Abstract:
A method and system for automating a reservoir simulation. The method includes identifying a simulation parameter associated with a simulation resource to perform a computer-based reservoir simulation using reservoir data associated with a subterranean reservoir and configuring the simulation resource using a simulation engine to include the simulation parameter for performing the reservoir simulation with a reduced likelihood of simulation failure. The method also includes performing the reservoir simulation using the configured simulation resource and the reservoir data to generate reservoir simulation data and evaluate the reservoir.
Abstract:
An intelligent data management system leverages heterogeneous database technologies and cloud technology to manage data for reservoir simulations across the lifetime of a corresponding energy asset(s) and facilitates access of that data by various consumers despite changing compute platforms and adoption of open source paradigms. The intelligent data management system identifies the various data units that constitute a reservoir simulation output for storage and organization. The intelligent data management system organizes the constituent data units across a file system and object database based on correspondence with different simulation run attributes: project, study, and model. The intelligent data management system also learns to specify or guide configuration of simulation runs.
Abstract:
A method for determining active constraint equations in a network of wells and surface facilities includes constructing at least one constraint equation for a connection in the network. Each constraint equation includes a respective slack variable and a respective slack variable multiplier. The method further includes constructing a base equation for the connection. The base equation includes the respective slack variable and another respective slack variable multiplier. The method further includes introducing a pseudo slack variable for another connection in the network such that a Schur complement, of a matrix of constraint and base equations dependent only on slack variable multipliers, is sparse. The method further includes solving for each respective slack variable using the Shur complement matrix. The method further includes adjusting a variable parameter of the network using results from solving for each respective slack variable.
Abstract:
In some embodiments, a system, as well as a method and an article, may operate to generate a first matrix, based on equations that model a reservoir, that includes mass conservation and volume balance information for grid blocks in the reservoir; to generate a second matrix, based on the first matrix, that includes saturation information and pressure information of each grid block; to remove the saturation information from the second matrix to generate a third matrix that includes only pressure information; to solve the third matrix to generate a first pressure solution; to solve the second matrix based on the first pressure solution to generate a first saturation solution and a second pressure solution; and to use the first saturation solution and the second pressure solution to generate a solution of the first matrix. Additional apparatus, systems, and methods are disclosed.
Abstract:
Systems and methods for cloud-based management of reservoir simulation projects are provided. A cloud-based application server may receive from a client device over the communication network information defining a reservoir simulation project for a wellsite in a hydrocarbon producing field. The reservoir simulation project may include at least one reservoir simulation job to be performed by the cloud-based application server. The information may include one or more parameters for the reservoir simulation job. The cloud-based application server may perform the reservoir simulation job according to the one or more parameters. The cloud-based application server may provide results of the simulation job to the client device over the communication network for display within a graphical user interface (GUI) provided at the client device for a cloud-based reservoir simulation application executable by the application server.
Abstract:
In some embodiments, a system, as well as a method and an article, may operate to generate a first matrix, based on equations that model a reservoir, that includes mass conservation and volume balance information for grid blocks in the reservoir; to generate a second matrix, based on the first matrix, that includes saturation information and pressure information of each grid block; to remove the saturation information from the second matrix to generate a third matrix that includes only pressure information; to solve the third matrix to generate a first pressure solution; to solve the second matrix based on the first pressure solution to generate a first saturation solution and a second pressure solution; and to use the first saturation solution and the second pressure solution to generate a solution of the first matrix. Additional apparatus, systems, and methods are disclosed.
Abstract:
An intelligent data management system leverages heterogeneous database technologies and cloud technology to manage data for reservoir simulations across the lifetime of a corresponding energy asset(s) and facilitates access of that data by various consumers despite changing compute platforms and adoption of open source paradigms. The intelligent data management system identifies the various data units that constitute a reservoir simulation output for storage and organization. The intelligent data management system organizes the constituent data units across a file system and object database based on correspondence with different simulation run attributes: project, study, and model. The intelligent data management system also learns to specify or guide configuration of simulation runs.
Abstract:
A method for determining active constraint equations in a network of wells and surface facilities includes constructing at least one constraint equation for a connection in the network. Each constraint equation includes a respective slack variable and a respective slack variable multiplier. The method further includes constructing a base equation for the connection. The base equation includes the respective slack variable and another respective slack variable multiplier. The method further includes introducing a pseudo slack variable for another connection in the network such that a Schur complement, of a matrix of constraint and base equations dependent only on slack variable multipliers, is sparse. The method further includes solving for each respective slack variable using the Shur complement matrix. The method further includes adjusting a variable parameter of the network using results from solving for each respective slack variable.