Patents by Inventor Rossen Parashkevov
Rossen Parashkevov has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10198535Abstract: There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model comprising a plurality of sub regions. At least one of the sub regions is simulated using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the at least one sub region. A machine learning algorithm is used to approximate, based on the set of training parameters, an inverse operator of a matrix equation that provides a solution to fluid flow through a porous media. The hydrocarbon reservoir can be simulated using the inverse operator approximated for the at least one sub region. The method also includes generating a data representation of a physical hydrocarbon reservoir can be generated in a non-transitory, computer-readable, medium based, at least in part, on the results of the simulation.Type: GrantFiled: May 19, 2011Date of Patent: February 5, 2019Assignee: ExxonMobil Upstream Research CompanyInventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Sergey A. Terekhov, Xiaohui Wu, Yahan Yang
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Patent number: 10087721Abstract: There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model that has a plurality of sub regions. A solution surrogate is obtained for a sub region by searching a database of existing solution surrogates to obtain an approximate solution surrogate based on a comparison of physical, geometrical, or numerical parameters of the sub region with physical, geometrical, or numerical parameters associated with the existing surrogate solutions in the database. If an approximate solution surrogate does not exist in the database, the sub region is simulated using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the sub region. A machine learning algorithm is used to obtain a new solution surrogate based on the set of training parameters. The hydrocarbon reservoir can be simulated using the solution surrogate obtained for the at least one sub region.Type: GrantFiled: May 19, 2011Date of Patent: October 2, 2018Assignee: ExxonMobil Upstream Research CompanyInventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Sergey A. Terekhov, Xiaohui Wu, Yahan Yang
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Patent number: 9626466Abstract: A variable discretization method for general multiphase flow simulation in a producing hydrocarbon reservoir. For subsurface regions for which a regular or Voronoi computational mesh is suitable, a finite difference/finite volume method (“FDM”) is used to discretize numerical solution of the differential equations governing fluid flow (101). For subsurface regions with more complex geometries, a finite element method (“FEM”) is used. The invention combines FDM and FEM in a single computational framework (102). Mathematical coupling at interfaces between different discretization regions is accomplished by decomposing individual phase velocity into an averaged component and a correction term.Type: GrantFiled: August 12, 2011Date of Patent: April 18, 2017Assignee: ExxonMobil Upstream Research CompanyInventors: Yahan Yang, Linfeng Bi, Weidong Guo, Rossen Parashkevov, Xiaohui Wu
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Patent number: 9187984Abstract: There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model that has a plurality of coarse grid cells. A plurality of fine grid models is generated, wherein each fine grid model corresponds to one of the plurality of coarse grid cells that surround a flux interface. The method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation.Type: GrantFiled: May 19, 2011Date of Patent: November 17, 2015Assignee: ExxonMobil Upstream Research CompanyInventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Sergey A. Terekhov, Xiaohui Wu, Yahan Yang
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Patent number: 9058445Abstract: A method is presented for modeling reservoir properties. The method includes constructing a coarse computational mesh for the reservoir. The coarse computational mesh comprises a plurality of cells. The method further includes determining a plurality of flows for each of the plurality of cells based on Dirichlet boundary conditions. Additionally, the method includes determining a solution to a coarse pressure equation for the reservoir based on the plurality of flows.Type: GrantFiled: May 23, 2011Date of Patent: June 16, 2015Assignee: ExxonMobil Upstream Research CompanyInventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Xiaohui Wu, Yahan Yang
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Publication number: 20130231907Abstract: A variable discretization method for general multiphase flow simulation in a producing hydrocarbon reservoir. For subsurface regions for which a regular or Voronoi computational mesh is suitable, a finite difference/finite volume method (“FDM”) is used to discretize numerical solution of the differential equations governing fluid flow (101). For subsurface regions with more complex geometries, a finite element method (“FEM”) is used. The invention combines FDM and FEM in a single computational framework (102). Mathematical coupling at interfaces between different discretization regions is accomplished by decomposing individual phase velocity into an averaged component and a correction term.Type: ApplicationFiled: August 12, 2011Publication date: September 5, 2013Inventors: Yahan Yang, Linfeng Bi, Weidong Guo, Rossen Parashkevov, Xiaohui Wu
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Publication number: 20130166264Abstract: A method is presented for modeling reservoir properties. The method includes constructing a coarse computational mesh for the reservoir. The coarse computational mesh comprises a plurality of cells. The method further includes determining a plurality of flows for each of the plurality of cells based on Dirichlet boundary conditions. Additionally, the method includes determining a solution to a coarse pressure equation for the reservoir based on the plurality of flows.Type: ApplicationFiled: May 23, 2011Publication date: June 27, 2013Inventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Xiaohui Wu, Yahan Yang
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Publication number: 20130118736Abstract: There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model that has a plurality of coarse grid cells. A plurality of fine grid models is generated, wherein each fine grid model corresponds to one of the plurality of coarse grid cells that surround a flux interface. The method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation.Type: ApplicationFiled: May 19, 2011Publication date: May 16, 2013Inventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Sergey A. Terekhov, Xiaohui Wu, Yahan Yang
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Publication number: 20130096900Abstract: There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model comprising a plurality of sub regions. At least one of the sub regions is simulated using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the at least one sub region. A machine learning algorithm is used to approximate, based on the set of training parameters, an inverse operator of a matrix equation that provides a solution to fluid flow through a porous media. The hydrocarbon reservoir can be simulated using the inverse operator approximated for the at least one sub region. The method also includes generating a data representation of a physical hydrocarbon reservoir can be generated in a non-transitory, computer-readable, medium based, at least in part, on the results of the simulation.Type: ApplicationFiled: May 19, 2011Publication date: April 18, 2013Inventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Sergey A. Terekhov, Xiaohui Wu, Yahan Yang
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Publication number: 20130096899Abstract: There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model comprising a plurality of coarse grid cells. The method includes generating a fine grid model corresponding to one of the coarse grid cells and simulating the fine grid model using a training simulation to generate a set of training parameters comprising boundary conditions of the coarse grid cell. A machine learning algorithm may be used to generate, based on the set of training parameters, a coarse scale approximation of a phase permeability of the coarse grid cell. The hydrocarbon reservoir can be simulated using the coarse scale approximation of the effective phase permeability generated for the coarse grid cell. The method also includes generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable, medium based at least in part on the results of the simulation.Type: ApplicationFiled: May 19, 2011Publication date: April 18, 2013Applicant: Exxonmobile Upstream Research CompanyInventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Sergey A. Terekhov, Xiaohui Wu, Yahan Yang
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Publication number: 20130096898Abstract: There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model that has a plurality of sub regions. A solution surrogate is obtained for a sub region by searching a database of existing solution surrogates to obtain an approximate solution surrogate based on a comparison of physical, geometrical, or numerical parameters of the sub region with physical, geometrical, or numerical parameters associated with the existing surrogate solutions in the database. If an approximate solution surrogate does not exist in the database, the sub region is simulated using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the sub region. A machine learning algorithm is used to obtain a new solution surrogate based on the set of training parameters. The hydrocarbon reservoir can be simulated using the solution surrogate obtained for the at least one sub region.Type: ApplicationFiled: May 19, 2011Publication date: April 18, 2013Applicant: Exxonmobile Upstream Research CompanyInventors: Adam Usadi, Dachang Li, Rossen Parashkevov, Sergey A. Terekhov, Xiaohui Wu, Yahan Yang
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Publication number: 20120158389Abstract: The present techniques disclose methods and systems for rapidly evaluating multiple models using multilevel surrogates (for example, in two or more levels). These surrogates form a hierarchy in which surrogate accuracy increases with its level. At the highest level, the surrogate becomes an accurate model, which may be referred to as a full-physics model (FPM). The higher level surrogates may be used to efficiently train the low level surrogates (more specifically, the lowest level surrogate in most applications), reducing the amount of computing resources used. The low level surrogates are then used to evaluate the entire parameter space for various purposes, such as history matching, evaluating the performance of a hydrocarbon reservoir, and the like.Type: ApplicationFiled: July 28, 2010Publication date: June 21, 2012Applicant: Exxonmobile Upstream Research CompanyInventors: Xiaohui Wu, Dachang Li, Rossen Parashkevov, Adam K. Usadi, Yahan Yang