Abstract: A method includes receiving seismic data related to a subterranean formation. The method also includes receiving a selection of a property of the subterranean formation that is permitted to vary during a simulation of a model of the subterranean formation. The method also includes simulating fluid flow in the model of the subterranean formation based at least partially on the seismic data, and the selected property. The method also includes generating an updated model based at least partially upon a result of simulating the fluid flow in the model.

Abstract: Systems and methods are provided for modeling a reservoir. An exemplary method includes: receiving a reservoir model associated with a reservoir workflow process; modifying the reservoir model associated with the reservoir workflow process using an optimum time-step strategy; extracting features from the reservoir model along with first time-step sizes; generating a first set of data for devising a training set using the first time-step sizes; determining whether the selected amount of the first set of data reaches a predetermined level; triggering a real-time training using the training set and a machine learning (ML) algorithm; generating an ML model having second time-step sizes using the training set; selecting the first step-sizes or the second step-sizes based on the confidence level; sending the selected step-sizes to a simulator for processing; receiving results from the simulator that used the selected step-sizes; and determining whether results from the simulator require updating the training set.

Abstract: A method includes receiving seismic data related to a subterranean formation. The method also includes receiving a selection of a property of the subterranean formation that is permitted to vary during a simulation of a model of the subterranean formation. The method also includes simulating fluid flow in the model of the subterranean formation based at least partially on the seismic data, and the selected property. The method also includes generating an updated model based at least partially upon a result of simulating the fluid flow in the model.

Abstract: A new adjoint method for calculating and using adjoint gradients in a Reservoir Simulator comprises: calculating adjoint gradients of an objective function with respect to changes in valve settings taking into account the modeling of pressure drop and fluid flow along a wellbore, and using the adjoint gradients to calculate sensitivities of a reservoir to changes in parameterization of downhole devices and using of these sensitivities in optimal control of the wells to optimize some objective function subject to production constraints.

Abstract: A new adjoint method for calculating and using adjoint gradients in a Reservoir Simulator comprises: calculating adjoint gradients of an objective function with respect to changes in valve settings taking into account the modeling of pressure drop and fluid flow along a wellbore, and using the adjoint gradients to calculate sensitivities of a reservoir to changes in parameterization of downhole devices and using of these sensitivities in optimal control of the wells to optimize some objective function subject to production constraints.