Abstract:
A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.
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 a common equation of state (EOS) model for each of a plurality of reservoirs. The black oil data representing fluids within each reservoir. At least one multi-dimensional black oil table representing a mix of the fluid components to be produced from each of the plurality of reservoirs via the common surface network is generated based on the EOS model that matches the one or more black oil tables for each reservoir. Properties of the fluids in the mix during a simulation of fluid production from the plurality of reservoirs are calculated based on the generated multi-dimensional black oil table for each reservoir.
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:
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.