Joint rough sets and Karhunen-Loève transform approach to seismic attribute selection for porosity prediction in a Chinese sandstone reservoir

The Paleogene Lower Dongying Formation of the Bohai Bay Basin, China, consists of a series of vertically stacked sand-filled delta distributary channels. These laterally complex sandstones create the need for a precise interwell estimation of reservoir porosity. In this study, we integrated wireline logs from 101 wells and 43 km³ of seismic data to directly predict porosity in the area of an existing heavy-oil field in the middle of the Liaoxi Uplift, Bohai Bay Basin. The top and the bottom horizons of the target oil unit interpreted on high-quality 3D seismic data are used to constrain the time window for 41 horizon-based attribute extractions. Next, we used the joint rough sets and Karhunen-Loève transform (K-L transform) selection method to choose the optimal number and the type of seismic attributes that exhibit a high correlation with porosity. Finally, a method combinings multiple linear regression and radial basis function neural network was used to predict porosity based on the selected attribute subsets. After error analysis of the 101 wells, the results from the joint attribute selection approach showed the least prediction error at well locations because the joint attribute selection captured the linear and nonlinear relationship between attributes and porosities. We obtained a geologically realistic predicted porosity distribution using the joint approach, which suggested a network of main distributary channel sands characterized by high-porosity zones (approximately 32%).