Exxonmobil Upstream Research Company

Sunwoong Lee

Jerome R Krebs

Anatoly I Baumstein

Young Ho Cha

Partha Sarathi Routh

Warren S Ross

Yaxun Tang

John E Anderson

Spyridon K Lazaratos

Carey M Marcinkovich

Volkan Akcelik

Ramesh Neelamani

Pavel Dimitrov

Anoop A Mullur

Valeriy V Brytik

David L Hinkley

Sirui Tan

Nathan Downey

Spyridon Lazaratos

Spyridon Lazarato

A method and apparatus for identifying features of a subsurface region, including: obtaining an initial physical property model and survey data for the subsurface region; identifying a current model to be the initial physical property model; and executing one or more iterations of: generating synthetic data and forward wavefields with the current model and the survey data by forward modeling with forward wave equations representing isotropic wave-mode-independent attenuation; generating adjoint wavefields with the synthetic data and the survey data by adjoint modeling with adjoint wave equations representing isotropic wave-mode-independent attenuation; computing an objective function gradient with the forward wavefields and the adjoint wavefields by solving gradient equations with the corresponding wave equations representing isotropic wave-mode-independent attenuation; computing a search direction of the objective function; searching for a possible improved model along the search direction; and updating the current model to be the possible improved model.

Inversion, migration, and imaging related to isotropic wave-mode- independent attenuation

A computer-implemented method for updating subsurface models including: using an offset continuation approach to update the model, and at each stage defining a new objective function where a maximum offset for each stage is set, wherein the approach includes, performing a first stage iterative full wavefield inversion with near offset data, as the maximum offset, to obtain velocity and density or impedance models, performing subsequent stages of iterative full wavefield inversion, each generating updated models, relative to a previous stage, wherein the subsequent stages include incrementally expanding the maximum offset until ending at a full offset, wherein a last of the stages yields finally updated models, the subsequent stages use the updated models as starting models, and the full wavefield inversions include constraining scales of the velocity model updates at each stage of inversion as a function of velocity resolution; and using the finally updated models to prospect for hydrocarbons.

Full wavefield inversion with reflected seismic data starting from a poor velocity model

A computer-implemented method for updating a physical properties model of a subsurface region in an iterative inversion of seismic data using a gradient of a cost function that compares the seismic data to model-simulated data, said method comprising: obtaining a contrast model of a subsurface physical parameter that is sensitive to data dynamics and a kinematic model of a subsurface physical parameter; determining a gradient of a cost function using the contrast model and the kinematic model, wherein the cost function compares seismic data to model-simulated data; updating the kinematic model using a search direction derived from the gradient; adapting the contrast model according to an update to the kinematic model performed in the updating step; iteratively repeating the determining, updating, and adapting steps until a predetermined stopping criteria is reached, and generating a subsurface image from a finally updated kinematic model; and using the subsurface image to prospect for hydrocarbons.

Tomographically enhanced full wavefield inversion

Method for reducing computational time in inversion of geophysical data to infer a physical property model (), especially advantageous in full wavefield inversion of seismic data. An approximate Hessian is pre-calculated by computing the product of the exact Hessian and a sampling vector composed of isolated point diffractors (), and the approximate Hessian is stored in computer hard disk or memory (). The approximate Hessian is then retrieved when needed () for computing its product with the gradient () of an objective function or other vector. Since the approximate Hessian is very sparse (diagonally dominant), its product with a vector may therefore be approximated very efficiently with good accuracy. Once the approximate Hessian is computed and stored, computing its product with a vector requires no simulator calls (wavefield propagations) at all. The pre-calculated approximate Hessian can also be reused in the subsequent steps whenever necessary.

Accelerating full wavefield inversion with nonstationary point-spread functions

Method for performing simultaneous encoded-source inversion of geophysical data to estimate parameters of a physical property model (), especially adapted for surveys without fixed-receiver acquisition geometry, such as marine seismic surveys with moving source and receivers. The encoding functions () used on the sources to generate one or more simultaneous encoded-source gathers of data (), as well as to simulate the same (), are orthogonal or pseudo-orthogonal with respect to cross-correlation. In addition, receivers are also encoded, with the receiver encoding being designed to make a given receiver less sensitive to sources to which it was not listening during the survey (). The encoding functions may be temporal bandpass filters differing one from another by central frequency, phase, or both. Efficiency of the method may be further improved by grouping several sources into a super-source, grouping the corresponding gathers into a super-gather, and then applying the above encoding strategy.

Orthogonal source and receiver encoding

Method for reducing artifacts in a subsurface physical properties model () inferred by iterative inversion () of geophysical data (), wherein the artifacts are associated with some approximation () made during the iterative inversion. In the method, some aspect of the approximation is changed () as the inversion is iterated such that the artifacts do not increase by coherent addition.

Artifact reduction in iterative inversion of geophysical data

A method for estimating velocity dispersion in seismic surface waves in massive 3-D data sets () that improves upon auto-picking of a curve along the peak or ridge of the magnitude of the beam-formed field (). The seismic data are transformed to the frequency-slowness domain, where nonlinear constrained optimization is performed on the transformed data. The optimization matches a nonlinear mathematical parametric model () of a beam-formed field to that in the transformed data, adjusting the parameters each iteration to reduce mismatch (). Dispersion curves are determined by the center of the beam in the optimized models (). A preferred nonlinear parametric mathematical model is a Gaussian-shaped beam or a cosine-tapered boxcar beam.

Dispersion estimation by nonlinear optimization of beam-formed fields

Method for estimating the Hessian of the objective function, times a vector, in order to compute an update in an iterative optimization solution to a partial differential equation such as the wave equation, used for example in full wave field inversion of seismic data. The Hessian times vector operation is approximated as one forward wave propagation () and one gradient computation () in a modified subsurface model (). The modified subsurface model may be a linear combination of the current subsurface model () and the vector () to be multiplied by the Hessian matrix. The forward-modeled data from the modified model are treated as a field measurement in the data residual of the objective function for the gradient computation in the modified model. In model parameter estimation by iterative inversion of geophysical data, the vector in the first iteration may be the gradient of the objective function.

Methods for approximating hessian times vector operation in full wavefield inversion

Method for simultaneous full-wavefield inversion of gathers of source (or receiver) encoded geophysical data to determine a physical properties model () for a subsurface region, especially suitable for surveys where fixed receiver geometry conditions were not satisfied in the data acquisition. Simultaneous source separation () is performed to lessen any effect of the measured geophysical data's not satisfying the fixed-receiver assumption. A data processing step () coming after the simultaneous source separation acts to conform model-simulated data () to the measured geophysical data () for source and receiver combinations that are missing in the measured geophysical data.

Simultaneous source encoding and source separation as a practical solution for full wavefield inversion

Method for converting seismic data to obtain a subsurface model of, for example, bulk modulus or density. The gradient of an objective function is computed () using the seismic data () and a background subsurface medium model (). The source and receiver illuminations are computed in the background model (). The seismic resolution volume is computed using the velocities of the background model (). The gradient is converted into the difference subsurface model parameters () using the source and receiver illumination, seismic resolution volume, and the background subsurface model. These same factors may be used to compensate seismic data migrated by reverse time migration, which can then be related to a subsurface bulk modulus model. For iterative inversion, the difference subsurface model parameters () are used as preconditioned gradients ().

Methods for subsurface parameter estimation in full wavefield inversion and reverse-time migration

Embodiments use seismic processing methods that account for the spatial variability of surface wave velocities. Embodiments analyze surface wave properties by rapidly characterizing spatial variability of the surface waves in the seismic survey data (). Filtering criteria are formed using the spatial variability of the surface waves (). The filtering criteria can then be used to remove at least a portion of the surface waves from the seismic data (). The rapid characterization involves estimating a local group velocity of the surface waves by cross-correlation of the analytic signals ().

Characterizing spatial variability of surface waves in seismic processing

Embodiments are directed to systems and methods () that enable spatial variability of surface waves to be accounted for in dispersion correction in seismic data processing. This yields superior surface wave noise mitigation, with reduced likelihood of attenuating signal. Embodiments are operative with spatially inhomogeneous media.

Surface wave mitigation in spatially inhomogeneous media

Method for simultaneous full-wavefield inversion of gathers of source (or receiver) encoded geophysical data to determine a physical properties model for a subsurface region, especially suitable for surveys where fixed receiver geometry conditions were not satisfied in the data acquisition. First, a shallow time window of the data () where the fixed receiver condition is satisfied is inverted by simultaneous encoded () source inversion (). Then, the deeper time window of the data () is inverted by sparse sequential source inversion (), using the physical properties model from the shallow time window () as a starting model (). Alternatively, the shallow time window model is used to simulate missing far offset data () producing a data set satisfying the stationary receiver assumption, after which this data set is source encoded () and inverted by simultaneous source inversion ().

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Houston, TX, United States of America

Sunwoong Lee