الفهرس 
Chapter 1Only 14 pages are availabe for public view
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Abstract
Over the years, seismic amplitude variation with offset has been successfully applied for predicting the elastic properties of the subsurface. The physical model of the AVO inversion is described by the Zoeppritz equations, and its many linear approximations that yield different sets of elastic parameters. Nevertheless, the solution of the amplitude inversion is not unique due to limited bandwidth and noises of the seismic data, and different sensitivities among model parameters that result in reliable estimation of at most two elastic parameters. Unconventional reservoirs’ characterization, particular shales, requires additional parameters for estimating the effective moduli and predicting the high-quality gas zones. Here, we study the high-dimensional anisotropic AVO inversion, with the application to shale gas reservoirs to help in predicting the sweet spots. We build each section of the thesis based on conclusions of previous scholars, in the form of inspiration quotes that are presented at the beginning of each chapter. Each quotation provides an insight into the proposed methodology. Additionally, we make use of several fundamental concepts of state-of-the-art machine learning, to optimally integrate all the available data, well log, rock physics modeling, and surface seismic data, to enhance the reservoir characterization and reduce exploration risk.