Fracture identification based on remote detection acoustic reflection logging
Zhang Gong1,2, Li Ning1,3, Guo Hong-Wei1, Wu Hong-Liang1, and Luo Chao1
1. PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China.
2. School of Earth and Space Sciences, Peking University, Beijing 100871, China.
3. Yangtze University, Wuhan, Hubei 430100, China.
Abstract：
Fracture identification is important for the evaluation of carbonate reservoirs. However, conventional logging equipment has small depth of investigation and cannot detect rock fractures more than three meters away from the borehole. Remote acoustic logging uses phase-controlled array-transmitting and long sound probes that increase the depth of investigation. The interpretation of logging data with respect to fractures is typically guided by practical experience rather than theory and is often ambiguous. We use remote acoustic reflection logging data and high-order finite-difference approximations in the forward modeling and prestack reverse-time migration to image fractures. First, we perform forward modeling of the fracture responses as a function of the fracture–borehole wall distance, aperture, and dip angle. Second, we extract the energy intensity within the imaging area to determine whether the fracture can be identified as the formation velocity is varied. Finally, we evaluate the effect of the fracture–borehole distance, fracture aperture, and dip angle on fracture identification.
Zhang Gong,Li Ning,Guo Hong-Wei et al. Fracture identification based on remote detection acoustic reflection logging[J]. APPLIED GEOPHYSICS, 2015, 12(4): 473-481.
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2015, Vol. 12 
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