Self-facies-control Pre-stack Inversion Technology for Turbidite Sandstone Reservoir with Complex Fault System

doi:10.11885/j.issn.1674-5086.2021.01.29.01

Abstract

Abstract: Turbidite sandstone reservoir is a typical gravity flow deposit, which is characterized by lateral variation, vertical multi-stage superposition and frequent migration. Seismic inversion is one of the main methods for fine reservoir description, but the complex fault system, lateral abrupt variation and overlimit thickness of the reservoir in E Oilfield restrict the accuracy of reservoir inversion and its subsequent application. In order to solve the problem of reservoir prediction in E Oilfield, a self-facies-control pre-stack inversion technology with complex fault system is proposed in this paper. Firstly, the deep learning algorithm based on the fault contact relationship chart library is used to construct the complex fault system model, and then the high-precision seismic stratigraphic framework is constructed. Secondly, a high-precision self-facies-control low-frequency model is built using the self-facies-control low-frequency model construction technology. Finally, under the constraints of high-precision stratigraphic framework and self-facies-control low-frequency model, self-facies-control pre-stack inversion is realized, which effectively improves the accuracy of sand body prediction near the fault, overlimit thick reservoir characterization and reservoir lateral boundary identification. The application in E Oilfield shows that this method has achieved good results. The thickness coincidence rate of the horizontal length of 16 new drilled development wells is 91%.

Key words: deep learning, complex fault, overlimit thick reservoir, reservoir boundary, self-facies-control low frequency model

CLC Number:

TE122

WANG Zongjun, TIAN Nan, FAN Ting'en, GAO Yunfeng. Self-facies-control Pre-stack Inversion Technology for Turbidite Sandstone Reservoir with Complex Fault System[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2023, 45(2): 43-57.

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URL: http://journal15.magtechjournal.com/Jwk_xnzk/EN/10.11885/j.issn.1674-5086.2021.01.29.01

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