Open Access
ARTICLE
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).
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APA Style
Yue, X., Luo, S. (2022). Machine learning-based prediction of oil-water flow dynamics in carbonate reservoirs. Fluid Dynamics & Materials Processing, 18(4), 1195-1203. https://doi.org/10.32604/fdmp.2022.020649
Vancouver Style
Yue X, Luo S. Machine learning-based prediction of oil-water flow dynamics in carbonate reservoirs. Fluid Dyn Mater Proc. 2022;18(4):1195-1203 https://doi.org/10.32604/fdmp.2022.020649
IEEE Style
X. Yue and S. Luo, "Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs," Fluid Dyn. Mater. Proc., vol. 18, no. 4, pp. 1195-1203. 2022. https://doi.org/10.32604/fdmp.2022.020649