability is one of the most critical challenges during drilling, often manifested as wellbore collapse, shrinkage, falling rocks, and formation fracturing, which may result in complex problems such as pipe sticking, high torque, mud loss, thus impeding the drilling progress and increasing the cost of the drilling operation. Conventional wellbore stability prediction relies on some deterministic physical models, involving some empirical coefficients which are difficult to determine and often dependent on field experience. In addition, some complex factors, such as natural fractures, cannot be explicitly and quantitatively characterized in existing wellbore stability prediction models. Artificial intelligence technique has shown unique advantages in nonlinear issues. The artificial intelligence technique is used to predict wellbore stability in this study, including artificial neural networks (ANNs) and support vector machine (SVM). The logging data and drilling data were collected from the field. According to the correlation analysis between influencing factors and wellbore enlargement rate, 16 parameters were extracted, such as mud density, formation density porosity, acoustic interval transit time, weight on bit as the input data of the models, and wellbore enlargement rate as output. Both SVM and ANNs models have exceptional performance in predicting wellbore stability. When the kernel of the SVM model is Linear, predictions perform optimally. In the ANNs model prediction results, the result performs optimally when the total number of neurons is 1024 in the hidden layer. Overall, ANNs model performs better than SVM model with a coefficient of determination (R2) of 0.991, therefore it is recommended to apply ANNs to predict wellbore stability. The present analysis supplies knowledge that can be used to predict wellbore stability problems before drilling, optimize drilling parameters, and reduce drilling accidents and costs. INTRODUCTION Wellbore instability refer to a series of responses resulting from mechanical, chemical and other effects of the rock around the wellbore. Wellbore instability is one of the most critical challenges during drilling, often manifested as wellbore collapse, shrinkage, falling rocks, and formation fracturing, which may result in complex problems such as pipe sticking, high torque, mud loss, thus impeding the drilling progress and increasing the cost of the drilling operation.
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