Abstract
The low porosity and low permeability of tight sandstone pose considerable challenges on characterizing the reservoir fluid. Moreover, the geophysical characteristics of the tight sandstone are usually similar to the surrounding rocks, leading to small impedance contrast, compounding the difficulty of reservoir parameters inversion. Amplitude‐versus‐offset technology is an important tool to invert the reservoir parameters from seismic data. The existing amplitude‐versus‐offset inversion methods are challenged to predict the tight sandstone reservoir parameters for the reasons above. In this paper, we propose a new workflow based on amplitude‐versus‐offset attributes to characterize the tight sandstone gas reservoirs in the Xihu Depression, East China Sea.
Jiaxin Yu
My research encompasses both theoretical investigations in the domains of rock and subsurface studies, as well as the practical application of AI technologies to geoscientific endeavors. On the theoretical front, my research interests are primarily centered around rock physics, granular medium theory, and poroelasticity. From an applied perspective, my focus lies in leveraging artificial intelligence for rock mineral identification, employing Graph Neural Networks (GNNs) for simulating granular mediums, and contributing to open-source initiatives dedicated to rock physics and computational geoscience projects