... were tested for the study using both conceptual and full-field Tengiz models, realizing the utility of some techniques that could help in many field control parameters. However, all these optimization techniques required more than 2000 simulation runs to achieve optimal results, which was not practical...

... chemical flooding methods uncertainty parameter uncertainty analysis Efficiency Permeability reduction reservoir enhanced recovery uncertainty table conventional approach recovery factor Upstream Oil & Gas chemical performance uncertainty simulation run polymer flood microemulsion...

... , … , M fuzzy logic optimization simulation run optimization problem History application workflow algorithm Artificial Intelligence objective function Pareto front multiple objective optimization mirzabozorg differential evolution optimal solution population-based optimization...

... performance compared to EnOpt for the life cycle production optimization step of the sequential framework. Upstream Oil & Gas well control optimization run Artificial Intelligence joint optimization problem enopt algorithm simulation run algorithm optimization problem covariance matrix...

... method well control enhanced recovery Upstream Oil & Gas quasi-newton method simulation run optimal well control max-min problem objective NPV realization waterflooding Pareto front objective function NBI method direct weighted sum method punq case vector lexicographic method...

... foundations for the next generation of uncertainty tools and workflows. machine learning history matching spe reservoir simulation symposium reservoir simulation variance Artificial Intelligence Upstream Oil & Gas optimization problem likelihood function simulation run algorithm spe...

... of the Newton-Raphson method. To solve the adjoint system, one can store either these Jacobians, which are generated during the reservoir simulation run, or save the primary variables from the simulation run and recompute the Jacobians at each backward time step of the adjoint solution. For history matching...

... and convergence in fewer iterations. Objective function upstream oil & gas enopt multiscale regularized enopt objective function well control producer artificial intelligence multiscale regularization optimization simulation run production optimization producer 1 bobyqa optimization...

... model 1 ipafactory IPALight CPU time solver performance cum cpu time algorithm ipadatabase Upstream Oil & Gas solver parameter combination simulator solver parameter probability matrix parameter combination optimization problem Simulation time step simulation run solver...

... simulation cases. reservoir simulation uncertainty range uncertainty parameter history matching best case simulation run Artificial Intelligence Upstream Oil & Gas simulation model workflow application Time Period experimental design cluster analysis model calibration simulation case...

... oil volume) over the whole lifecycle of the field. An essential tool in tackling this optimization problem is the adjoint method from optimal control theory. Starting from a base case reservoir simulation run, this extremely efficient method makes it possible to compute the sensitivities of the total...

... is that with changing flow conditions the grid needs to be altered over the period of a simulation run. This paper presents the application of the windowing technique, introduced by Deimbacher and Heinemann 1 , to locally honor the changing directions of flow by modifying the grid during a simulation run. One...

... Abstract This paper presents a novel approach for estimating relative permeabilities through an automated history matching procedure which makes use of linear models. The new technique makes use of linear relationships which are found to exist between simulation runs for the same points...