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Steam chamber conformance in Steam Assisted Gravity Drainage (SAGD) influences the efficiency and economic performance of bitumen recovery. Conventional SAGD well completion designs provide limited control points in long horizontal well pairs leading to development of a non-ideal steam chambers. Developing advanced wellbore completions and optimizing downhole tool settings is critical to achieve optimal steam distribution in heterogeneous reservoirs for optimal recovery. This paper presents a workflow to optimize SAGD well completion design by using flow control devices (FCDs). Optimum FCD placement, and specifications are determined in consideration of reservoir heterogeneity. Uncertainties in spatial distribution of facies and rock types, reservoir rock and fluid properties are represented by multiple equiprobable deterministic and stochastic geological realizations using Monte-Carlo simulation. The methodology is based on constrained nonlinear optimizationtomaximize the net present value (NPV) as the objective function. A coupled wellbore/reservoir simulation model of a well pad is implemented in the study, and the efficacy of different scenarios with varied well designs are assessed from evaluating bitumen production, steam injection, and well completion expenses. Results indicate superior performance of the wells equipped with FCDs compared to conventional concentric and parallel dual string well completion designs. For the cases examined, this translates to an average 7% increase of the expected NPV for different well completion designs when using FCDs. Furthermore, results show using zonal isolation in the well design is essential for compartmentalized reservoirs such aspoint bar deposits with their significant heterogeneity. Advanced wellbore completions provide sufficient tools to constrain steam injection and liquid production into and from different well segments, and manage steam chamber conformance along the horizontal well pairs, improve production efficiency, increase bitumen recovery, and reduce operating costs. A novel workflow is presented to optimize advanced wellbore completions utilizing flow control devices. This integrated assisted optimization approach considers uncertainties in geological properties, and determines the optimal FCD parameters and well completion design with acceptable computational effort. This integrated workflow allowed us to undertake a thorough evaluation of the key subsurface uncertainties, and design an overall development plan. The probabilistic nature of the results legitimize quantifying the uncertainties and identify associated risks for different completion strategies.

The Ensemble Kalman Filter (EnKF) has gained popularity over recent years as a Monte-Carlo based technique for assisted history matching and real time updating of reservoir models. The EnKF procedure utilizes an ensemble of model states (e.g. realizations of reservoir properties such as porosity and permeability) to approximate the covariance matrices used in the updating process. EnKF works efficiently with Gaussian variables and linear dynamics, but it often fails to preserve the reference probability distribution of the model parameters and to achieve an acceptable production data match where the system dynamics are strongly nonlinear, especially of the type related to multiphase flow, or if non-Gaussian prior models are used. In order to alleviate these drawbacks, we investigated various weighted averaging techniques for computing the ensemble mean by introducing a weighting factor to each ensemble; two new formulations were implemented. The first weighting factor was calculated based on the mismatch in entropy of the model parameters, a normalized measure of the spread of a given probability distribution. The second weighting factor was computed using the forecast mismatch. In addition, both weights could be applied at a single updating step for reducing the forecast mismatch and maintaining the prior distribution simultaneously. The performance of traditional EnKF and these weighted EnKF methods were evaluated by performing various simulation studies with different reservoir heterogeneity. The qualities of the final matching results were assessed by computing the experimental histogram and variograms of the final ensemble, as well as the Root Mean Square Error (RMSE) of the predicted data mismatch. The results reveal that reasonable improvement in the efficiency of the EnKF is achieved by suggested weighted techniques. The RMSE of the predicted data is improved, and the quantity of spurious model parameters is reduced at each updating step. Taking advantage of the entropy based weighting factor assists the filter to preserve the reference distribution. The improvement indicates that the Entropy weighted EnKF (EWEnKF) has a significant potential to resolve the shortfall of traditional EnKF in reservoir characterization and history matching of challenging reservoirs with non-Gaussian distributions.