Oblique-incidence reflectivity difference method combined with deep learning for predicting anisotropy of invisible-bedding shale

Ru Chen, Zewei Ren, Zhaohui Meng, Honglei Zhan, Xinyang Miao, Kun Zhao, Huibin Lű, Kuijuan Jin, Shijie Hao, Wenzheng Yue, Guozhen Yang
2020 Energy Reports  
Deep learning methodologies have revolutionized prediction in many fields and is potential to do the same in the petroleum industry because of the complex oil-gas reservoir. A limitation remains for dense shale exploration in that the shales with invisible bedding are difficult to characterize measurably because of the considerable complexity of the geological structures. The oblique-incidence reflectivity difference method (OIRD) is sensitive to the surface features and was used to obtain a
more » ... ered distribution of dielectric properties in shales. In this paper, we report a combination of OIRD and deep learning method to identify the dielectric anisotropy of an invisible-bedding shale. The model performs well and clearly identifies the bedding of the shale based on the output values associated with the probability. Only a single direction was determined to have laminations with widths of 20-60 µm. The anisotropy features detected by OIRD also existed in the invisible-bedding shale and were caused by the smaller cracks and denser particles' orientation relative to general shales. As current dense reservoirs include rich invisible-bedding shales, we believe that the OIRD method combined with deep learning method can help improve the exploration efficiency of shale reservoirs.
doi:10.1016/j.egyr.2020.04.004 fatcat:ngtwryxqovde3l6me3275m5cja