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OH sands have great amount o f reserves in the world with increasing commercial produc tions. Prediction of reservoir performances o f oil sands is challenging mainly due to long simulation time fo r modeling heat and fluids flows in steam assisted gravity drainage (SAGD) operations. Because o f accurate modeling difficulties and limited geophysical data, it requires many simulation cases o f geostatistically generated fields to cover uncer tainty in reservoir modeling. Therefore, it is imperative to develop a new technique to analyze production performances efficiently and economically. This paper presents a new ranking method using a static factor that can he used fo r efficient prediction o f oil sands production. The features vector proposed can reflect shale barrier effects in terms o f shale length and relative distance from the injection well. It preprocesses area that steam chamber bypasses, and then counts steam chamber expanding an area cumulatively. K-means clustering selects a few fields fo r fu ll simulation run and they will cover cumula tive probability distribution function (CDF) o f all the fields examined. Accuracy o f the prediction is high when cluster number is more than 10 based on cases o f cluster number 5, 10, and 15. This technique is applied to fields with 3%, 5%. 10%, and 15% shale fra c tion and all the cases allow efficient and economical predictions o f oil sands productions

Uncertainty in oil sands reservoirs can be quantified by generating multiple realizations using geostatistical methods. However, it requires huge computing time to simulate all of the realizations. This article proposes a new approach for features modeling of oil sands reservoirs in metric space. As the first step, an area affected by the expansion of a steam chamber is set and converted to the polar coordinate system. The converted area is expressed as an image matrix consisting of 0 or 1 value. Then the matrix is transformed using two-dimensional discrete Fourier transform. Key features in the front columns and rows of the transformed matrix are extracted. These features in metric space are plotted using principal component analysis. Self-organizing map algorithm is used to select representative models of realizations for performing full flow simulations. In the result of grouping, each cluster group distributes separately in metric space according to reservoir productivity, but there are mixes of a small portion among the adjacent groups due to similar productivity.

For decision making, it is crucial to have proper reservoir characterization and uncertainty assessment of reservoir performances. Since initial models constructed with limited data have high uncertainty, it is essential to integrate both static and dynamic data for reliable future predictions. Uncertainty quantification is computationally demanding because it requires a lot of iterative forward simulations and optimizations in a single history matching, and multiple realizations of reservoir models should be computed. In this paper, a methodology is proposed to rapidly quantify uncertainties by combining streamline-based inversion and distance-based clustering. A distance between each reservoir model is defined as the norm of differences of generalized travel time (GTT) vectors. Then, reservoir models are grouped according to the distances and representative models are selected from each group. Inversions are performed on the representative models instead of using all models. We use generalized travel time inversion (GTTI) for the integration of dynamic data to overcome high nonlinearity and take advantage of computational efficiency. It is verified that the proposed method gathers models with both similar dynamic responses and permeability distribution. It also assesses the uncertainty of reservoir performances reliably, while reducing the amount of calculations significantly by using the representative models.

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