Efficient Prediction of SAGD Productions Using Static Factor Clustering

Abstract

Oil sands have great amount of reserves in the world with increasing commercial productions. Prediction of reservoir performances of oil sands is challenging mainly due to long simulation time for modeling heat and fluids flows in steam assisted gravity drainage (SAGD) operations. Because of accurate modeling difficulties and limited geophysical data, it requires many simulation cases of geostatistically generated fields to cover uncertainty 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 be used for efficient prediction of oil sands production. The features vector proposed can reflect shale barrier effects in terms of 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 for full simulation run and they will cover cumulative probability distribution function (CDF) of all the fields examined. Accuracy of the prediction is high when cluster number is more than 10 based on cases of cluster number 5, 10, and 15. This technique is applied to fields with 3%, 5%, 10%, and 15% shale fraction and all the cases allow efficient and economical predictions of oil sands productions.