ISSN 1004-4140
CN 11-3017/P
Volume 31 Issue 3
May  2022
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ZHANG Y, QIN D W, HUANG J. Application of lame parameter direct inversion in hydrocarbon detection of low-porosity and low-permeability reservoirs in N structure in East China Sea basin[J]. CT Theory and Applications, 2022, 31(3): 305-316. DOI: 10.15953/j.ctta.2021.088. (in Chinese).
Citation: ZHANG Y, QIN D W, HUANG J. Application of lame parameter direct inversion in hydrocarbon detection of low-porosity and low-permeability reservoirs in N structure in East China Sea basin[J]. CT Theory and Applications, 2022, 31(3): 305-316. DOI: 10.15953/j.ctta.2021.088. (in Chinese).
shu

Application of Lame Parameter Direct Inversion in Hydrocarbon Detection of Low-porosity and Low-permeability Reservoirs in N Structure in East China Sea Basin

  • CNOOC of Shanghai, Shanghai 200335, China

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  • Received Date: February 24, 2022
  • Accepted Date: March 03, 2022
  • Available Online: March 14, 2022
  • Published Date: May 22, 2022
    Graphical Abstract
    • Abstract
  • The main target layer of the N structure in the East China Sea is a delta subaqueous distributary channel sand body developed under a strong hydrodynamic environment. The distribution of planar sand layer is discontinuous and the lateral heterogeneity is very strong. Under the influence of deep burial compaction and diagenesis, the reservoir is characterized by low porosity and low permeability, and the properties of rock-physics are overlapped seriously. In addition, the lack of large angle information of deep seismic data is a common problem. It is of great importance to implement the fluid distribution range of tight reservoir in the study area for the design and deployment of exploration and development. In this paper, a new method of deep seismic fluid description is introduced based on direct inversion of lame parameters. Through qualitative and quantitative analysis of rock-physics of measured well data, optimal highly sensitive hydrocarbon detection factor is selected. Furthermore, the AVO properties of Lambda parameters are extracted from the pre-stack trace set by combining with the parametric equations of the two AVO models of Lambda parameters. Then, the AVO properties are directly transformed into the interlayer elastic information by using the colored inversion technique. Finally, the seismic fluid sensitive elastic data is obtained to guide the seismic fluid description. The practical application shows that the hydrocarbon detection results of this method are compatible with the logging interpretation achievement, and can effectively describe the low-porosity and low-permeability reservoirs fluid development law of the study area, and can provide important technical support for the discovery of oil and gas resources in new fields.
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