Application of data mining techniques in building predictive models for oil and gas problems: a case study on casing corrosion prediction Online publication date: Tue, 23-Dec-2014
by Mazda Irani; Rick Chalaturnyk; Mohsen Hajiloo
International Journal of Oil, Gas and Coal Technology (IJOGCT), Vol. 8, No. 4, 2014
Abstract: This paper describes the use of (supervised) data mining to predict casing corrosion in carbon geological storage projects. This study discusses: 1) data pre-processing such as missing value handling and discretisation; 2) feature selection methods such as correlation coefficient, signal-to-noise ratio, information gain, Gini index, and the k-nearest neighbour (KNN) approach; 3) classification techniques including decision trees (C4.5 and CART) and Bayesian networks; 4) evaluation methods like cross-validation as four successive steps of supervised learning. The experimental analysis of the casing corrosion problem based on the given supervised learning framework shows the effectiveness of data mining techniques in finding features relevant to the problem under study and in building models to predict and identify casing corrosion. [Received: June 19, 2013; Accepted: December 24, 2013]
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Oil, Gas and Coal Technology (IJOGCT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook