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Construction of complex digital rock physics based on full convolution network

Petroleum Science 2022年第2期19卷 651-662页

作者： Jia Kang Nian-Yin Li Li-Qiang Zhao Gang Xiong Dao-Cheng Wang Ying Xiong Zhi-Feng Luo State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Engineering Southwest Petroleum UniversityChengdu610500SichuanChina Research Institute of Natural Gas Technology Southwest Oil and Gas Field CompanyPetroChinaChengdu610213SichuanChina

Digital rock physics(DRP)is a paramount technology to improve the economic benefits of oil and gas fields,devise more scientific oil and gas field development plans,and create digital oil and gas fields.Currently,a significant gap is present between DRP theory and practical applications.Conventional digital-core construction focuses only on simple cores,and the recognition and segmentation effect of fractures and pores of complex cores is poor.The identification of rock minerals is inaccurate,which leads to the difference between the digital and actual cores.To promote the application of DRP in developing oil and gas fields,based on the high-precision X-ray computed tomography scanning technology,the U-Net deep learning model of the full convolution neural network is used to segment the pores,fractures,and matrix from the complex rock core with natural fractures innovatively.Simultaneously,the distribution of rock minerals is divided,and the distribution of rock conditions is corrected by X-ray diffraction.A pore—fracture network model is established based on the equivalent radius,which lays the foundation for fluid seepage simulation.Finally,the accuracy of the established a digital core is verified by the porosity measured via nuclear magnetic resonance technology,which is of great significance to the development and application of DRP in oil and gas fields.

关键词： Digital rock physics depth learning U-Net Complex core Complex fracture

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Automatic Detection of Weld Defects in Pressure Vessel X-Ray Image Based on CNN

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Wuhan University Journal of Natural Sciences 2022年第6期27卷 489-498页

作者： XIAO Wenkai FENG Xiang NAN Shuiyu ZHANG Linlin Department of Computer Science and Engineering.East China University of Science and Technology Shanghai 200237China Shanghai Engineering Research Center of Smart Energy Shanghai 201103China Shanghai Aino Industrial Technology CO..LTD.Shanghai 201612 China

The visual automatic detection method based on artificial intelligence has attracted more and more attention. In order to improve the performance of weld nondestructive defect detection,we propose DRepDet(Dilated RepPoints Detector). First, we analyze the weld defect dataset in detail and summarize the distribution characteristics of weld defect data, that is, the defect scale is very different and the aspect ratio distribution range is large. Second, according to the distribution characteristics of defect data, we design DResBlock module, and introduce dilated convolution with different dilated rates in the process of feature extraction to expand the receptive field and improve the detection performance of large-scale defects. Based on DResBlock and anchor-free detection framework RepPoints, we design DRepDet. Extensive experiments show that our proposed detector can detect 7 types of defects. When using combined dilated rate convolution network in detection, the AP50 and Recall50 of big defects are improved by 3.1% and 3.3% respectively, while the performance of small defects is not affected, almost the same or slightly improved. The final performance of the whole network is improved a large margin,with 6% AP50 and 4.2% Recall50 compared with Cascade RCNN and 1.4% AP50 and 2.9% Recall50 compared with RepPoints.

关键词： nondestructive testing depth learning weld defect detection convolutional neural networks dilated convolution

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