Machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records

作     者：Hui-Wen Pang Han-Qing Wang Yi-Tian Xiao Yan Jin Yun-Hu Lu Yong-Dong Fan Zhen Nie 

作者机构：National Key Laboratory of Petroleum Resources and EngineeringChina University of Petroleum(Beijing)Beijing102249China College of ScienceChina University of Petroleum-BeijingBeijing102249China Petroleum Exploration and Production Research InstituteSINOPECBeijing102206China College of Petroleum EngineeringChina University of Petroleum-BeijingBeijing102249China Research Institute of Petroleum Exploration and DevelopmentCNPCBeijing100083China 

出 版 物：《Petroleum Science》 (石油科学（英文版）)

年 卷 期：2024年第21卷第2期

页      面：1241-1256页

核心收录：

学科分类：0820[工学-石油与天然气工程] 12[管理学] 082001[工学-油气井工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the financially supported by the National Natural Science Foundation of China(Grant No.52104013) the China Postdoctoral Science Foundation(Grant No.2022T150724) 

主　　题：Lost circulation Risk prediction Machine learning Seismic attributes Mud loss records 

摘      要：Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network ***, the influence of the number of sub-Gausses and the uncertainty coefficient on the model s prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.