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
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