Stable nonlinear predictive operator based on neural network, genetic algorithm and controlled gradient method

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2015Stable nonlinear predictive operator based on neural network, genetic algorithm and controlled gradient methodAuthors: Alexander KobrunovIvan Priezzhev*Alexander KobrunovUkhta State Technical UniversitySearch for more papers by this author and Ivan Priezzhev*SchlumbergerSearch for more papers by this authorhttps://doi.org/10.1190/segam2015-5720421.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract We propose a new technology to building a stable nonlinear predictive operator based on a combination of a neural network, a genetic algorithm, and the controlled gradient method. The main idea of the proposed technology lies in application of the combination of stochastic and deterministic approaches during construction of the operator in the learning stage. This operator can be used to predict different variables in spatial or/and time coordinates when their deterministic nature is unknown or it is impossible to apply direct inversion. It is assumed that there exists a possibility to predict some variables via other measured variables due to the existing unknown nonlinear dependence. For example, it is necessary to predict possible oil and gas production rates on a map (sweet spot) using different geological and geophysical maps (porosity, density, seismic attributes, gravity, magnetic, etc.) and based on initial oil and gas production rates for several wells in the area of interest. At the first stage, a learning set is used to build an operator, and, at the second stage, the operator is applied to predict the target parameters. Keywords: neural networks, unconventional, reservoir characterization, production, optimizationPermalink: https://doi.org/10.1190/segam2015-5720421.1FiguresReferencesRelatedDetailsCited byCo-optimization of CO2-EOR and Storage Processes under Geological UncertaintyEnergy Procedia, Vol. 114Optimum design of CO2 storage and oil recovery under geological uncertaintyApplied Energy, Vol. 195 SEG Technical Program Expanded Abstracts 2015ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2015 Pages: 5634 publication data© 2015 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 19 Aug 2015 CITATION INFORMATION Alexander Kobrunov and Ivan Priezzhev*, (2015), "Stable nonlinear predictive operator based on neural network, genetic algorithm and controlled gradient method," SEG Technical Program Expanded Abstracts : 2941-2946. https://doi.org/10.1190/segam2015-5720421.1 Plain-Language Summary Keywordsneural networksunconventionalreservoir characterizationproductionoptimizationPDF DownloadLoading ...