... process is highly recommended. The scope of the activity is then to set up a new workflow able to fully integrate Production Data Analysis (PDA) with an Ensemble History Matching (ENHM) workflow. PDA outcomes represent evidence highlighted by the whole production history based on the collection, analysis...

... of a large uncertainty ensemble of static realisations, and most of all the large number of simulation runs that potentially needs to be conducted. This is exacerbated when the models are large and require many hours to run. Moreover, even with the prevalence of distributed and parallel computing clusters...

... 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...

... 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...

... users to easily model conceptual as well as parametric uncertainties of the reservoir without the necessity of any complex scripting. The chain of models for all concepts is presented in one view, to provide overview of the key differences between concepts used. An ensemble of geologically sound samples...

... strategy ensemble reservoir model Upstream Oil & Gas development strategy algorithm control strategy Scenario enhanced recovery injector producer drainage strategy development scenario reservoir society of petroleum engineers waterflooding Introduction In a recent report...

... consistent as well. It would further help to reduce the time taken to get human expertise to complete the task, as well as the associated cost. machine learning classification recognition ensemble Artificial Intelligence reservoir characterisation detection Upstream Oil & Gas convolutional...

... Abstract This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties, captured at every stage of the interpretative and modelling process, are used as inputs within...

... the CO2 sequestration in the reservoir. Reservoir Characterization Modeling & Simulation Upstream Oil & Gas enhanced recovery Fluid Dynamics Plateau Length Brine ensemble experiment configuration Carbon Capture aquifer flow in porous media temperature co2-rich giant gas field...

... 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...

.... The statistical approach also indirectly takes into account noise in the data. well logging water resistivity well log data saturation machine learning Upstream Oil & Gas traditional workflow fraction log analysis oil field porosity ensemble Artificial Intelligence nonlinear equation core...

... 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...

... Abstract In this paper, a modified ensemble randomized maximum likelihood (EnRML) algorithm has been developed to estimate three-phase relative permeabilities with consideration of hysteresis effect by reproducing the actualproduction data. Ensemble-based history matching uses an ensemble...

... displacement workflow ensemble compartment Ingredients of the inversion Planning of reservoir exploitation is often mainly based on predictions of future reservoir behavior by reservoir flow simulations. These flow simulations incorporate our a-priori understanding of the geology of the reservoir...

... 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...

... . Chen , Y. , Oliver , D.S. and Zhang , D. 2009 . Efficient ensemble-based closed-loop production optimization . SPEJ 14 ( 4 ) 634 – 645 . doi: 10.2118/112873-PA . Jansen , J.D. , Brouwer , D.R. , Nævdal , G. and van Kruijsdijk , C.P.J.W. 2005 . Closed-loop...

... performance. Another key issue is that model parameters are always affected by uncertainty, even after being conditioned to the historical data. Objective of this paper is to present a robust ensemble-based optimization methodology (EnOpt) for maximizing reservoir life-cycle production under geological...

... geologic modeling artificial intelligence tis facies realization realization consistency weight pcm approach enkf probability map khodabakhshi facies distribution ensemble ti consistency weight forecast multiple tis reference model ensemble kalman filter Introduction Subsurface...

... 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...

... properties. It is shown that an EnKF workflow can be effectively coupled to other stochastic optimization schemes with complimentary optimization features. The EnKF formulation reduces a non-linear optimization problem in a large parameter space to a statistical optimization problem in ensemble space...