Saudi Arabian Oil Company

Novosibirsk State University

Diego Rovetta

Daniele Colombo

Federico Miorelli

Gary Wayne Mcneice

Ernesto Sandoval Curiel

Apostolos Kontakis

Ersan Turkoglu

Gary W McNeice

Mikhail Belonosov

Anastasiia Galaktionova

Andrei Belonosov

This specification describes workflows for, but is not limited to, performing full waveform inversion (FWI) to build high resolution velocity models to improve the accuracy of seismic imaging of a subterranean formation. This specification describes processes to automatically edit and enhance S/N quality of seismic data (such as land seismic data) to prepare the datasets for FWI. The methods for automatic corrections and pre-processing include: automatic iterative surface-consistent residual statics calculation, automatic rejection of anomalous traces (such as dead traces), and the automatic correction of surface-consistent amplitude anomalies (such as by scalar or deconvolution approaches). The operations include automatic 'muting' of noise before first arrivals.

Automatic data enhancement for full waveform inversion in the midpoint-offset domain

Methods for full waveform inversion (FWI) in the midpoint-offset domain include using a computer system to sort seismic traces into common midpoint-offset bins (XYO bins). For each XYO bin, a linear moveout correction is applied to a collection of seismic traces within the XYO bin. The collection of seismic traces is stacked to form a pilot trace. The computer system determines a surface-consistent residual static correction for each seismic trace. The computer system determines that the surface-consistent residual static correction for each seismic trace is less than a threshold. Responsive to the determining that the surface-consistent residual static correction is less than the threshold, the computer system stacks the collection of seismic traces to provide the pilot trace. The computer system groups the pilot traces for the XYO bins into a set of virtual shot gathers. The computer system performs one-dimensional FWI based on each virtual shot gather.

Full waveform inversion in the midpoint-offset domain

Systems and methods are provided to correct seismic data for the undesired effects caused by near surface velocity variations. In one embodiment, a method includes receiving travel time data for a near surface region and estimating an initial velocity model for the near surface region using the travel time data. The method can include updating the velocity model by performing an inversion iteration of including inversion of travel times to estimate slowness. The process can also include calculating at least one long-wavelength static for the near surface region. The long-wavelength statics may be used to correct for undesired effects caused by near surface velocity variations.

Systems and methods to correct seismic data for undesired effects caused by near surface velocity variations by correcting for long-wavelength statics

Seismic data from seismic exploration surveys are mapped into a hypercube of bins or voxels in a four-dimensional space (X, Y, Offset, and Azimuth) according to Common Mid-Point (or CMP) between source and receivers. The mapped data from individual voxels or bins is then analyzed by multimodal statistics. Robust estimates of first break picks are obtained from the analysis. The first break picks are then used to as seed inputs for autopicking iteration, which proceeds to convergence. Estimates of confidence levels in the data are provided for re-picking to reduce computer processing time in successive autopicking iterations. Analysis is provided of different seismic attributes such as azimuthal velocity variations indicative of anisotropy, positioning errors of sources/receivers, geometry errors, and three dimensional distribution of inversion residuals. Analysis is also performed of standard deviation of the travel time data useful for estimating data errors in the inversion covariance matrix.

Automatic quality control of seismic travel time

In some implementations, airborne electromagnetic (AEM) data and seismic data for a geographic region including sand dunes are received, and the AEM data identifies apparent resistivity as a function of depth within the sand dunes. An inversion with cross-domain regularization is calculated of the AEM data and the seismic data to generate a velocity-depth model, and the velocity depth model identifies velocity variations within the sand dunes. A seismic image using the velocity-depth model is generated.

Determining sand-dune velocity variations

A method for refraction-based surface-consistent amplitude compensation and deconvolution includes receiving seismic traces, the seismic traces generated using at least one source and at least one receiver; calculating an amplitude residual for each seismic trace; determining surface-consistent amplitude residuals for the at least one source and the at least one receiver based on the amplitude residual for each seismic trace; and performing surface-consistent amplitude correction to each seismic trace by applying the determined surface-consistent amplitude residuals for the at least one source and the at least one receiver.

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Ras Tanura, Saudi Arabia

Diego Rovetta