Lithologic Reservoirs ›› 2021, Vol. 33 ›› Issue (4): 111-120.doi: 10.12108/yxyqc.20210412

• EXPLORATION TECHNOLOGY • Previous Articles     Next Articles

Shear wave velocity prediction based on one-dimensional convolutional neural network

MA Qiaoyu1, ZHANG Xin2, ZHANG Chunlei3, ZHOU Heng1, WU Zhongyuan1   

  1. 1. School of Science, China University of Geosciences (Beijing), Beijing 100083, China;
    2 School of Statistics, Beijing Normal University, Beijing 100875, China;
    3. Beijing Zhongdirunde Petroleum Technology Co., Ltd., Beijing 100083, China
  • Received:2020-10-23 Revised:2020-12-25 Online:2021-08-01 Published:2021-08-06

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Abstract: Shear and compressional waves is extremely important information for identifying reservoir lithology, physical properties and fluids in geophysical exploration. Due to the limitation of acquisition technology and cost investment,shear wave data is usually lacking,and shear wave prediction has become an urgent need for petrophysical analysis. Traditionally,shear wave conversion methods based on theoretical models and empirical formulas have great limitations. Conventional machine learning models which is point-to-point cannot effectively express the spatial characteristics of logging parameters,and the inherent relationship between shear wave parameters and other logging parameters is not sufficiently represented. To this end,a study on shear wave velocity prediction methods based on one-dimensional convolutional neural network（1D-CNN）models was carried out. A deep learning regression model was established based on 16 logging parameters such as acoustic time difference,density,natural gamma and resistivity. The structural characteristics of logging parameters in the logging depth space was extracted by different scale convolution and a multi-layer network structure learned the relationship between shear wave parameters and logging parameters depth characteristics,thereby establishing a more accurate prediction model. Through the practical application in the Upper Paleozoic clastic reservoir of the Sulige gas field,it is verified that the one-dimensional convolutional neural network model has higher shear wave prediction accuracy.

Key words: wave velocity, logging parameters, clastic reservoir, 1D-CNN, deep learning, Sulige gas field

CLC Number: 

  • P631.4
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