TY  - EJOU
AU  - Yue, Xianhe 
AU  - Luo, Shunshe 

TI  - Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs
T2  - Fluid Dynamics \& Materials Processing

PY  - 2022
VL  - 18
IS  - 4
SN  - 1555-2578

AB  - 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).
KW  - Carbonate rock; machine learning; support vector machine; fluid dynamics; neural network

DO  - 10.32604/fdmp.2022.020649