...Abstract Abstract Compositional reservoir simulation is essential to represent the complex interactions associated with gas flooding processes. Generally, an improved description of such small-scale phenomena requires the use of very detailed reservoir models, which impact the computational...

...Abstract Abstract Although it is possible to apply traditional optimization algorithms to determine the Pareto front of a multi-objective optimization problem, the computational cost is extremely high, when the objective function evaluation requires solving a complex reservoir simulation...

... & gas time step estimator solver equation iteration error estimator implicit solver error component denote newton iteration enhanced recovery reservoir simulation posteriori error estimate error estimate vohralík stopping criteria Introduction One of the major challenges...

... to be powerful tools for estimating poorly known parameters for reservoir simulation ( Nævdal et al. 2003 ; Aanonsen et al. 2009 ; Kiærr et al. 2020), and are now well-established tools for history matching. EnKF methods are based on Bayesian statistics. We assume a Gaussian prior distribution...

... prediction realization machine learning reservoir simulation fluid dynamics optimization algorithm permeability bayesian mcmc lstm petroleum engineer Introduction Accurate modeling of fluid flow in subsurface reservoirs is essential in various environmental and energy applications...

... approaches, and it ensures continuity of transport characteristics at solubilization changes in phase composition. fluid dynamics composition quantity saturation upstream oil & gas phase transition miscible term expression reservoir simulation phase state discontinuity algorithm flow...

... between seismic deformation (e.g. milliseconds) and transient flow (e.g. hours) allows a "two-model paradigm" for geophysics and reservoir simulation. In the context of flow-induced geohazard risk mitigation and micro-seismic data integration, this paradigm breaks down. Under micro-seismic deformation...

...Abstract Abstract Accounting for poro-mechanical effects in full-field reservoir simulation studies and uncertainty quantification workflows is still limited, mainly because of their high computational cost. We introduce a new approach that couples hydrodynamics and poro-mechanics with dual...

... simulators as an independent module. In addition, it is also an automatic process that fits well with iterative optimization algorithms. machine learning reservoir characterization production monitoring risk management reservoir simulation drillstem/well testing drill sequence evolutionary...

... and convergence behavior. flow in porous media reservoir simulation hydraulic fracturing upstream oil & gas modeling & simulation complex reservoir average ratio timestep case 2 locality equation of state fluid dynamics prediction convergence behavior fracture network iteration...

... that the optimized schedule offers significant improvement in recovery factor, net present value, and sweep efficiency compared to the base scenario using equal rate injection and production. The optimization algorithm is computationally efficient as it requires only a few forward reservoir simulations. flow...

...Abstract Abstract A new implementation of a multiscale sequential fully implicit (MS SFI) reservoir simulation method is applied to a set of reservoir engineering problems to understand its utility. An assessment is made to highlight areas where the approach brings substantial advantage...

.... The numerical scheme is presented in detail explaining the discrete setting with a focus on the treatment of curved interfaces. Examples, inspired from industrial applications, are presented showing the validity of the proposed approach. reservoir simulation approximation computation flow in porous...

...Abstract Abstract Alternative to CPU computing architectures, such as GPU, continue to evolve increasing the gap in peak memory bandwidth achievable on a conventional workstation or laptop. Such architectures are attractive for reservoir simulation, which performance is generally bounded...

...Abstract Abstract We present a novel reservoir simulator time-step selection approach which uses machine-learning (ML) techniques to analyze the mathematical and physical state of the system and predict time-step sizes which are large while still being efficient to solve, thus making...

... the in-situ remediation processes such as biodegradation. Antennas are placed either on the ground or installed in the soil near the ground surface. The antennas operate in the hundreds of kHz to MHz range. To model the RF heating process, we successfully coupled a reservoir simulator with a 3-dimensional...

... management. In the coupled model the subsurface reservoir simulator generates well inflow performance relationship (IPR) curves which are used by the surface network model to determine well rates that satisfy surface constraints. In the proposed proxy model, the CPU intensive reservoir simulation...

... inference cpu time reservoir simulation gmm approximation artificial intelligence upstream oil & gas normalized error function unknown gmm parameter posterior pdf mixture weight gaussian component nonlinear synthetic problem machine learning history matching iteration optimal solution...

... are demonstrated using reservoir simulation. Models with complex wells running into convergence problems are constructed and simulated. With this novel nonlinear solver, simulation gives much more reliable results on well productions without numerical oscillations and computational cost is much less. well...