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Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs

Xianhe Yue*, Shunshe Luo

School of Geosciences, Yangtze University, Wuhan, 430100, China

* Corresponding Author: Xianhe Yue. Email: email

(This article belongs to this Special Issue: Meshless, Mesh-Based and Mesh-Reduction Methods Based Analysis of Fluid Flow in Porous Media)

Fluid Dynamics & Materials Processing 2022, 18(4), 1195-1203.


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).


Cite This Article

Yue, X., Luo, S. (2022). Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs. FDMP-Fluid Dynamics & Materials Processing, 18(4), 1195–1203.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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