Xianhe Yue*, Shunshe Luo
FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.4, pp. 1195-1203, 2022, DOI:10.32604/fdmp.2022.020649
Abstract Because carbonate rocks have a wide range of reservoir forms, a low matrix permeability, and a complicated seam hole formation, using traditional capacity prediction methods to estimate carbonate reservoirs can lead to significant errors. We propose a machine learning-based capacity prediction method for carbonate rocks by analyzing the degree of correlation between various factors and three machine learning models: support vector machine, BP neural network, and elastic network. The error rate for these three models are 10%, 16%, and 33%, respectively (according to the analysis of 40 training wells and 10 test wells). More >
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