A new reservoir prediction method: PCA value‐weighted attribute optimization

Abstract

This paper introduces the principle component attribute (PCA) technology into carbonate reservoir prediction, and a PCA value‐weighted attribute optimization method is proposed here. As the calculation time‐window of the PCA value‐weighted attribute is the same (viz., free of the constraint of time‐window scale), it can effectively avoid the negative effect of different time‐windows on attribute optimization results. A test area is chosen from Tarim Basin, and the reservoir information of the known wells is treated as the training samples. The discriminant model of PCA value‐weighted attribute and reservoir parameters are built. Through the retrospective test, the total fit rate between the attribute optimization results and the production results is up to 94.62%, proving that the discriminant capability of PCA value‐weighted attribute for reservoir quality is significant. Finally, the proposed method is employed for the integration prediction of carbonate reservoir in test area. The prediction results finely exhibiting the distribution of favorable carbonate reservoir. This paper is aimed at providing a scientific and practical method for seismic attribute analysis and reservoir prediction.