Journal
ENERGY REPORTS
Volume 6, Issue -, Pages 795-801Publisher
ELSEVIER
DOI: 10.1016/j.egyr.2020.04.004
Keywords
Oblique-incidence reflectivity difference method; Deep learning method; Shale; Anisotropy
Categories
Funding
- National Nature Science Foundation of China [11804392]
- Science Foundation of China University of Petroleum, Beijing, China [2462017YJRC029, ZX20190163, 2462018BJC005]
- University-Industry Collaborative Education Program [201901066031]
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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 layered 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 2060 mu m. 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. (C) 2020 The Authors. Published by Elsevier Ltd.
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