Chevron U.s.a. Inc.

Adam Dean Halpert

Laura L Bandura

Lisa R Goggin

Ke Wang

Konstantin Osypov

Christopher Hugh Skelt

Gary Michael Hoversten

James P Disiena

Shuxing Cheng

David A Wilkinson

Dimitri Bevc

Ellen Xiaoxia Xu

Bruce A Power

David M Sibley, Jr

Justin Curtis Palmer

David M Sibley

A computer-implemented method is described for seismic facies identification including receiving a seismic dataset representative of a subsurface volume of interest; applying a model conditioned by petrophysical classifications to the seismic dataset to identify seismic facies and generate a classified seismic image; and identifying geologic features based on the classified seismic image. The method generates seismic facies probability volumes.

System and method for classifying seismic data by integrating petrophysical data

A method is described for property estimation including receiving a seismic dataset representative of a subsurface volume of interest and a well log from a well location within the subsurface volume of interest; identifying seismic traces in the seismic dataset that correspond to the well location to obtain a subset of seismic traces; windowing the subset of seismic traces and the well log to generate windowed seismic traces and a windowed well log; multiplying the windowed seismic traces and the windowed well log by a random matrix to generate a plurality of training datasets; and training a neural network using the plurality of training datasets. The method may be executed by a computer system.

System and method for property estimation from seismic data

Systems and methods for training a model that refines estimated parameter values include computer processors and non-transitory electronic storage that stores subsurface map data sets that correspond to different subsurface volumes of interest, the system configured to obtain training data including unrefined subsurface map data sets specifying estimated parameter values of a first parameter as a function of position within corresponding subsurface volumes of interest, obtain an initial seismic mapping model, generate a conditioned seismic mapping model, and store the conditioned seismic mapping model in the electronic storage.

Systems and methods for refining estimated parameter values in seismic imaging

A computer-implemented method is described for automated seismic interpretation that includes receiving, at one or more processors, a pre-stack seismic dataset representative of the subsurface volume of interest; performing, via the one or more processors, a machine learning algorithm on the pre-stack seismic dataset to identify seismic facies based on AVA clusters, wherein the identified seismic facies include cluster sequences in depth for a plurality of spatial x-y locations in the subsurface volume; performing, via the one or more processors, seismic interpretation of the seismic dataset based on the identified seismic facies to generate a digital image of the seismic interpretation; and displaying the digital image of the seismic interpretation on a user interface.

System and method for automated seismic interpretation

Systems and methods for training a model that uses probabilities of lithologies as prior information in an inversion are disclosed. Exemplary implementations may: obtain training data, the training data including (i) subsurface map data sets, and (ii) known lithologies; obtain an initial seismic mapping model; generate a conditioned seismic mapping model by training the initial seismic mapping model; store the conditioned seismic mapping model; obtain a target subsurface map data set; apply the conditioned seismic mapping model to generate a classified lithology map data set; apply an inversion to the classified lithology map data set to generate volumes of lithologies; generate an image that represents the volumes of lithologies; display the image.

Systems and methods for using probabilities of lithologies in an inversion

A method is described for seismic facies identification including receiving a seismic dataset representative of a subsurface volume of interest; performing a machine learning algorithm on the seismic dataset to identify seismic facies and generate a classified seismic image; and identifying geologic features based on the classified seismic image. The method may be executed by a computer system.

System and method for seismic facies identification using machine learning

A method is described for time-lapse seismic imaging that may include detecting moir&#xe9; patterns in seismic images generated from time-lapse seismic data and identifying geologic features based on the moir&#xe9; patterns. The method may be executed by a computer system.

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Houston, TX, United States of America

Adam Dean Halpert