Abstract: A method and an apparatus for protecting privacy of an ophthalmic patient and a storage medium are disclosed. The method includes: acquiring an examination video of the ophthalmic patient; extracting image features of each frame of images in the examination video, performing region division on each frame of the images according to position information of organs in the image features, and obtaining a region to be precisely reconstructed and a region to be roughly reconstructed according to a region division result; performing three-dimensional (3D) reconstruction on the region to be precisely reconstructed and the region to be roughly reconstructed, to obtain 3D reconstruction data corresponding to each frame of the images; and rendering all the 3D reconstruction data into a 3D reconstruction video.

Abstract: A semi-elimination methodology for simulating high flow features in a reservoir and wells is disclosed. The reservoir and wells may be divided into a plurality of cells, including small cells in wells and the reservoir and bulk cells in the bulk of the reservoir, where the small cells are smaller (e.g., by pore volume) than the bulk cells. Processing of all of the cells, including all of the small cells, may be too computationally expensive, particularly when processing is iterative. In that regard, at least some of the small cells are partly processed in an iteration, such as for flow rates, compositions, or flow derivatives. After which, some or all of the small cells are eliminated from further processing in the iteration. In that way, high flow features in a reservoir and wells may be simulated effectively.

Abstract: A method and system are described to form a subsurface model for use in hydrocarbon operations. The method and system utilize stability proxies with the subsurface models, such as simulation models, and to manage the reservoir simulation.

Abstract: 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.

Abstract: 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.

Abstract: A method is presented for modeling reservoir properties. The method includes an auxiliary time-stepping procedure of the reservoir between an old time and a new time, and calculating a plurality of masses explicitly. A plurality of phase component densities is updated linearly from the plurality of masses. A plurality of saturation changes is calculated based on the plurality of masses. A plurality of phase flow rates is updated based on the plurality of saturation changes, a plurality of phase flow rates at the old time, and a plurality of saturation derivatives of the phase flow rates at the old time. A plurality of component flow rates may be calculated based on the updated plurality of phase component densities and the plurality of phase flow rates. The method also includes a formulation method based on the auxiliary time stepping procedure.

Abstract: A method and system are described to form a subsurface model for use in hydrocarbon operations. The method and system utilize stability proxies with the subsurface models, such as simulation models, and to manage the reservoir simulation.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method is presented for modeling reservoir properties. The method includes an auxiliary time-stepping procedure of the reservoir between an old time and a new time, and calculating a plurality of masses explicitly. A plurality of phase component densities is updated linearly from the plurality of masses. A plurality of saturation changes is calculated based on the plurality of masses. A plurality of phase flow rates is updated based on the plurality of saturation changes, a plurality of phase flow rates at the old time, and a plurality of saturation derivatives of the phase flow rates at the old time. A plurality of component flow rates may be calculated based on the updated plurality of phase component densities and the plurality of phase flow rates. The method also includes a formulation method based on the auxiliary time stepping procedure.

Abstract: 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.

Abstract: Methods and systems to reduce or eliminate numerical oscillations in solutions that occur when using conventional MPFA when modeling flow in a reservoir are provided. The technique may be referred to as enriched multi-point flux approximation (EMPFA) and may be used to improve the consistency and accuracy in constructing pressure interpolations in cells for the purpose of determining flux equations used in predicting flow in a reservoir.

Abstract: Methods and systems to reduce or eliminate numerical oscillations in solutions that occur when using conventional MPFA when modeling flow in a reservoir are provided. The technique may be referred to as enriched multi-point flux approximation (EMPFA) and may be used to improve the consistency and accuracy in constructing pressure interpolations in cells for the purpose of determining flux equations used in predicting flow in a reservoir.