Advanced Geophysical Technology Inc.

Exxonmobil Upstream Research Company

Wenyi Hu

Carey M Marcinkovich

Hongchuan Sun

Lorie K Bear

A computer-implemented method for obtaining reconstructed seismic data for determining a subsurface feature, includes: determining an initial training velocity model, training a machine learning model based on first training seismic data and second training seismic data generated from the training velocity model, the first training seismic data corresponding to one or more first frequencies, the second training seismic data corresponding to one or more second frequencies lower than the one or more first frequencies, obtaining, based on measured seismic data and the machine learning model, reconstructed seismic data corresponding to the one or more second frequencies, generating a velocity model based on the measured seismic data, the reconstructed seismic data, and a full waveform inversion (FWI), and when the generated velocity model does not satisfy a preset condition, updating the training velocity model based on the generated velocity model, to obtain updated reconstructed seismic data for determining a subsurface feature.

Methods and systems for obtaining reconstructed low-frequency seismic data for determining a subsurface feature

A computer-implemented method for determining a subsurface feature, includes: determining a first velocity model based on an initial velocity model; generating a second velocity model based on measured seismic data at one or more first frequencies, the first velocity model, and a full waveform inversion (FWI); and in response to the second velocity model not satisfying a preset condition, performing a seismic forward simulation on the second velocity model to generate simulated seismic data at one or more second frequencies lower than the one or more first frequencies; updating the first velocity model based on the simulated seismic data at the one or more second frequencies; and updating the second velocity model based on the measured seismic data at the one or more first frequencies, the updated first velocity model, and the FWI, to determine the subsurface feature.

Methods and systems for determining subsurface feature using broadband full waveform inversion

A memory-efficient Q-RTM computer method and apparatus for imaging seismic data is described. A seismic image may be formed from a memory-efficient Q-RTM module utilizing received attenuated seismic data. Seismic data is processed by the memory-efficient Q-RTM module to compensate for amplitude attenuation and phase velocity dispersion simultaneously during back-propagation in RTM. A negative quality factor, Q, is obtained by modifying the wave equation to compensate for amplitude attenuation. One or more dispersion optimization terms introduced to a wave equation for compensation of Q effects on the phase, solved by a finite difference algorithm, compensate for phase velocity change and further adjust amplitude attenuation compensation.

Memory efficient Q-RTM computer method and apparatus for imaging seismic data

Method for reconstructing subsurface Q depth profiles from common offset gathers () of reflection seismic data by performing migration (), ray tracing (), CDP-to-surface takeoff angle finding (), kernel matrix construction (), depth-to-time conversion and wavelet stretching correction (), source amplitude spectrum fitting, centroid frequency shift calculation (), and box-constrained optimization ().

Reflection seismic data Q tomography

A full wave inversion (FWI) may utilize an Amplitude-Frequency-Differentiation (AFD) or a Phase-Frequency-Differentiation (PFD) operation to form a velocity model of a subterranean formation utilizing recovered low wavenumber data. Received seismic data is processes to isolate two data signals at different frequencies. In an AFD operation, the two data signals are summed and the data of the envelope of the summed function is used for the FWI. In a PFD operation, the phase data of the quotient of the two data signals is used for the FWI. The FWI proceeds iteratively utilizing either the AFD or PFD data or with single frequency data until the cost function of the AFD or PFD is satisfied.

Beat tone full waveform inversion

Method for reconstructing subsurface Q models () from seismic data () by performing ray-based (), centroid frequency shift () Q tomography. The seismic source waveform's amplitude spectrum is approximated by a frequency-weighted exponential function of frequency (), having two parameters to adjust to fit the frequency shift data, thereby providing a better fit to various asymmetric source amplitude spectra. Box constraints may be used in the optimization routine, and a multi-index active-set method used in velocity tomography is a preferred technique for implementing the box constraints ().

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Katy, TX, United States of America

Wenyi Hu