Abstract: System and methods of modeling fluids in a simulation of fluid production in a multi-reservoir system with a common surface network are provided. Pressure-volume-temperature (PVT) data is determined for fluids in each of a plurality of reservoirs coupled to the common surface network. A shared equation of state (EOS) characterization representing each of the fluids across the plurality of reservoirs is generated based on the corresponding PVT data. Data representing properties of the fluids in each reservoir is calculated based on the shared EOS characterization of the fluids. When the calculated data is determined not to match the PVT data associated with the fluids in each reservoir, to the shared EOS characterization is adjusted based on a difference between the calculated data and the PVT data.

Abstract: In some embodiments, systems, methods, and articles may operate to compute, in parallel, to determine values of unknowns in network equations associated with a network of sub-surface wells and at least one surface facility, for intra-well subdivisions of the network, and then for inter-well subdivisions of the network, wherein the computing is based on default values of the unknowns, or prior determined values of the unknowns. Additional activities may include constructing a distributed Jacobian matrix having portions comprising coefficients of the unknowns distributed among a number of processors, wherein each of the portions is distributed to a particular one of the processors previously assigned to corresponding ones of the subdivisions. The Jacobian matrix may be factored to provide factors and eliminate some of the unknowns. Back-solving is used to determine remaining unsolved ones of the unknowns, using the factors. Additional apparatus, systems, and methods are described.

Abstract: System and methods of simulating fluid production in a multi-reservoir system with a common surface network are provided. Black oil data is matched with an equation of state (EOS) model representing different fluid components of each reservoir in the multi-reservoir system. The black oil data is converted into a two-component black oil model for each reservoir, based on the EOS model. Fluid production in the multi-reservoir system is simulated for at least one simulation point in the common surface network, based in part on the two-component black oil model of each reservoir. When fluids produced at the simulation point are determined to be from different reservoirs, properties of the fluids are calculated based on weaved EOS models of the different reservoirs. Otherwise, properties of the fluids are calculated using the two-component black oil model for the reservoir from which the fluids are produced.

Abstract: A method for implementing a reservoir simulator is described. The method comprises developing training data by performing a calculation on an initial set of input data relating to reservoir conditions to obtain corresponding output data; training an artificial neural network (“ANN”) to perform the calculation using the training data; and using the trained ANN to perform the calculation on a second set of input data to obtain corresponding output data for use by the reservoir simulator in performing simulations.

Abstract: System and methods of simulating fluid production in a multi-reservoir system with a common surface network are presented. An equation of state (EOS) characterization of fluids is matched with a delumped EOS model representing different components of the fluids for each reservoir within the multi-reservoir system. Fluid production in the multi-reservoir system is simulated for at least one simulation point in the common surface network, based in part on the delumped EOS model for each reservoir. If the fluids produced during the simulation at the simulation point are mixed fluids from different reservoirs, one or more interpolation tables representing the mixed fluids are generated and properties of the mixed fluids are calculated based on the generated interpolation tables. Otherwise, the properties of the fluids are calculated using the delumped EOS model corresponding to the reservoir from which the fluids are produced.

Abstract: System and methods of modeling fluids in a simulation of fluid production in a multi-reservoir system with a common surface network are provided. Pressure-volume-temperature (PVT) data is determined for fluids in each of a plurality of reservoirs coupled to the common surface network. A shared equation of state (EOS) characterization representing each of the fluids across the plurality of reservoirs is generated based on the corresponding PVT data. Data representing properties of the fluids in each reservoir is calculated based on the shared EOS characterization of the fluids. When the calculated data is determined not to match the PVT data associated with the fluids in each reservoir, to the shared EOS characterization is adjusted based on a difference between the calculated data and the PVT data.

Abstract: System and methods of simulating fluid production in a multi-reservoir system with a common surface network are presented. An equation of state (EOS) characterization of fluids is matched with a delumped EOS model representing different components of the fluids for each reservoir within the multi-reservoir system. Fluid production in the multi-reservoir system is simulated for at least one simulation point in the common surface network, based in part on the delumped EOS model for each reservoir. If the fluids produced during the simulation at the simulation point are mixed fluids from different reservoirs, one or more interpolation tables representing the mixed fluids are generated and properties of the mixed fluids are calculated based on the generated interpolation tables. Otherwise, the properties of the fluids are calculated using the delumped EOS model corresponding to the reservoir from which the fluids are produced.

Abstract: System and methods of simulating fluid production in a multi-reservoir system with a common surface network are provided. Black oil data is matched with an equation of state (EOS) model representing different fluid components of each reservoir in the multi-reservoir system. The black oil data is converted into a two-component black oil model for each reservoir, based on the EOS model. Fluid production in the multi-reservoir system is simulated for at least one simulation point in the common surface network, based in part on the two-component black oil model of each reservoir. When fluids produced at the simulation point are determined to be from different reservoirs, properties of the fluids are calculated based on weaved EOS models of the different reservoirs. Otherwise, properties of the fluids are calculated using the two-component black oil model for the reservoir from which the fluids are produced.

Abstract: A method for implementing a reservoir simulator is described. The method comprises developing training data by performing a calculation on an initial set of input data relating to reservoir conditions to obtain corresponding output data; training an artificial neural network (“ANN”) to perform the calculation using the training data; and using the trained ANN to perform the calculation on a second set of input data to obtain corresponding output data for use by the reservoir simulator in performing simulations.

Abstract: Systems and methods for running a multi-thread simulation and evaluating the results during a simulation run to select the best thread.

Abstract: Systems and methods for determining active constraints in a surface facility network, which include the use of slack variables and multipliers in system equations to eliminate the extraneous (inactive) constraints.

Abstract: Systems and methods for determining active constraints in a surface facility network, which include the use of slack variables and multipliers in system equations to eliminate the extraneous (inactive) constraints.

Abstract: Systems and methods for running a multi-thread simulation and evaluating the results during a simulation run to select the best thread.

Abstract: Systems and methods for determining active constraints in a surface facility network, which include the use of slack variables and multipliers in system equations to eliminate the extraneous (inactive) constraints.

Abstract: Systems and methods for determining active constraints in a surface facility network, which include the use of slack variables and multipliers in system equations to eliminate the extraneous (inactive) constraints.