This research investigate the capabilities of data mining in identifying carbonate litho-facies from well<br>logs based on extreme learning and support vector machines. Formation facies usually influence the<br>hydrocarbon movement and distribution. Identifying geological formation facies is critical for economic<br>successes of reservoir management and development. The identification of various facies, however, is<br>a very complex problem due to the fact that most reservoirs show different degree of heterogeneity.<br>Last decade, there has been an intense interest in the use of both computational intelligence and<br>softcomputing learning schemes in the field of oil and gas: exploration and production to identify and<br>predict permeability and porosity, identify flow regimes, and predict reservoir characteristics. However,<br>most of these learning schemes suffer from numerous of important shortcoming. This paper explores<br>the use both extreme learning and support vector machines systems to identify geological formation<br>facies from well logs. Comparative studies are carried out to compare the performance of both extreme<br>learning and support vector machines with the most common empirical and statistical predictive<br>modeling schemes using both real-world industry databases and simulation study. We discuss how the<br>new approach is reliable, efficient, outperforms, and more economic than the conventional method.


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