Exxonmobil Upstream Research Company

Tetyana Vdovina

Anatoly I Baumstein

Yaxun Tang

John E Anderson

Thomas A Dickens

Carey M Marcinkovich

Valeriy V Brytik

Di Yang

Reeshidev Bansal

William Curry

Sirui Tan

Gboyega Ayeni

A multi-stage FWI workflow uses multiple-contaminated FWI models to predict surface-related multiples. A method embodying the present technological advancement, can include: using data with free surface multiples as input into FWI; generating a subsurface model by performing FWI with the free-surface boundary condition imposed on top of the subsurface model; using inverted model from FWI to predict multiples; removing predicted multiples from the measured data; using the multiple-free data as input into FWI with absorbing boundary conditions imposed on top of the subsurface model; and preparing a multiple free data set for use in conventional seismic data processing.

Multistage full wavefield inversion process that generates a multiple free data set

A method for exploring for hydrocarbons, including: simulating a seismic waveform, using a computer, wherein computations are performed on a computational grid representing a subsurface region, said computational grid using perfectly matched layer (PML) boundary conditions that use an energy dissipation operator to minimize non-physical wave reflections at grid boundaries; wherein, in the simulation, the PML boundary conditions are defined to reduce computational instabilities at a boundary by steps including, representing direction of energy propagation by a Poynting vector, and dissipating energy, with the dissipation operator, in a direction of energy propagation instead of in a phase velocity direction; and using the simulated waveform in performing full waveform inversion or reverse time migration of seismic data, and using a physical property model from the inversion or a subsurface image from the migration to explore for hydrocarbons.

Poynting vector minimal reflection boundary conditions

A method for iteratively inverting seismic data to jointly infer a model for at least P-wave velocity and attenuation parameters of the subsurface, the method including: jointly inverting the P-wave velocity and attenuation parameters with an iterative visco-acoustic full wavefield inversion process, wherein the iterative visco-acoustic full wavefield inversion process includes computing a gradient of an objective function, the objective function measuring a misfit between all or part of the seismic data and corresponding model-simulated seismic data; for each of the P-wave velocity and attenuation parameters, computing a search direction in model space from the gradient; determining line search step sizes &#x3b1; and &#x3b2; for the search directions for the P-wave velocity and attenuation parameters, respectively, wherein a ratio of the step sizes is a function of the P-wave velocity parameter; and using the step sizes &#x3b1; and &#x3b2; and the search directions for each of the P-wave velocity and attenuation parameters, computing a new search direction in model space, then performing a line search along the new search direction to arrive at a new step size, and using the new step size and the new search direction to generate an updated model for a current iteration of the iterative visco-acoustic full wavefield inversion process.

Joint full wavefield inversion of P-wave velocity and attenuation using an efficient first order optimization

A method, including performing, with a computer, up/down separation of geophysical data, which produces an approximate up-going wavefield and an approximate down-going wavefield; creating an areal source based at least in part on the down-going wavefield; and performing, with a computer, a full wavefield inversion process with the areal source, and an objective function measuring a misfit between modeled up-going wavefields and recorded up-going wavefields, wherein the full wavefield inversion process generates a final subsurface physical property model.

FWI with areal and point sources

Method for correcting seismic simulations, RTM, and FWI for temporal dispersion due to temporal finite difference methods in which time derivatives are approximated to a specified order of approximation. Computer-simulated seismic data () are transformed from time domain to frequency domain (), and then resampled using a mapping relationship that maps, in the frequency domain, to a frequency at which the time derivative exhibits no temporal dispersion (), or to a frequency at which the time derivative exhibits a specified different order of temporal dispersion. Alternatively, measured seismic data from a field survey () may have temporal dispersion of a given order introduced, by a similar technique, to match the order of approximation used to generate simulated data which are to be compared to the measured data.

Growing community of inventors

Spring, TX, United States of America

Tetyana Vdovina