... in-house simulators due to limited support for adjoint methods in commercial simulators. This approach enables reservoir engineers to efficiently perform history matching using adjoint gradients while still using industry-standard commercial reservoir simulators. history matching optimization...

... condensate production artificial intelligence enhanced recovery objective function oil production constraint optimization problem asset and portfolio management cloud computing geological subdiscipline water production oil rim directional drilling flow in porous media reservoir surveillance...

... by providing a structure for the optimization problem to be framed, specifying the decision variables, objective function, uncertainties and optionally constraints. It is integrated with industry standard reservoir simulators and allows to connect to external software. Over the past decade, EVEREST has been...

... optimization problem asset and portfolio management portfolio analysis risk management optimization constraint artificial intelligence evolutionary algorithm pareto front scenario ullage exploration reservoir simulator export network optimization approach reservoir simulation...

... and operations of the geothermal reservoir, the solar field and the power plant under optimal conditions. deep learning artificial intelligence neural network machine learning production monitoring configuration reservoir surveillance power plant optimization problem production control production...

... intelligence machine learning geology scientific computing reservoir characterization optimizer geologist optimization problem reservoir geomechanics geological subdiscipline florez flow in porous media jacobian duchon spline approximation evolutionary algorithm objective function unknown...

... Abstract Accurate well placement plays essential role in increasing the field recovery/storage while reducing the operational costs. This task is complex, requiring robust solutions that can handle optimization problems efficiently. Despite numerous existing solutions, there remains a need...

... uncertainty. We highlight the necessary modifications for our CCUS applications. Constrained Bi-Objective Robust Production Optimization Problem Note that in this paper, we convert any maximization into its equivalent minimization form, for the sake of consistency with the theories of convex...

... optimization problem complex reservoir modeling & simulation structural geology production monitoring rock type machine learning numerical simulation reservoir surveillance schiozer production control wag-co 2 matrix cycle size artificial intelligence chemical flooding methods snapshot...

... multiple well-placement optimization problem. The Egg benchmark model is used where the locations of one injector and one producer serve as the decision variables and the immobile CO 2 stored as the objective function. A modified Genetic Algorithm (GA) and a modified modelbased Trust-Region (TR) algorithm...

... vector, the observation error, and the impact of considering a different synthetic truth, was analyzed in this study to find the optimum parameters and test the accuracy and robustness of the DSI technique for optimization. geologist optimization problem reservoir simulation enhanced recovery...

... and full-physics flow equations are solved on 1D connections in the network. Behm et al. (2019 ) then applied StellNet to a steamflood optimization problem. Abstract History matching using high-fidelity reservoir models can be computationally expensive. Previously, we implemented a novel physics...

... the computational cost from the size m and n of a history matching problem before running expensive simulations. united kingdom government optimization problem united states government reservoir simulation upstream oil & gas subroutine europe government strategic planning and management...

... and field gas production rate) at each control step need to be honored, solving a large-scale production optimization problem, particularly in geological uncertainty, becomes significantly challenging. This study presents a stochastic gradient-based framework to efficiently solve a nonlinearly constrained...

... to perform efficiently for well-placement optimization problems when such problems are translated into real-valued representations, and special noisy objective function handling protocols are implemented. However, applying such methods to large-scale problems may still be impractical because the gradients...

... if the pore radius decreases to one nanometer, which indicates the potential of our algorithm for simulating the shale reservoir production process. upstream oil & gas complex reservoir algorithm united states government equation of state pvt measurement optimization problem artificial...

... modeling & simulation scaling method journal fluid dynamics united states government optimization problem artificial intelligence machine learning scenario workflow fast history matching society producer constraint category spe journal injector Introduction The close-loop modern...

... artificial intelligence cross-section implementation fracture characterization reservoir simulation europe government scaling method optimization problem modeling & simulation reservoir characterization avgmpfa figure stencil cut-cell grid water saturation cross-section ntpfa accuracy...

... efficiency maximization case. For the mobile CO 2 minimization case, by contrast, wells are separated and pulsed, which facilitates dissolution and residual trapping. fluid dynamics upstream oil & gas optimization problem subsurface storage enhanced recovery co 2 constraint-based reasoning...

... sequestration wheeler co 2 probabilistic surrogate evaluation function evaluation evolutionary algorithm optimization problem bayesian optimization optimization application gaussian process acquisition function carbon sequestration petroleum engineer Bayesian Optimization Bayesian...