论文成果

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

摘要: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. ? 2023 57th US Rock Mechanics/Geomechanics Symposium. All Rights Reserved.

ISSN号:1

卷、期、页:无

发表日期:2023-06-25

期刊分区(SCI为中科院分区):无

收录情况:EI(工程索引)

发表期刊名称:57th US Rock Mechanics/Geomechanics Symposium

参与作者:彭成勇

通讯作者:吴俊涛,林海,刘海龙

第一作者:刘伟

论文类型:会议论文

论文概要:吴俊涛,刘伟,林海,刘海龙,彭成勇,Application of Artificial Intelligence in Prediction of Wellbore Stability using Well Logging and Drilling Data,57th US Rock Mechanics/Geomechanics Symposium,2023,无

论文题目:Application of Artificial Intelligence in Prediction of Wellbore Stability using Well Logging and Drilling Data