Intelligent identification and segmentation method of wellbore fractures in resistivity logging imaging map
XIA Wenhe1, ZHU Zhehao1, HAN Yujiao2, YANG Yikai1, LIN Yongxue2, WU Xiongjun2
1. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, Sichuan 610500, China; 2. SINOPEC Petroleum Engineering Technology Research Institute, Beijing 102200, China
Abstract：In view of the huge workload, strong subjectivity in artificial identification, and poor stability in the fracture identification and processing of logging images, this paper introduces the computer vision technology and deep learning framework into the analysis and interpretation of logging images, builds an intelligent identification and segmentation network model of fracture morphology, and intelligently identifies wellbore fractures in resistivity logging images. First, the model extracts the shallow and deep features of wellbore images through multi-scale dilated convolution and attention mechanism, and multi-scale fusion of shallow and deep features is conducted to form new features with more representation ability. According to the new features, the two pixel classification is carried out to complete foreground and background type identification of each pixel in the logging images. Several pixels classified as foreground present the contour of the fractured area. The multi-scale feature fusion model can fully retain more contour details of the fracture image from the micro perspective, and the identification and classification accuracy of each fracture pixel reaches almost 80.0%. Finally, by drawing lessons from the evaluation system of human eye visual similarity, a performance evaluation algorithm is designed for intelligently identifying fracture contour from the macro perspective. The evaluation results show that when the visual similarity perception rating is grade II, 81.3% and 80.0% of identification results in the fracture region in the training set and test set images are basically consistent with the artificial identification results. The results indicate that the proposed method can replace artificial interpretation to complete fracture identification and marking, greatly reduce the image analysis workload and carefully outline the fracture contour. Meanwhile, it is conducive to the rapid and timely judgment of wellbore and shaft stability, thus providing technical support for subsequent intelligent quantitative evaluation and calculation of fractured areas.
GUPTA D, SHAH M.A comprehensive study on artificial intelligence in oil and gas sector[J].Environmental Science and Pollution Research, 2022, 29(34):50984-50997.
[2]
匡立春, 刘合, 任义丽, 等.人工智能在石油勘探开发领域的应用现状与发展趋势[J].石油勘探与开发, 2021, 48(1):1-11.KUANG Lichun, LIU He, REN Yili, et al.Application and development trend of artificial intelligence in petroleum exploration and development[J].Petroleum Exploration and Development, 2021, 48(1):1-11.
[3]
KOROTEV D, TEKIC Z.Artificial intelligence in oil and gas upstream:trends, challenges, and scenarios for the future[J].Energy and AI, 2021, 3:100041.
[4]
D'ALMEIDA A L, BERGIANTE N C R, DE SOUZA FERREIRA G, et al.Digital transformation:a review on artificial intelligence techniques in drilling and production applications[J].The International Journal of Advanced Manufacturing Technology, 2022, 119(9):5553-5582.
[5]
SIRCAR A, YADAV K, RAYAVARAPU K, et al.Application of machine learning and artificial intelligence in oil and gas industry[J].Petroleum Research, 2021, 6(4):379-391.
[6]
CHERNIKOV A D, EREMIN N A, STOLYAROV V E, et al.Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells:problems and solutions[J].Georesursy, 2020, 22(3):87-96.
[7]
吴思源, 李守定, 陈冬, 等.大闭环伺服控制随钻智能导向钻井方法[J].地球物理学报, 2021, 64(11):4215-4226.WU Siyuan, LI Shouding, CHEN Dong, et al.An intelligent-while-drilling steering method of global closed-loop servo control[J].Chinese Journal of Geophysics, 2021, 64(11):4215-4226.
[8]
肖小玲, 靳秀菊, 张翔, 等.基于常规测井与电成像测井多信息融合的裂缝识别[J].石油地球物理勘探, 2015, 50(3):542-547.XIAO Xiaoling, JIN Xiuju, ZHANG Xiang, et al.Fracture identification based on information fusion of conventional logging and electrical imaging logging[J].Oil Geophysical Prospecting, 2015, 50(3):542-547.
[9]
李潮流, 周灿灿, 黄书先.FMI成像测井失真图像的修复及其地质应用[J].石油天然气学报, 2007, 29(1):88-91.LI Chaoliu, ZHOU Cancan, HUANG Shuxian.Inpainting of distorted FMI image and its geologic application[J].Journal of Oil and Gas Technology, 2007, 29(1):88-91.
[10]
闫建平, 蔡进功, 首祥云, 等.成像测井图像中的裂缝信息智能拾取方法[J].天然气工业, 2009, 29(3):51-53.YAN Jianping, CAI Jingong, SHOU Xiangyun, et al.Intelligent picking method of the fracture information from imaging logging[J].Natural Gas Industry, 2009, 29(3):51-53.
[11]
王熊, 张翔.成像测井图像纹理特征提取的统计方法研究[J].石油天然气学报, 2012, 34(4):83-87.WANG Xiong, ZHANG Xiang.The statistical methods for extracting texture features of borehole imaging logging diagram[J].Journal of Oil and Gas Technology, 2012, 34(4):83-87.
[12]
张群会, 师玉璞, 陈有兰.成像测井裂缝自动识别元胞自动机方法研究[J].西安科技大学学报, 2012, 32(6):772-775.ZHANG Qunhui, SHI Yupu, CHEN Youlan.Automatic identification of crack in imaging logging based on cellular automaton[J].Journal of Xi'an University of Science and Technology, 2012, 32(6):772-775.
[13]
TAIEBI F, AKBARIZADEH G, FARSHIDI E.Detection of reservoir fractures in imaging logs using directional filtering[C].2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 2017, 150-154.
[14]
宋锦祥.基于知识的碳酸盐岩电成像测井裂缝自动识别方法[J].测井技术, 2019, 43(6):606-611.SONG Jinxiang.Knowledge reasoning method for automatically identifying fractures in carbonate rock using electrical logging data[J].Well Logging Technology, 2019, 43(6):606-611.
[15]
魏伯阳, 潘保芝, 殷秋丽, 等.基于条件生成对抗网络的成像测井图像裂缝计算机识别[J].石油物探, 2020, 59(2):295-302.WEI Baiyang, PAN Baozhi, YIN Qiuli, et al.Identification of image logging data based on conditional generation adversarial network[J].Geophysical Prospecting for Petroleum, 2020, 59(2):295-302.
[16]
FERNÁNDEZ-IBÁÑEZ F, DEGRAFF J M, IBRAYEV F.Integrating borehole image logs with core:a method to enhance subsurface fracture characterization[J].AAPG Bulletin, 2018, 102(6):1067-1090.
[17]
柯式镇, 许淑霞.井壁电成像测井资料定量评价方法研究[J].天然气工业, 2006, 26(9):62-64.KE Shizhen, XU Shuxia.A method study on quantitatively evaluating sidewall imaging well logging data[J].Natural Gas Industry, 2006, 26(9):62-64.
[18]
柳杰, 殷小敏, 张彦伟, 等.新型油基泥浆测井电成像方法研究[J].地球物理学进展, 2015, 30(2):790-796.LIU Jie, YIN Xiaomin, ZHANG Yanwei, et al.A novel approach for borehole electrical imaging in oil-based mud[J].Progress in Geophysics, 2015, 30(2):790-796.
[19]
SONG W, JIA G, JIA D, et al.Automatic pavement crack detection and classification using multiscale feature attention network[J].IEEE Access, 2019, 7:171001-171012.
[20]
ZHANG W, WU T, LI Z, et al.Fracture recognition in ultrasonic logging images via unsupervised segmentation network[J].Earth Science Informatics, 2021, 14(2):955-964.
[21]
张浩, 司马立强, 王亮, 等.基于卷积神经网络的电成像图像空白条带填充方法[J].地球物理学进展, 2021, 36(5):2136-2142.ZHANG Hao, SIMA Liqiang, WANG Liang, et al.Blank strip filling method for resistivity imaging image based on convolution neural network[J].Progress in Geophysics, 2021, 36(5):2136-2142.
[22]
RONNEBERGER O, FISCHER P, BROX T.U-Net:convolutional networks for biomedical image segmentation[C].Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, 2015, 234-241.
[23]
YU F, KOLTUN V.Multi-scale context aggregation by dilated convolutions[C].4th International Conference on Learning Representations, 2016, 1-13.
[24]
HOU Q, ZHOU D, FENG J.Coordinate attention for efficient mobile network design[C].2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, 13708-13717.
[25]
YU C, ZHAO X, ZHENG Q, et al.Hierarchical bilinear pooling for fine-grained visual recognition[C].Computer Vision-ECCV 2018, 2018, 595-610.
[26]
WANG X, MA S, GOLDFARB D, et al.Stochastic quasi-newton methods for nonconvex stochastic optima-zation[J].SIAM Journal on Optimization, 2017, 27(2):927-956.
[27]
CHEN L C, PAPANDREOU G, KOKKINOS I, et al.DeepLab:semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4):834-848.
[28]
HUANG H, XU H, WANG X, et al.Maximum F1-score discriminative training criterion for automatic mispronunciation detection[J].IEEE/ACM Transactions on Audio, Speech and Language Processing, 2015, 23(4):787-797.
[29]
王耀东, 朱力强, 史红梅, 等.基于局部图像纹理计算的隧道裂缝视觉检测技术[J].铁道学报, 2018, 40(2):82-90.WANG Yaodong, ZHU Liqiang, SHI Hongmei, et al.Vision detection of tunnel cracks based on local image texture calculation[J].Journal of the China Railway Society, 2018, 40(2):82-90.
ITU.Methodology for the subjective assessment of the quality of television pictures:Rec.ITU-R BT.500-11[S].ITU-R, Geneva, 2002.
[32]
褚江, 陈强, 杨曦晨.全参考图像质量评价综述[J].计算机应用研究, 2014, 31(1):13-22.CHU Jiang, CHEN Qiang, YANG Xichen.Review on full reference image quality assessment algorithms[J].Application Research of Computers, 2014, 31(1):13-22.
