Skip to main content

Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines

Buy Article:

$29.95 plus tax (Refund Policy)

We developed new concepts of extended Monte Carlo cross validation and Monte Carlo committee machines. We subsequently used those concepts to predict permeability by linear regression and machine learning methods such as Neural Networks, Support Vector machines, and Regression Tree. Among the parameters we calculated using extended Monte Carlo cross validation are: root-mean squared error of individual forecasts, forecast bias, correlation between forecast and actual permeability, and forecast instability as a measure of sensitivity to perturbations of the training set. Output of Monte Carlo committee machines is constructed as the average of machine learning outputs generated from multiple versions of perturbed training sets. We observed that Monte Carlo committee machines produced high stability forecasts, while individual machine learning forecasts (e.g. a single ANN) were characterized by lower stability. Higher accuracy forecasts were achieved when we applied machine learning methods and linear regression using permeability models that included quantitative and categorical predictors and second-order interactions among the predictors.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: COMMITTEE MACHINES; LINEAR REGRESSION; MACHINE LEARNING; MONTE CARLO CROSS VALIDATION; PERMEABILITY FORECAST

Document Type: Research Article

Publication date: 2016-12-01

More about this publication?
  • Welcome to the home page of the Journal of Sustainable Energy Engineering (JSEE), committed to publishing peer-reviewed original research seeking sustainable methods of worldwide energy production through engineering, scientific, and technological advances.
  • Editorial Board
  • Submit a Paper
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more