Exxonmobil Upstream Research Company

Tomoseis, Inc.

Spyridon K Lazaratos

Partha Sarathi Routh

Anatoly I Baumstein

Ke Wang

Jerome R Krebs

Sunwoong Lee

Carey M Marcinkovich

Reeshidev Bansal

Ivan Chikichev

Ramesh Neelamani

Brian P West

John E Eastwood

Michael P Matheney

Hongchuan Sun

Stefan Hussenoeder

Di Yang

Jonathan Liu

Jia Yan

Eugene C Trantham

Kyle J Basler-Reeder

Pravin M Shah

Roy L David

Peter Varnai

Eric G Wildermuth

A method, including: determining, with a computer, a reflectivity spectrum; generating, with a computer, a target spectrum that is a convolution of a roll-off function and the reflectivity spectrum; and filtering, with a computer, seismic data to cause the seismic data to have the target spectrum.

Method for generating optimized seismic target spectrum

A method, including: obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges; performing, with a computer, an acoustic full wavefield inversion process on each of the subsets, respectively, to invert for density and generate respective density models; generating acoustic impedances for each of the subsets, as a function of reflection angle, using the respective density models; and transforming, using a computer, the acoustic impedances for each of the subsets into reflectivity sections, wherein the transforming includes normalizing the reflectivity sections by their respective bandwidth.

FWI model domain angle stacks with amplitude preservation

A method, including: obtaining a velocity model generated by an acoustic full wavefield inversion process; generating, with a computer, a variable Q model by applying pseudo-Q migration on processed seismic data of a subsurface region, wherein the velocity model is used as a guided constraint in the pseudo-Q migration; and generating, with a computer, a final subsurface velocity model that recovers amplitude attenuation caused by gas anomalies in the subsurface region by performing a visco-acoustic full wavefield inversion process, wherein the variable Q model is fixed in the visco-acoustic full wavefield inversion process.

Q-compensated full wavefield inversion

Method for multi-parameter inversion using elastic inversion. This method decomposes data into offset/angle groups and performs inversion on them in sequential order. This method can significantly speed up convergence of the iterative inversion process, and is therefore most advantageous when used for full waveform inversion (FWI). The present inventive approach draws upon relationships between reflection energy and reflection angle, or equivalently, offset dependence in elastic FWI. The invention uses recognition that the amplitudes of small angle (near offset) reflections are largely determined by acoustic impedance alone (), independent for the most part of Vp/Vs. Large angle (middle and far offset) reflections are affected by Ip, Vp/Vs () and other earth parameters such as density () and anisotropy. Therefore, the present inventive method decomposes data into angle or offset groups in performing multi-parameter FWI to reduce crosstalk between the different model parameters being determined in the inversion.

Multi-parameter inversion through offset dependent elastic FWI

Method for speeding up iterative inversion of seismic data () to obtain a subsurface model (), using local cost function optimization. The frequency spectrum of the updated model at each iteration is controlled to match a known or estimated frequency spectrum for the subsurface region, preferably the average amplitude spectrum of the subsurface P-impedance. The controlling is done either by applying a spectral-shaping filter to the source wavelet () and to the data () or by applying the filter, which may vary with time, to the gradient of the cost function (). The source wavelet's amplitude spectrum (before filtering) should satisfy D(f)=fI(f)W(f), where f is frequency, D(f) is the average amplitude spectrum of the seismic data, and I(f) is the average amplitude spectrum for P-impedance in the subsurface region () or an approximation thereof.

Convergence rate of full wavefield inversion using spectral shaping

Wavelet estimation method, particularly advantageous for full wavefield inversion ('FWI') of seismic data, that makes use of both the primary and multiple reflections in the data. The inventive method uses an FWI algorithm to generate a subsurface model from primary reflections () in a shallow layer before first arrival of multiple reflections (). The model is then used to simulate multiples (). The wavelet is subsequently modified () such that the simulated multiples closely match the true recorded multiples (). The simulated multiples may then be subtracted from the measured data () thereby creating a deeper top layer of data substantially free of multiples, and the method may then be repeated to extend the subsurface model to a greater depth ().

Method of wavelet estimation and multiple prediction 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 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 (). The inversion involves optimization of a cross-correlation objective function ().

Simultaneous source inversion for marine streamer data with cross-correlation objective function

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 ().

Hybrid method for full waveform inversion using simultaneous and sequential source method

A geophysical model of a subsurface region is generated based on seismic data, e.g., seismic reflection data. Migration and seismic inversion are applied to the seismic data to generate estimates of one or more physical or seismic properties of the subsurface region. Seismic inversion, such as spectral shaping inversion, is applied before or after migrating the seismic data through a variety of techniques that each avoid the amplification of dipping energy while optimizing computational efficiency and/or accuracy.

Spectral shaping inversion and migration of seismic data

A method for assessing the suitability of seismic data for quantitative amplitude analysis, where the concern is excessive residual normal moveout (RNMO). The invention uses a near offset stack and a far offset stack, the time difference between the two, a mute pattern, a reflection shape assumption for the RNMO, and a waveform and frequency for the far stack traces to generate a formula that estimates far stack amplitude error caused by RNMO. The formula can be used to compensate the far stack amplitude where the error is not so great as to require reprocessing of the data. The method can also be applied to interpreted amplitude maps.

Method for post processing compensation of amplitude for misaligned and misstacked offset seismic data

A method for spectral balancing of near- and far-offset seismic data, whereby velocity and offsets are determined for the seismic data. First, an NMO stretch compensation filter is created by the following steps. A frequency spectrum of the near-offset seismic data is estimated and a stretch factor &bgr; is calculated for the frequency spectrum, using the velocity and the offsets. A stretched frequency spectrum of near-offset seismic data is calculated, based on the stretch factor &bgr;. The frequency spectrum of near-offset seismic data is divided by the stretched frequency spectrum of near-offset seismic data, generating a first frequency response filter. High frequency gain is limited in the first frequency response filter, generating a first gain-limited filter. The first gain-limited filter is convolved with a low pass filter, generating the NMO stretch compensation filter. Then the NMO stretch compensation filter is applied to the seismic data. Second, a relative Q compensation filter is created by the following steps. A difference in amplitude loss between the near- and far-offset seismic data is calculated, using the velocity and the offsets. A frequency gain is calculated, based on the difference in amplitude loss. A second frequency response filter is created, based on the frequency gain. High frequency gain is limited in the second frequency response filter, generating a second gain-limited filter. The second gain-limited filter is convolved with a low pass filter, generating the relative Q compensation filter. The relative Q compensation filter is applied to the seismic data.

Method for spectral balancing of near- and far-offset seismic data

A method for moveout-correcting CDP gathers of seismic data that takes nonhyperbolic moveout at large offsets into account. The entire range of offsets is divided into smaller ranges. Within each smaller range, a curve is fitted to the reflection time vs. offset data, and these separate curves are made to be continuous at range boundaries. The curves may be the hyperbolic or quartic options available in commercial seismic data processing packages. Additional computation time required due to lack of a single velocity function can be compensated for by interpolation techniques. The method has application to, inter alia, AVO analysis.

Method for non-hyperbolic moveout analysis of seismic data

A method of evaluating a subsurface region by separating/enhancing a certain type of seismic event data of interest from an overall set of seismic event data which includes other, different types of seismic event data is disclosed herein. In accordance with one feature, a particular type of gather is generated from the seismic event data such that the gather includes at least a portion of the data which is of interest and at least a portion of the other data. A series of data discrimination lines are incorporated into the gather at positions and directions which are established in the gather in a predetermined way. Using the data discrimination lines, the data of interest which is present in the gather is separated/enhanced with respect to the other data within the gather. The separated data may be used for example in producing a map of the particular subterranean region. In accordance with another feature, the gather is selected such that the incorporated discrimination lines approach a near parallel relationship with one another. Thereby, the data is transformed in a way which causes the discrimination lines to be parallel with one another, resulting in reduced frequency distortion accompanied by improved accuracy in the separation/enhancement of data. In accordance with still another feature, the disclosed data separation/enhancement method is compatible with an iterative approach.

Growing community of inventors

Houston, TX, United States of America

Spyridon K Lazaratos