Chevron U.s.a. Inc.

Yijie Zhou

Ke Wang

David Christensen

Jinsong Chen

Zhao Zhang

Sandra C Saldana

A method is described for seismic inversion with uncertainty quantification including performing low frequency Markov Chain Monte Carlo (MCMC) processes on rock physics models to generate low frequency models (LFMs) of rock properties and training a deep neural network using the low frequency models and synthetic seismograms to generate a trained neural network. Given a seismic dataset, the trained neural network can generate a high frequency rock property model and then broad-band MCMC processes can be performed on the high frequency rock property model for uncertainty quantification. The method is executed by a computer system.

System and method for prediction of reservoir parameters with uncertainty quantification

A method is described for reservoir structure characterization including obtaining well logs and seismic data; performing facies classification and stratigraphic sequencing on the well logs to identify a plurality of layers; estimating wavelets from the seismic data and using the wavelets to tie synthetic seismograms from well logs to the seismic data; determining a mean value for each elastic property in each layer of the well logs and assigning the mean value to each layer to generate blocky well logs; using the wavelets to attempt to tie synthetic seismograms from the blocky well logs to the seismic data; defining facies-dependent properties based on the blocky well logs; performing global optimization using the facies-dependent properties and the seismic data to find thicknesses of the layers across the volume of interest; and mapping the reservoir structure based on the global optimization to generate a graphical representation of the reservoir structure.

System and method for reservoir structure characterization

A neural network is utilized to improve the resolution of subsurface inversion. The neural network leverages posterior distribution of samples and adds high frequency components to the inversion by utilizing the data in both the time domain and the frequency domain. The improved resolution of the subsurface inversion enables more accurate prediction of subsurface characteristics (e.g., reservoir architecture).

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Richmond, TX, United States of America

Yijie Zhou