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risk pre-assessment method for regional drilling engineering based on deep learning and multi-source data

Petroleum Science 2023年第6期20卷 3654-3672页

作者： Yu-Qiang Xu Kuan Liu Bao-Lun He Tatiana Pinyaeva Bing-Shuo Li Yu-Cong Wang Jia-Jun Nie Lei Yang Fu-Xiang Li National Key Laboratory of Deep Oil and Gas China University of Petroleum(East China)Qingdao266580ShandongChina Shandong Ultra-Deep drilling Process Control Tech RD Center Qingdao266580ShandongChina

Accurately predicting downhole risk before drilling in new exploration areas is one of the *** intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a hot research topic and frontier in this ***,due to the small number and uneven distribution of drilled wells in new exploration areas and the lack of sample data related to risk,the training model has insufficient generalization ability,and thus the prediction is not *** this paper,a drilling risk profile(depth domain)rich in geological and engineering information is constructed by introducing a quantitative evaluation method for drilling risk of drilled wells,which can provide sufficient risk sample data for model training and thus solve the small sample *** the problem of uneven distribution of drilling wells in new exploration areas,the concept of virtual wells and their deployment methods were ***,two methods for calculating rock mechanical parameters of virtual wells were proposed,and the accuracy and applicability of the two methods are *** LSTM deep learning model was optimized to tap the quantitative relationship between drilling risk profiles and multi-source data(e.g.,seismic,logging,and rock mechanical parameters).The model was validated to have an average relative error of 9.19%.The quantitative prediction of the drilling risk profile of the virtual well was achieved using the trained LSTM model and the calculation of the relevant parameters of the virtual ***,based on the sequential Gaussian simulation method and the risk distribution of drilled and virtual wells,a regional 3D drilling risk model was *** analysis of real cases shows that the addition of virtual wells can significantly improve the identification of regional drilling risks and the prediction accuracy of pre-drill drilling risks in unexplored areas can be improved by up to 21%compared with the 3D risk model

关键词： pre-drill risk assessment risk samples Deep learning LSTM neural network 3D model

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