Pgs Geophysical As

Saudi Arabian Oil Company

Robertus Franciscus Hegge

Roald Gunnar Van Borselen

Rolf Huibert Baardman

Simon Rice Barnes

Roald Van Borselen

Paolo Terenghi

Andreas Klaedtke

Peter Aaron

Amr Ahmed Mahmoud Ibrahim

Jewoo Yoo

Paul M Zwartjes

Peter A Aaron

A method may include obtaining an input gather regarding a geological region of interest. The method may further include obtaining parameterization data regarding a seismic processing operation. The parameterization data may correspond to a first set of process parameter values that are different from a second set of process parameter values that are used to generate the input gather. The method may further include generating a predicted output gather using a machine-learning model, the input gather, and the parameterization data. The machine-learning model may include an encoder model and a decoder model. The method may further include generating a seismic image of the geological region of interest using the predicted output gather.

Method and system for determining seismic processing parameters using machine learning

Embodiments relate to a sensor cable that may be reconfigurable to have various combinations of seismic sensors. An apparatus may comprise a sensor cable and seismic sensors distributed throughout a volume of the sensor cable and along all three axes of the sensor cable, wherein the seismic sensors are assigned to sampling groups that are reconfigurable and not hardwired.

Reconfigurable seismic sensor cable

Systems and methods include a method for deblending signal and noise data. A shot domain for actual sources, a receiver domain for virtual sources, and a receiver domain for actual sources are generated from blended shot data. A dictionary of signal atoms is generated. Each signal atom includes a small patch of seismic signal data gathered during a small time window using multiple neighboring traces. A dictionary of noise atoms is generated. Each noise atom includes a small patch of seismic noise data gathered during a small time window using multiple neighboring traces. A combined signal-and-noise dictionary is generated that contains the signal atoms and the noise atoms. A sparse reconstruction of receiver domain data is created from the combined signal-and-noise dictionary. The sparse reconstruction is split into deblended data and blending noise data based on atom usage to create deblended shot domain gathers for actual sources.

Deblending using dictionary learning with virtual shots

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for echo-deblending using coincidence-filtering of offshore seismic data. In one aspect, a method includes receiving an offshore seismic dataset of a surveyed subsurface, the offshore seismic dataset comprising a primary-wave signal and a ghost-wave signal; determining a forward extrapolation and a backward extrapolation for the offshore seismic dataset; determining a coincident signal by applying a coincidence filtering to the forward extrapolation and the backward extrapolation; extrapolating the coincident signal to determine a ghost-wave value for the ghost-wave signal; applying adaptive subtraction to the offshore seismic dataset with the ghost-wave value to determine a computed primary-wave value for the primary-wave signal; generating a model of the surveyed subsurface based on primary-wave data calculated from the offshore seismic dataset based on the computed primary-wave value; and evaluating a productivity of the surveyed subsurface according to the model.

Deghosting of seismic data through echo- deblending using coincidence filtering

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a seismic data de-blending model. In one aspect, a method comprises: obtaining a plurality of de-blending training examples, wherein each de-blending training example defines: (i) one or more blended seismic traces, and (ii) for each blended seismic trace, a corresponding plurality of target unblended seismic traces; using the de-blending training examples to train a de-blending model having a plurality of de-blending model parameters, comprising, for each de-blending training example: processing the one or more blended seismic traces of the training example using the de-blending model to generate an output which defines, for each of the one or more blended seismic traces of the training example, a corresponding plurality of estimated unblended seismic traces; and adjusting values of the plurality of de-blending model parameters.

Seismic data de-blending

Prediction and subtraction of multiple diffractions may include transforming previously acquired seismic data from a time-space domain to a transformed domain using a dictionary of compressive basis functions and separating, within the transformed previously acquired seismic data, a first portion and a second portion of the transformed previously acquired seismic data. Prediction and subtraction of multiple diffractions may also include predicting a plurality of multiple diffractions based on the separated first and second portions and adaptively subtracting the predicted multiple diffractions from the previously acquired seismic data. Prediction and subtraction of multiple diffractions may also include inverse transforming a particular seismic data set from the transformed domain to the time-space domain.

Prediction and subtraction of multiple diffractions

Computational systems and methods for interpolating a pressure wavefield based on pressure wavefield and particle motion wavefield data are disclosed. The pressure and particle motion wavefields are sampled at a sample rate that is less than a lower bound for sample rates typically used to interpolate the pressure wavefield from the pressure wavefield samples alone. The particle motion wavefield can an acceleration wavefield or a time derivative of a velocity wavefield.

Methods and systems for interpolation of multi-component seismic data collected in a marine environment

One embodiment relates to a method of wavefield regularization for geophysical data acquisition of seismic geophysical data. Measured traces, are obtained from an array of sensors. For each grid point on a processing grid, best-fitting traces of the measured traces are found. Using the best-fitting traces, spectral amplitudes of down-going and up-going wavefields are computed. The down-going and up-going wavefields are subsequently transformed to an output grid in a space-time domain. Another embodiment relates to an apparatus for wavefield regularization of geophysical data acquisition. Other embodiments, aspects and features are also disclosed.

Wavefield regularization by 3-D wavefield decomposition for geophysical data

A best fitting trace in seismic data is determined for a desired trace to be reconstructed. A dip-based correction is calculated per trace and per sample for differences in azimuth, common midpoint coordinates, and offset between the best fitting trace and the desired trace. The dip-based correction is applied to the best fitting trace to reconstruct the desired trace for 3D surface-related multiple prediction.

Dip-based corrections for data reconstruction in three-dimensional surface-related multiple prediction

An operator is applied in a frequency domain to multiple source seismic data acquired in a marine survey, with multiple sources substantially horizontally collocated and at different depths, to generate an estimate of a primary source wavefield, the operator constructed to compensate for time delays between and for different depths of the primary and secondary sources. The primary source wavefield estimate is adaptively subtracted from the multiple source seismic data to generate secondary source wavefield estimates. Another operator is applied in the frequency domain to the secondary source wavefield estimates to generate another primary source wavefield estimate, the operator constructed to compensate for time delays between and for different depths of the primary and secondary sources. The primary and secondary source estimates are used, after further iterative refinements to remove noise, to generate upgoing and downgoing wavefield components of the multiple source seismic data.

Method for separating seismic sources in marine seismic surveys

Dips are calculated for a series of sets of adjacent multiple contribution traces, from seismic data representative of subsurface formations, in the vicinity of a boundary of an aperture of a multiple contribution gather, the seismic data acquired by deploying a plurality of seismic sensors proximate an area of the earth's subsurface to be evaluated, the seismic sensors generating at least one of an electrical and optical signal in response to seismic energy. The boundary of the aperture of the multiple contribution gather is recursively extended, based on the calculated dips. Multiple contribution traces in the multiple contribution gather with the extended aperture boundary are stacked to generate predicted multiple traces. The predicted multiple traces are subtracted from the seismic data to generate surface-related multiple attenuated data useful for imaging the subsurface formations.

Method for dynamic aperture determination for three-dimensional surface-related multiple elimination

A best fitting trace in seismic data is determined for a desired trace to be reconstructed. An azimuth correction is calculated for the azimuth difference between the best fitting trace and the desired trace. The azimuth correction is applied to the best fitting trace to reconstruct the desired trace for 3D surface-related multiple prediction.

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Rijswijk, Netherlands

Robertus Franciscus Hegge