DFN modelling constrained by multiple seismic attributes using the steering pyramid technology

Frontiers in Earth Science

Earth Science

Shanshan Gai¹  Wenzheng Yu¹  Yudi Wang²  Yungui Xu²  Libing Du²  Xuri Huang² 

[1] Geophysical Research Institute, Sinopec Shengli Oilfield Company, Dongying, China;State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, China;School of Geosciences and Technology, Southwest Petroleum University, Chengdu, China;

关键词: seismic attributes;    seismic data decomposition;    composite attribute;    fracture modelling;    discrete fracture network (DFN);   

DOI  :  10.3389/feart.2023.1257481

 received in 2023-07-12, accepted in 2023-08-29,  发布年份 2023

来源: Frontiers

【 摘 要 】

Fracture modelling is essential for understanding fluid flow in fractured hydrocarbon reservoirs, particularly in the phase of production; however, traditional discrete fracture network (DFN) modelling methods lack constraints that reflect characteristics of fracture development. Fractures or fracture networks exhibit a high degree of randomness; as such, it is difficult to model fracture characteristics. This paper proposes a new approach for DFN modelling constrained by seismic attributes. Firstly, the steerable pyramid method is adopted to improve seismic data resolution; secondly, multiple seismic attributes are extracted and combined into a composite attribute to characterize fracture spatial distribution; finally, a DFN modelling method is established by using the composite attribute as a location constraint. To verify the effectiveness of the approach, a case study is conducted in the Bonan Depression, in East China. The results show that, compared with the traditional DFN modelling methods, the DFN modelling with the location constraint create a more realistic fracture model which accurately reflects fracture distribution characteristics. The application demonstrates the potential of wide application prospects in fractured reservoirs.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Xu, Du, Gai, Yu and Huang.