Data mining applications in hydrocarbon exploration

Abstract

This paper presents a review of the use of intelligent data analysis techniques in Hydrocarbon Exploration. The term "intelligent" is used in its broadest sense. The process of hydrocarbon exploration exploits data which have been collected from different sources. Different dimensions of data are analyzed by using Statistical Analysis, Data Mining, Artificial Neural Networks and Artificial Intelligence. This review is meant not only to describe the evolution of intelligent data analysis techniques used in different phases of hydrocarbon exploration but also signifying the growing use of Data Mining in various application domains; we avoided a general review of Data Mining and other intelligent data analysis techniques in this paper. The volume of general literature might affect the precision of our view regarding the application of these techniques in hydrocarbon exploration. The review reveals the suitability of existing techniques to data collected from diverse sources in addition to the use of analytical techniques for the process of hydrocarbon exploration.

An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry

“…Di↵erent algorithms for feature extraction were also compared, namely Relief-F and SVM Recursive Feature Elimination (SVM-RFE) for feature ranking, and Correlation-based Feature Selection (CFS) and Las Vegas Filter (LVF), for filter subset selection. Contrasting with the remarks from Shaheen et al [31], the authors argue that the choice of the classification method should be more important than that of the feature selection algorithms.…”

“…The approaches surveyed in this article are approximately even split in what concerns the data type used , with 60% of the articles using petrophysical logs and 40% using geophysical surveys. Shaheen et al [31] surveyed data mining applications in hydrocarbon exploration, covering ANNs, FL, and SVMs, addressing as well remote sensing image analysis. The breakdown of the surveyed articles by problem type shows that feature selection is the biggest concern among researchers, with approximately half of the articles dedicated to "influencing factor analysis".…”

Labeled clustering a unique method to label unsupervised classes

“…Clustering is a technique of unsupervised classification. Many techniques of clustering had been developed and reviewed in [7,12]. All of these techniques are based upon earliest developed clustering technique named K-Mean clustering.…”

Context Based Positive and Negative Spatio-Temporal Association Rule Mining

“…For example, the pattern extracted from 100 records of customers may not reflect stable customer behavior, while a pattern extracted from thousands of records is considered to be a more reliable gauge of customer behavior. Data mining is a structured and modeled approach comprised of discrete and iterative steps, originating from raw data and concluding with patterns and predictions [14,31].…”