... neural network (NN), creating a map from well-location parameters to the objective function. We utilize the analytical gradient of the NN, resulting in an effective search direction while saving the computational cost associated with stochastic-gradient perturbations. Our method features a novel NN...

... In this study, we look at the performance of our in-house TU-NLCPO optimizer for robust production optimization with the gradients of the objective function and the constraints computed using the StoSAG method. Specifically, we apply these optimization methods to the synthetic-realistic reservoir...

... datta-gupta permeability optimization problem objective function reservoir model uncertainty parameter workflow moga basis function petroleum engineer Introduction One of the purposes of building reservoir simulation models is to perform predictions and forecasting. Ideally...

... & gas controller us government air emission complex reservoir objective function information cloud computing optimization problem cumulative production intermittent shut-in optimization deployment into the atmosphere carbon emission reduction acquisition function surface response liquid...

... defined. These cases are not yet conditioned to production data. History matched ensemble : Collection of cases (based on the initial ensemble) that has been conditioned to observed data using chosen objective functions and by defining appropriate tolerances. Objective function : set of criteria...

... objective function reservoir simulation model artificial intelligence upstream oil & gas ahm problem history matching bco algorithm gos-x workflow optimization algorithm algorithm food spot performance measure superiority optimization problem optimization technique hm parameter...

... start-up shut-in action production optimization framework cumulative production contract value relative error north sea field data upstream oil & gas operational criteria time horizon forward model machine learning neural network objective function field data penalty synthetic data...

... expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization. upstream oil & gas existing wellbore algorithm well planning objective function norwegian continental shelf artificial intelligence constraint...

... Algorithm (GA). DQN is a multi-thread optimization method that distributes an ensemble of optimization tasks among multiple high-performance-computing nodes. Thus, it can locate multiple optima of the objective function in parallel within a single run. Simulation results computed from one DQN optimization...

... data for reservoir characterization, and in turn, for the reservoir management and the decision-making processes. reservoir characterization ensemble-based method extraction window reservoir simulation objective function history history matching seismic data production data upstream oil...

... analysis artificial intelligence portfolio optimization climate change performance indicator strategic planning and management asset and portfolio management upstream oil & gas objective function broad range resilience performance objective performance delivery computation scenario...

... is to structure a data-driven-based workflow to design and optimize alkali-surfactant-polymer flooding ( ASP ) projects. Various objective functions from the techno-eco perspectives are considered to obtain comprehensive optimum ASP slug formulations and injection operation schemes. Several universal multi-layer...

... scale case study is presented to demonstrate the practicability of the workflow. An injection-pattern based reservoir model employing a compositional numerical simulator is established to develop and test the hybrid-optimization workflow. Such a scheme aims at optimizing objective functions including...

... are introduced to define the history matching objective functions from various types of data including bottomhole pressure data, and the oil, water and gas productions rates. The computational results show that our adaptive scheme obtains better history match solutions. The presented multi-scenario approach...

... and d by σ Jd and the variance of the objective function J by σ J 2 . We denote the data covariance, data-objective covariance and objective covariance calculated from the simulation results of the coarse uncertainty characterization by σ d , c 2 , σ Jd,c and σ...

... Artificial Intelligence Upstream Oil & Gas optimization optimization problem objective function asset and portfolio management closed-loop field development optimization decision variable field development optimization and planning infill well configuration reservoir model...

... Intelligence optimization problem reservoir simulation objective function platform optimization workflow simulation run development design subsurface uncertainty Upstream Oil & Gas optimization workflow Field Development Optimization realization asset and portfolio management optimization...

..., for the first time, modeling and optimization of a field-scale post-combustion CO2 WAG process. enhanced recovery objective function relative permeability function model response optimization problem Efficiency spe annual technical conference Upstream Oil & Gas sensitivity application...

... of x , conditioned to production data ( d obs ), can then be written as, (1) p ( x | d o b s ) = c p exp [ − f ( x ) ] , where c p is a normalization constant and f(x) is the objective function defined by, Abstract Uncertainty quantification...

... provides an understanding and clarification to serve as a guideline on optimization practices by outlining the significant components in the process. optimization problem objective function Upstream Oil & Gas Computational Geoscience constraint optimization Artificial Intelligence...