Cluster analysis of carboniferous gas reservoirs and application of recovery prediction model

Abstract

Since the discovery of the Carboniferous gas reservoirs in East Sichuan in 1977, after more than 40 years of development, most of the gas reservoirs have entered the middle and late stages of development. The gas reservoir is characterized by strong heterogeneity, large difference in permeability, and serious impact of water invasion in some blocks. Therefore, how to make a correct decision on gas field development and deployment is of vital importance. Combined with system clustering, BP neural network, correlation analysis and other methods, this paper first analyzes and calculates the static indicators of the Carboniferous gas reservoirs, and then divides the gas reservoirs into four categories using ward clustering method according to the calculated weight value, and determines the characteristics of each type of gas reservoirs using correlation coefficient analysis method. Finally, the recovery prediction model of each type of gas reservoir is established according to the BP neural network. The results indicate that: (1) The recovery rate prediction model can predict the trend of cumulative gas production changes, thereby obtaining the space for improving recovery rate, and the accuracy of the prediction results is high, which can be used as a reference for gas field planning. (2) The sub-active gas reservoirs with strong heterogeneous water bodies and the inactive gas reservoirs with low permeability water bodies have a certain space for enhanced oil recovery.