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

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Veröffentlicht in: Energy reports 6(2020) vom: Nov., Seite 795-801
Personen und Körperschaften: Chen, Ru (VerfasserIn), Ren, Zewei (VerfasserIn), Meng, Zhaohui (VerfasserIn), Zhan, Honglei (VerfasserIn), Miao, Xinyang (VerfasserIn), Zhao, Kun (VerfasserIn), Lu, Huibin (VerfasserIn), Jin, Kuijuan (VerfasserIn), Yue, Wenzheng (VerfasserIn), Yang, Guozhen (VerfasserIn)
Titel: 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 Lu, Kuijuan Jin, Wenzheng Yue, Guozhen Yang
Format: E-Book-Kapitel
Sprache: Englisch
veröffentlicht:
2020
Gesamtaufnahme: : Energy reports, 6(2020) vom: Nov., Seite 795-801
, volume:6
Schlagwörter:
Aufsatz in Zeitschrift
Quelle: Verbunddaten SWB
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Zusammenfassung: 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.
ISSN: 2352-4847
DOI: 10.1016/j.egyr.2020.04.004
Zugang: Open Access