Multi-attribute fusion fracture-cavity description technique and application based on deep learning: A case study of H gas field on the right bank of Amu Darya River
SUN Yao1, CHAI Hui2, WEI Xiaodong1, WEN Guangyao2, CHEN Jin'e1, LIU Wenjing1
1. Oversea Business Department, Geophysical Research Institute, BGP Inc., CNPC, Zhuozhou, Hebei 072751, China; 2. CNPC International(Turkmenistan), Beijing 100034, China
Abstract：Fracture-cavity reservoirs are well developed in the H gas field on the right bank of the Amu Darya River， and the degree of reservoir development and production vary greatly. Fracture development is a key factor restricting gas well productivity. With the advancing gas field exploration and development， higher requirements are put forward for reservoir fracture prediction. To this end， this paper introduces a fault prediction method based on deep learning and combines multi-attribute analysis to characterize fractures and cavities. Since the distribution of fractures and cavities is controlled by faults， faults should be first predicted by deep learning and identified by automatically interpreting 3D seismic data. Secondly， based on drilling， geology， core and logging information， the carbonate reservoir identification model is built to select the seismic attributes reflecting the development characteristics of fracture and cavities. Finally， according to the fault prediction results based on deep learning and multi-attribute analysis， the fracture-cavity body is characterized. The prediction results of fracture-cavity reservoirs show that the fracture-cavity reservoirs in Amu Darya River are characterized by high frequency and strong amplitude seismic response. The predicted fracture-cavity reservoirs are mainly developed near the fault system， which is consistent with the drilling and geological laws with reliable prediction results.
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SUN Yao, CHAI Hui, WEI Xiaodong, WEN Guangyao, CHEN Jin'e, LIU Wenjing. Multi-attribute fusion fracture-cavity description technique and application based on deep learning: A case study of H gas field on the right bank of Amu Darya River. Oil Geophysical Prospecting, 2023, 58(s1): 1-5.
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