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Xie, Chunhui (China National Petroleum Corporation) | Wang, Enli (China National Petroleum Corporation) | Zhong, Tingjun (Peking University) | Wang, Hongqiu (China National Petroleum Corporation) | Du, Bingyi (China National Petroleum Corporation)
As an important seismic method for the characterization of fracture orientation and density, the azimuthal P-wave anisotropic inversion is essentially dominated by the signal to noise ratio (SNR) of CRP gathers and azimuth distribution. As known to all, the higher the SNR is, the more accurate the AVAZ inversion results are. In addition, the distribution schemes for input azimuthal CRP gathers, including azimuth width, center and number, substantially affect the inversion efficiency and precision. In this paper, numerical model with different azimuth distribution scheme is designed to study the influence of azimuth distribution schemes on anisotropic inversion. It is proven that 1) for the fixed azimuthal center, the wider the azimuthal range is, the smaller inversion error is; 2) for the random azimuthal width, the inversion error is the smallest as the azimuth center is located at the range of 48° to 57°; 3) the inversion error is almost uncorrelated with the azimuth number. In particular, the optimization of azimuth division is definitely helpful to reduce condition number of inversion matrix, which is beneficial for improving the inversion accuracy.
Presentation Date: Tuesday, September 26, 2017
Start Time: 9:45 AM
Presentation Type: ORAL
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Prior information plays an important role in Bayesian AVO inversion. Conventionally, the prior distribution in the inversion focuses on the Gaussian distribution, Huber distribution, Cauchy or the improved Cauchy distribution. However, not all the parameters in field data belong to one of the above distributions. After statistical analysis with wells data of different regions, we found that inverse parameters were generally consistent with t-distribution, while errors were usually shown in Cauchy and Gaussian distribution. In view of this, Bayesian inversion algorithm based on t-distribution as priori constraint was proposed in this paper. The algorithm can adapt to the areas of different parameter distribution by choosing different degrees of freedom. And, improving the matching of the priori information can increase the credibility of the posterior function and so ensure a better inversion. Model test and real data example verify the stability and feasibility of this proposed method.