@Article{fdmp.2022.020649,
AUTHOR = {Xianhe Yue, Shunshe Luo},
TITLE = {Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs},
JOURNAL = {Fluid Dynamics \& Materials Processing},
VOLUME = {18},
YEAR = {2022},
NUMBER = {4},
PAGES = {1195--1203},
URL = {http://www.techscience.com/fdmp/v18n4/47317},
ISSN = {1555-2578},
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).},
DOI = {10.32604/fdmp.2022.020649}
}