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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Georeservoirs
Volume 12 - 2024 | doi: 10.3389/feart.2024.1456122
This article is part of the Research Topic Advances and New Methods in Reservoirs Quantitative Characterization Using Seismic Data View all articles

Post-stack multi-scale fracture prediction and characterization methods for granite buried hill reservoirs: A case study in the Pearl River Mouth Basin, South China Sea

Provisionally accepted
Junping Liu Junping Liu 1Huailai Zhou Huailai Zhou 1*Luyao Liao Luyao Liao 1*Cong Niu Cong Niu 2*Qiuyu Li Qiuyu Li 3*
  • 1 Chengdu University of Technology, Chengdu, China
  • 2 CNOOC Research Institute Ltd., and National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing, China
  • 3 Research and Development Center, BGPlnc. CNPC, Zhuozhou, China

The final, formatted version of the article will be published soon.

    Granite buried hill oil and gas reservoirs are relatively scarce worldwide, and the fine prediction and characterization of their fractures have always been a significant industry challenge. Particularly in the South China Sea region, large and thick granite buried-hill reservoirs are influenced by various geological processes such as weathering and tectonics, resulting in a complex internal fracture system. The seismic reflection characteristics exhibit high steepness, discontinuity, and significant amplitude differences, posing significant difficulties for the fine characterization of fractures. A systematic and comprehensive research approach has not yet been established. Therefore, this study considers the large granite-buried hill A reservoir in the South China Sea as a typical case study and proposes a multi-scale fracture fine prediction and characterization methodology system. The method starts with analyzing the fracture scale and genesis to refine the fracture scales identifiable by conventional seismic data. Based on this, the U-SegNet model and transfer learning are utilized to achieve fine detection of large-scale fractures. Meanwhile, using high-resolution ant tracking technology based on MVMD frequency division and sensitive attribute preferences realizes a fine prediction of mediumto-small-scale fractures. Finally, the discrete fracture network is used to conduct deterministic modeling of fractures from geometric morphology to percolation behavior. Ultimately, a post-stack seismic multi-scale fracture prediction-characterization technical workflow is established with the aim of finely characterizing the development degree, spatial morphology, and percolation characteristics of fractures in granite buried hill reservoirs, providing scientific evidence for oil and gas exploration and development.

    Keywords: Granite buried hill, Multi-scale fractures, deep learning, frequency-division attribute, fracture prediction and characterization, Discrete fracture network model

    Received: 28 Jun 2024; Accepted: 19 Jul 2024.

    Copyright: © 2024 Liu, Zhou, Liao, Niu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Huailai Zhou, Chengdu University of Technology, Chengdu, China
    Luyao Liao, Chengdu University of Technology, Chengdu, China
    Cong Niu, CNOOC Research Institute Ltd., and National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing, China
    Qiuyu Li, Research and Development Center, BGPlnc. CNPC, Zhuozhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.