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公开(公告)号：US10087721B2
公开(公告)日：2018-10-02
申请号：US13805647
申请日：2011-05-19
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
摘要： 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.
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公开(公告)号：US09187984B2
公开(公告)日：2015-11-17
申请号：US13805650
申请日：2011-05-19
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
CPC分类号： E21B41/0092 , E21B41/00 , E21B43/00 , G01V99/005 , G06F17/5009 , G06F17/5018 , G06F2217/16 , G06N3/0427
摘要： 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.
摘要翻译： 提供了一种用于对烃储层进行建模的方法，其包括产生具有多个粗网格单元的储层模型。 产生多个精细网格模型，其中每个细网格模型对应于围绕磁通界面的多个粗网格单元之一。 该方法还包括使用训练模拟来模拟多个精细网格模型，以获得一组训练参数，包括在通量界面周围的每个粗网格单元处的电位和穿过磁通界面的通量。 机器学习算法用于产生本构关系，为通过流体界面的流体流提供解决方案。 该方法还包括使用本构关系模拟烃储层，并且基于模拟结果在非暂时的计算机可读介质中生成物理烃储层的数据表示。
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公开(公告)号：US10198535B2
公开(公告)日：2019-02-05
申请号：US13805649
申请日：2011-05-19
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
摘要： 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.
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公开(公告)号：US20130096899A1
公开(公告)日：2013-04-18
申请号：US13805648
申请日：2011-05-19
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
CPC分类号： E21B43/00 , G01V99/005 , G06F17/5018 , G06F2217/16 , G06N3/0427 , G06N20/00
摘要： 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.
摘要翻译： 提供了一种用于对烃储层进行建模的方法，其包括产生包括多个粗网格单元的储层模型。 该方法包括生成对应于粗网格单元之一的精细网格模型，并使用训练模拟来模拟精细网格模型，以生成包括粗网格单元的边界条件的一组训练参数。 可以使用机器学习算法基于训练参数集合来生成粗网格单元的相位磁导率的粗略近似。 可以使用为粗网格单元生成的有效相位磁导率的粗略近似来模拟烃储层。 该方法还包括至少部分地基于模拟的结果，在非暂时的计算机可读介质中生成物理碳氢化合物储层的数据表示。
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公开(公告)号：US20130096900A1
公开(公告)日：2013-04-18
申请号：US13805649
申请日：2011-05-19
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
IPC分类号： G06F17/50
CPC分类号： G06F17/5009 , G06F17/5018 , G06N3/0427
摘要： 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.
摘要翻译： 提供了一种用于对烃储层进行建模的方法，其包括产生包括多个子区域的储层模型。 使用训练模拟来模拟至少一个子区域，以获得包括所述至少一个子区域的状态变量和边界条件的训练参数集合。 机器学习算法用于基于训练参数集来近似矩阵方程的逆算子，其提供通过多孔介质的流体流动的解决方案。 可以使用对于至少一个子区域近似的逆算子来模拟烃储层。 该方法还包括生成物理碳氢化合物储集层的数据表示可以至少部分地基于模拟的结果以非暂时的，计算机可读的，基于媒体的方式产生。
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公开(公告)号：US20130096898A1
公开(公告)日：2013-04-18
申请号：US13805647
申请日：2011-05-19
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
CPC分类号： E21B43/00 , G01V1/282 , G01V99/005 , G01V2210/66 , G06F17/5018 , G06F2217/16 , G06N3/0427 , G06N99/005
摘要： 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.
摘要翻译： 提供了一种用于对烃储层进行建模的方法，其包括产生具有多个子区域的储层模型。 通过搜索现有解决方案代理的数据库来获得一个解决方案代用品，以便通过将子区域的物理，几何或数值参数与物理，几何或数值参数与 现有的代理解决方案在数据库中。 如果数据库中不存在近似的解决方案代用品，则使用训练模拟来模拟子区域，以获得包括状态变量和子区域的边界条件的训练参数的集合。 基于训练参数的集合，使用机器学习算法获得新的解决方案代理。 可以使用为至少一个子区域获得的溶液替代物来模拟烃储存器。
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公开(公告)号：US20130118736A1
公开(公告)日：2013-05-16
申请号：US13805650
申请日：2011-05-19
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Sergey A. Terekhov , Xiaohui Wu , Yahan Yang
CPC分类号： E21B41/0092 , E21B41/00 , E21B43/00 , G01V99/005 , G06F17/5009 , G06F17/5018 , G06F2217/16 , G06N3/0427
摘要： 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.
摘要翻译： 提供了一种用于对烃储层进行建模的方法，其包括产生具有多个粗网格单元的储层模型。 产生多个精细网格模型，其中每个细网格模型对应于围绕磁通界面的多个粗网格单元之一。 该方法还包括使用训练模拟来模拟多个精细网格模型，以获得一组训练参数，包括在通量界面周围的每个粗网格单元处的电位和穿过磁通界面的通量。 机器学习算法用于产生本构关系，为通过流体界面的流体流提供解决方案。 该方法还包括使用本构关系模拟烃储层，并且基于模拟结果在非暂时的计算机可读介质中生成物理烃储层的数据表示。
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公开(公告)号：US20130166264A1
公开(公告)日：2013-06-27
申请号：US13805651
申请日：2011-05-23
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Xiaohui Wu , Yahan Yang
IPC分类号： G06F17/50
CPC分类号： G06F17/5009 , G06F2217/16
摘要： 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.
摘要翻译： 提出了一种用于建模储层性质的方法。 该方法包括为储层构建粗计算网格。 粗计算网格包括多个单元。 该方法还包括基于Dirichlet边界条件来确定多个小区中的每一个的多个流。 另外，该方法包括基于多个流量确定储层的粗压方程的解。
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公开(公告)号：US09058445B2
公开(公告)日：2015-06-16
申请号：US13805651
申请日：2011-05-23
申请人： Adam Usadi , Dachang Li , Rossen Parashkevov , Xiaohui Wu , Yahan Yang
发明人： Adam Usadi , Dachang Li , Rossen Parashkevov , Xiaohui Wu , Yahan Yang
CPC分类号： G06F17/5009 , G06F2217/16
摘要： 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.
摘要翻译： 提出了一种用于建模储层性质的方法。 该方法包括为储层构建粗计算网格。 粗计算网格包括多个单元。 该方法还包括基于Dirichlet边界条件来确定多个小区中的每一个的多个流。 另外，该方法包括基于多个流量确定储层的粗压方程的解。
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10.发明申请公开(公告)号：US20120158389A1
公开(公告)日：2012-06-21
申请号：US13392035
申请日：2010-07-28
申请人： Xiaohui Wu , Dachang Li , Rossen Parashkevov , Adam K. Usadi , Yahan Yang
发明人： Xiaohui Wu , Dachang Li , Rossen Parashkevov , Adam K. Usadi , Yahan Yang
CPC分类号： G06F17/5009 , G16C20/30
摘要： 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.
摘要翻译： 本技术公开了使用多级代理（例如，在两个或更多个级别）中快速评估多个模型的方法和系统。 这些代理形成了代理精度随着其水平而增加的等级。 在最高级别，代理成为一个准确的模型，可以被称为全物理模型（FPM）。 可以使用较高级别的代理来有效地训练低级别代理（更具体地说，在大多数应用中是最低级替代），减少了所使用的计算资源的数量。 然后，使用低级替代物来评估用于各种目的的整个参数空间，例如历史匹配，评估烃储层的性能等。
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