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 s...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).展开更多
文摘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).
