... sequestration. IPARS is coupled to IBM Bayesian Optimization (IBO) for parallel optimizations of CO 2 injection strategies during field-scale CO 2 sequestration. Bayesian optimization builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm, Gaussian process...

... 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 problem...

...) compositional simulation model with advanced Field Management (FM) logic was used to perform the study. Vertical conformance control was implemented in the model enabling completion control of 4 compartments per well. A model-based optimization workflow was defined to maximize recovery. Objective functions...

... interpolation parameter space reservoir permeability neural network objective function interpolation algorithm assumption porosity permeability artificial neural network modeling dilation curve Introduction Due to the gradual depletion of conventional oil and gas reservoirs with high...

... it to be useful for engineers designing IOR pilot for brown fields with complex reservoir models. Artificial Intelligence objective function machine learning history matching reservoir simulation variance Effectiveness statistical model uncertainty reduction conditioning workflow joint...

... Index (HMQI) with Moving Linear Regression Analysis to evaluate simulation results from history matching process. The HMQI provides normalized values for all objective functions having different magnitude and leads to a more consistent and robust approach to evaluate the updated models through model...

... development activities by obtaining optimization solutions that are robust against changes in future decisions, and (2) considerably reduces the performance losses that can result from field development when uncertainty is disregarded. optimization problem objective function Artificial Intelligence...

... the objective functions. The study demonstrates the potential impacts of reservoir heterogeneities on the WSI process. Models with different heterogeneity settings are examined. The results reveal that the impacts of shale barriers may be more/less evident under different circumstances. The proxy models can...

... is the weighted sum objective function constructed from the first objective and second objective functions J 1 and J 2 with w 1 and w 2 as weighting factors. The choice of the weighting factors is user dependent. It is usually recommended that different weight combinations are evaluated to explore...

... sensitivity plots, such as production and tornado plots, are used to illustrate the relative influence of the parameters on the objective function. A tornado plot is simply a stacked bar chart that displays the range of an investigated output as affected by the variation in each individual input...

... Intelligence training data Optimization Method gmm sample inverse covariance matrix error function gaussian component fitting method Gao normalized error function gmm fitting formulation formulation Upstream Oil & Gas objective function History algorithm uncertain parameter gmm fitting...

... of the history match and the quality of the forecast. So, the traditional belief that a good history matched model will also produce a good forecast is not always true. Artificial Intelligence Upstream Oil & Gas correlation Modeling & Simulation objective function variation history matching...

... objective function OPC base solution optimal solution Injection Rate candidate solution water production long-term opc Artificial Intelligence constraint optimization workflow Optimization Opportunity case study reservoir Scenario Introduction Comment As volatility in oil prices...

... is applicable for large scale history matching problems with negligible extra cost in CPU and memory. optimization problem CPU time reservoir simulation objective function Artificial Intelligence Upstream Oil & Gas iteration computational cost uncertain parameter test problem realization...

... of the uncertain model parameters. The improvements in expected NPV demonstrate the practical applicability of ensemble-based approaches for optimization under uncertainty to real field cases. If CO 2 storage credits are added to the objective function, a different control strategy is found that also leads...

... problems that have thousands of variables. An adjoint method is used to compute efficiently the derivatives of objective functions with respect to decision variables and a sequential quadratic programming method is used for optimization search. MOGA is a population-based method, which combines a Pareto...

... in a deterministic fashion and applied to low, mid and high simulation scenarios. The workflow proposed in this paper takes into account model uncertainty in the optimization outcomes. Deterministic WLO entails optimizing the easting and northing coordinates of wells with respect to an objective function (e.g. net...

...-Gaussian assumption between the observation data and the objective function, the EVA method quantifies the expected uncertainty reduction from covariance information that is estimated from an ensemble of simulations. The result of EVA can then be used with a decision tree to quantify the VOI of a given...

... constraint RML method machine learning reservoir simulation objective function unconditional realization Artificial Intelligence history matching computational cost realization Reynolds base case global-dgn rml method production data algorithm green curve Bayesian Inference Upstream Oil...

... machine learning history matching reservoir simulation approximation ensemble smoother inverse problem square problem ensemble member outlier Bayesian Inference objective function model parameter assumption algorithm iglesia regularization parameter ensemble iteration Reynolds...