Prestack inversion based on anisotropic Markov random field–maximum posterior probability inversion and its application to identify shale gas sweet spots
Wang Kang-Ning1,2, Sun Zan-Dong1,3, and Dong Ning2
1. Lab for Integration of Geology & Geophysics, China University of Petroleum, Beijing 102249, China.
2. SINOPEC Petroleum Exploration & Production Research Institute, Beijing 100083, China.
3. BGP, CNPC, Zhuozhou 072750, China.
Abstract：
Economic shale gas production requires hydraulic fracture stimulation to increase the formation permeability. Hydraulic fracturing strongly depends on geomechanical parameters such as Young’s modulus and Poisson’s ratio. Fracture-prone sweet spots can be predicted by prestack inversion, which is an ill-posed problem; thus, regularization is needed to obtain unique and stable solutions. To characterize gas-bearing shale sedimentary bodies, elastic parameter variations are regarded as an anisotropic Markov random field. Bayesian statistics are adopted for transforming prestack inversion to the maximum posterior probability. Two energy functions for the lateral and vertical directions are used to describe the distribution, and the expectation–maximization algorithm is used to estimate the hyperparameters of the prior probability of elastic parameters. Finally, the inversion yields clear geological boundaries, high vertical resolution, and reasonable lateral continuity using the conjugate gradient method to minimize the objective function. Antinoise and imaging ability of the method were tested using synthetic and real data.
Wang Kang-Ning,Sun Zan-Dong,Dong Ning. Prestack inversion based on anisotropic Markov random field–maximum posterior probability inversion and its application to identify shale gas sweet spots[J]. APPLIED GEOPHYSICS, 2015, 12(4): 533-544.
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2015, Vol. 12 
