Abstract
Predicting reservoir parameters accurately is of great significance in petroleum exploration and development. In this paper, we propose a reservoir parameter prediction method named a fusional temporal convolutional network (FTCN). Specifically, we first analyze the relationship between logging curves and reservoir parameters. Then, we build a temporal convolutional network and design a fusion module to improve the prediction results in curve inflection points, which integrates characteristics of the shallow convolution layer and the deep temporal convolution network. Finally, we conduct experiments on real logging datasets. The results indicate that compared with the baseline method, the mean square errors of FTCN are reduced by 0.23, 0.24 and 0.25 in predicting porosity, permeability, and water saturation, respectively, which shows that our method is more consistent with the actual reservoir geological conditions. Our innovation is that we propose a new reservoir parameter prediction method and introduce the fusion module in the model innovatively. Our main contribution is that this method can well predict reservoir parameters even when there are great changes in formation properties. Our research work can provide a reference for reservoir analysis, which is conducive to logging interpreters’ efforts to analyze rock strata and identify oil and gas resources.