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

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外文摘要：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 pro-duction 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,16 seismic 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 model.Then,the influence of the number of sub-Gausses and the uncertainty coefficient on the model's pre-diction 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 R2 is 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.

外文关键词：

Lost circulationRisk predictionMachine learningSeismic attributesMud loss records

作者：

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

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作者单位：

National Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China

College of Science,China University of Petroleum-Beijing,Beijing,102249,China

Petroleum Exploration and Production Research Institute,SINOPEC,Beijing 102206,China

National Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing 102249,China

College of Petroleum Engineering,China University of Petroleum(Beijing),Beijing 102249,China

Research Institute of Petroleum Exploration and Development,CNPC,Beijing 100083,China

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基金：

国家自然科学基金中国博士后科学基金

项目编号：

521040132022T150724

出版年：

2024

DOI：

10.1016/j.petsci.2023.10.024

石油科学(英文版)

中国石油大学（北京）

石油科学(英文版)

影响因子：0.88

ISSN：1672-5107

年,卷(期)：2024.21(2)