Research on artificial intelligence analysis technology for microscopic characteristics of shale gas reservoir

Xin Jin; Yunzhe Hou; Heng Huang; Chaoen Xiao

doi:10.1117/12.2606336

28 October 2021 Research on artificial intelligence analysis technology for microscopic characteristics of shale gas reservoir

Xin Jin, Yunzhe Hou, Heng Huang, Chaoen Xiao

Author Affiliations +

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841K (2021) https://doi.org/10.1117/12.2606336
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

The analysis of microscopic pore characteristics of geological reservoirs is the key content of unconventional oil and gas exploration. Currently, there are still some problems in the known analysis methods, such as small analysis area, poor analysis accuracy and weak quantitative analysis of human factors. In order to solve the problems in the current exploration methods, this research adopts the artificial intellience image analysis method, uses the image processing and computer vision technology, proposes the hole contour fitting extraction algorithm, the pore aggregation degree discrimination algorithm and the convex hull calculation algorithm to analyze the image. A large number of scanning electron microscope images of shale gas reservoirs were used for experiments. The final experimental results show that, compared with the known qualitative methods of micropore characteristics, this method reduces the intervention of human factors, improves the efficiency of analysis, and can obtain more accurate analysis results under the premise of obtaining correct results. Keywords: Image processing, shale matrix, pore structure analysis, cracks.

Citation Download Citation

Xin Jin, Yunzhe Hou, Heng Huang, and Chaoen Xiao "Research on artificial intelligence analysis technology for microscopic characteristics of shale gas reservoir", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841K (28 October 2021); https://doi.org/10.1117/12.2606336

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KEYWORDS

Image processing

Electron microscopes

Artificial intelligence

Image analysis

Throat

Defect detection

Scanning electron microscopy

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