Open AccessPrediction of Coal-Bed Methane Production Based on PCA and SVM
Author(s) -
Duanwei He,
Shun Zhan,
Pengbo He,
Lu Han,
Xiaoya Chen
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/750/1/012119
Subject(s) - principal component analysis , support vector machine , production (economics) , oil production , coal , production line , gaussian , methane , predictive modelling , coal mining , regression , petroleum engineering , principal component regression , computer science , data mining , machine learning , artificial intelligence , engineering , statistics , mathematics , chemistry , waste management , organic chemistry , economics , macroeconomics , mechanical engineering , computational chemistry
Accurate prediction of oil production is difficult because there are so many factors affecting production. In this paper, new screening rules are used for principal component analysis and factor screening, the support vector machine is used to complete subsequent learning and prediction work, and Gaussian regression is selected to determine the dividing line of prediction results. The results show that the model is more accurate in predicting Wells with production below 2000 cubic meters, and more accurate in predicting Wells with production above 2000 cubic meters.