Backgroud Accurately predicting reservoir parameters is significant for characterizing subsurface reservoirs, establishing gas accumulation patterns, releasing production capacity, and understanding fluid migration. The traditional approaches based on core measurement or mathematical-petrophysical modeling are limited by the strong multiplicity of solutions and low accuracy of elastic parameters inversion results, making it difficult to meet the demands of modern exploration.
Objective and Methods To more effectively predict reservoir parameters, this study proposed a petrophysical modeling-guided method for predicting parameters of low-permeability reservoirs. With the convolutional neural network (CNN) as a deep learning framework, the proposed method can predict water saturation, clay content, and porosity based on actual seismic data. Additionally, considering insufficient labeled data, the petrophysical modeling combined with the random perturbation of elastic parameters was adopted to generate high-quality training samples, thus effectively expanding the size of sample data.
Results and Conclusions The theoretical model tests demonstrate that: (1) This method can effectively predict the spatial distributions of parameters of low-permeability reservoirs in the case of low sensitivities of reservoir parameters to petrophysics. (2) Compared to data-driven deep learning, this method can yield high-accuracy predicted results of reservoir parameters based on merely a few log data. As substantiated by exploration in the Dongfang block of the Yinggehai Basin, the proposed method facilitates the optimization of well deployment, guiding the achievement of significant exploration breakthroughs and reserve discovery in the low-permeability areas of the basin.
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