AUTHOR=Zhang Kai , Peng Xian , Chen Yingli , Yan Yuhan , Mei Qingyan , Chen Yu , Zhang Dongming 

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

JOURNAL=Frontiers in Earth Science

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2023.1220189

DOI=10.3389/feart.2023.1220189

ISSN=2296-6463

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. After training the historical data of the gas reservoir, the prediction model can be established to predict the change trend of the cumulative gas production under different production conditions, so as to obtain the recoverable space. At the same time, the established model can conduct sensitivity analysis on dynamic indicators to obtain the impact of different indicators on the cumulative gas production and under what conditions, the cumulative gas production can reach the maximum value.