Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244077 
Year of Publication: 
2020
Citation: 
[Journal:] Energy Reports [ISSN:] 2352-4847 [Volume:] 6 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2020 [Pages:] 795-801
Publisher: 
Elsevier, Amsterdam
Abstract: 
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 20-. 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.
Subjects: 
Oblique-incidence reflectivity difference method
Deep learning method
Shale
Anisotropy
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
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