DATA-DRIVEN DOMAIN CONVERSION USING MACHINE LEARNING TECHNIQUES

Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the traini...

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Bibliographic Details
Main Authors Roy, Samiran, Chaki, Soumi, Vallabhaneni, Sridharan
Format Patent
LanguageEnglish
Published 26.02.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DETECTING MASSES OR OBJECTS
GEOPHYSICS
GRAVITATIONAL MEASUREMENTS
MEASURING
PHYSICS
TESTING
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Summary:Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data, and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.
Bibliography:Application Number: NO20210000264