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1. 发明申请

WO2012015518A3 METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED SIMULATION OF FLOW 审中-公开
公开(公告)号：WO2012015518A3
公开(公告)日：2012-02-02
申请号：PCT/US2011/037178
申请日：2011-05-19
申请人： EXXONMOBIL UPSTREAM RESEARCH COMPANY , USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui , YANG, Yahan
发明人： USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui , YANG, Yahan
IPC分类号： G06G7/48
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.

2. 发明申请

WO2012015517A1 METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED SIMULATION OF FLOW 审中-公开
标题翻译：基于机器学习的流程模拟方法与系统
公开(公告)号：WO2012015517A1
公开(公告)日：2012-02-02
申请号：PCT/US2011/037177
申请日：2011-05-19
申请人： EXXONMOBIL UPSTREAM RESEARCH COMPANY , USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui , YANG, Yahan
发明人： USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui , YANG, Yahan
IPC分类号： G06G7/48
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。

3. 发明申请

WO2012015516A8 METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED SIMULATION OF FLOW 审中-公开
公开(公告)号：WO2012015516A8
公开(公告)日：2012-02-02
申请号：PCT/US2011/037174
申请日：2011-05-19
申请人： EXXONMOBIL UPSTREAM RESEARCH COMPANY , USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui , YANG, Yahan
发明人： USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui , YANG, Yahan
IPC分类号： G06G7/48
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.

4. 发明申请

WO2012015515A1 METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED SIMULATION OF FLOW 审中-公开
标题翻译：基于机器学习的流程模拟方法与系统
公开(公告)号：WO2012015515A1
公开(公告)日：2012-02-02
申请号：PCT/US2011/037168
申请日：2011-05-19
申请人： EXXONMOBIL UPSTREAM RESEARCH COMPANY , USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-Hui , YANG, Yahan
发明人： USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-Hui , YANG, Yahan
IPC分类号： G06G7/48
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。

5. 发明公开

EP2599030A1 METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED SIMULATION OF FLOW 审中-公开
标题翻译：基于机器学习的流动模拟方法和系统
公开(公告)号：EP2599030A1
公开(公告)日：2013-06-05
申请号：EP11812891.7
申请日：2011-05-19
申请人： ExxonMobil Upstream Research Company
发明人： USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui , YANG, Yahan
IPC分类号： G06G7/48
CPC分类号： E21B43/00 , G01V99/005 , 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 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：提供了一种用于建模碳氢化合物储层的方法，其包括生成包括多个粗栅格单元的储层模型。该方法包括生成与粗糙网格单元中的一个对应的精细网格模型并且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。机器学习算法可以用于基于该组训练参数生成粗网格单元的相位导磁率的粗略近似。可以使用为粗网格单元生成的有效相位渗透率的粗尺度近似来模拟碳氢化合物储层。该方法还包括至少部分地基于模拟结果在非暂时性计算机可读介质中生成物理碳氢化合物储层的数据表示。

6. 发明公开

EP2599029A1 METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED SIMULATION OF FLOW 审中-公开
标题翻译：方法和系统有关基于机器学习的流场模拟
公开(公告)号：EP2599029A1
公开(公告)日：2013-06-05
申请号：EP11812890.9
申请日：2011-05-19
申请人： ExxonMobil Upstream Research Company
发明人： USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-Hui , YANG, Yahan
IPC分类号： G06G7/48
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.

7. 发明公开

EP2599031A1 METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED SIMULATION OF FLOW 审中-公开
标题翻译：基于机器学习的流动模拟方法和系统
公开(公告)号：EP2599031A1
公开(公告)日：2013-06-05
申请号：EP11812892.5
申请日：2011-05-19
申请人： ExxonMobil Upstream Research Company , Yang, Yahan
发明人： YANG, Yahan , USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui
IPC分类号： G06G7/48
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：提供了一种用于建模碳氢化合物储层的方法，其包括生成包括多个子区域的储层模型。使用训练模拟模拟至少一个子区域以获得包括至少一个子区域的状态变量和边界条件的一组训练参数。机器学习算法用于基于该组训练参数来近似矩阵方程的逆运算符，该矩阵方程为通过多孔介质的流体流提供解决方案。可以使用近似于至少一个子区域的逆算子来模拟碳氢化合物储层。该方法还包括可以至少部分地基于模拟结果在非暂时性计算机可读介质中生成物理碳氢化合物储层的数据表示。

8. 发明公开

EP2599023A2 METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED SIMULATION OF FLOW 审中-公开
标题翻译：基于机器学习的流动模拟方法和系统
公开(公告)号：EP2599023A2
公开(公告)日：2013-06-05
申请号：EP11812893.3
申请日：2011-05-19
申请人： ExxonMobil Upstream Research Company
发明人： USADI, Adam , LI, Dachang , PARASHKEVOV, Rossen , TEREKHOV, Sergey, A. , WU, Xiao-hui , YANG, Yahan
IPC分类号： G06F19/00
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：提供了一种用于建模碳氢化合物储层的方法，其包括生成具有多个粗栅格单元的储层模型。生成多个细网格模型，其中每个细网格模型对应于围绕通量接口的多个粗网格单元中的一个。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，包括围绕通量接口的每个粗网格单元处的电势和通量接口上的通量。机器学习算法用于生成本构关系，该关系为通过通量接口的流体流动提供了解决方案。该方法还包括使用本构关系来模拟碳氢化合物储层，并且基于模拟的结果在非暂时性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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