... After that, a third step is performed to investigate the impact of each parameter on the selected mismatches, to assess the parameterization characteristics of the ensemble generated, in practice, the objective is to clarify if and how a parameter may affect a specific mismatch. With large-scale...

... dependency parametric uncertainty realization ensemble seed number lease boundary uncertain parameter geological modeling Upstream Oil & Gas facies workflow rigorous multi-scenario uncertainty analysis society of petroleum engineers equity percentage machine learning compartment...

... Abstract In this work we discuss the successful application of our previously developed automated scenario reduction approach applied to life-cycle optimization of a real field case. The inherent uncertainty present in the description of reservoir properties motivates the use of an ensemble...

... network activation map ensemble visual recognition bayesian optimized network neuron lithology neural network alexnet operation lithological class cNN society of petroleum engineers validation dataset Introduction Drill cuttings are the small pieces of rock that are chipped away...

... investigation of the various approaches was conducted in Fahimuddin et al. (2010) examining different kinds of seismic data for history matching with the Ensemble Kalman Filter. They concluded that time-difference impedance data performed better than time-difference amplitude data. The former approach...

... and Table 3 the objective function parameters. The base case well controls is highlighted in red in Table 2 . An injection BHP of 210bar and a producer BHP of 170bar was held for all the respective wells and simulated over the entire ensemble at the original well locations and then the new well locations...

... as a modification to the ensemble optimization (enOpt) technique previously introduced by Chen ( Chen, et al., 2009 ). The StoSAG algorithm works as follows: Let c ∈ ℝ n c ×1 , denote the input vector of n c control variables subject to optimization. Furthermore, let x (i) ∈ ℝ n x ×1 , denote...

... the simulation model, using customized logic for field management strategies, can bring improved workflow flexibility and efficiency, by reducing the effort needed for uncertainty iterations. To achieve meaningful forecasts for an ensemble of uncertain scenarios, it is important to distinguish between different...

... ensemble pressure interference Modeling & Simulation Brine experiment configuration injectivity permeability Fluid Dynamics society of petroleum engineers Carbon Capture aquifer Risk analysis well configuration flow in porous media temperature co2-rich giant gas field development...

... field procedure fokker reservoir simulation surface displacement data workflow ensemble ground surface displacement data assimilation aquifer compartment Abstract One way to improve our understanding of the reservoir behavior at depth is to analyze the ground surface response during...

... are commonly used to determine the uncertain model variables by minimizing the objective function that represents the mismatch between the observed and simulated production data ( Gang 2006 ; Gu and Oliver 2007 ; Oliver and Chen 2011 ). The introduction of an ensemble for model variables (e.g., horizontal...

... Abstract Power of ensemble based data assimilation methods to history match oil and gas reservoirs has been revealed during the past couple of years by different field application works ( Oliver and Chen, 2011 ). Moreover the achievements in advanced parameterization ( Lorentzen et al. 2011...

... workflow fraction log analysis oil field porosity ensemble saturation Denote Well Log analysis equation Artificial Intelligence nonlinear equation core porosity statistical learning society of petroleum engineers Abstract A method is presented for deducing porosity and saturations from...

... Abstract The aim of this work is to present the effectiveness of a fully integrated approach for ensemble-based history matching on a complex real field application. We show that the predictive ability of the ensemble of models is greatly enhanced through an integrated workflow promoting...

... development. Alternatively, ensemble-based optimization methods can be used to estimate the objective function gradient through the correlation provided by an ensemble of control strategies and the associated reservoir model predictions. The simulator is used as a black box, thus giving the method great...

... to the reservoir models using classical history matching techniques such as the ensemble Kalman filter (EnKF). Recognizing that one of the main advantages of having 4D seismic data available lies on the possibility of interpreting saturation maps to track the water front in water flooding problems, Leeuwenburgh...

... predictions and associated uncertainty estimates can be trusted. In order to achieve this task, a clear requirement is that the reservoir models consistently honour both static and dynamic data, while capturing all modelling uncertainties. In this paper we will illustrate how using ensemble based methods can...

... ensemble kalman filter realization consistency weight pcm approach enkf probability map khodabakhshi image understanding flow data facies distribution ensemble ti consistency weight forecast Introduction Since MPS simulation is a pattern-imitating method it tends to replicate the main...

... Abstract The Gibbs- NVT ensemble Monte Carlo method is used to simulate the liquid-vapor coexistence diagram and the simulation results of methane agree well with the experimental data in a wide range of temperatures. For systems with two components, the Gibbs- NPT ensemble Monte Carlo method...

... Summary An integrated reservoir characterization workflow, for structural uncertainty assessment and continuous updating of the structural reservoir model, by assimilation of production data, is presented. An ensemble of reservoir models, expressing explicitly the uncertainty resulting from...