With the persistent quest for better prediction accuracies of petroleum reservoir properties, research in Computational Intelligence (CI) continues to evolve new techniques to meet this noble objective of petroleum reservoir characterization. In previous presentations, it was established that individual CI techniques are limited in their performance as they have their respective areas of strengths and weaknesses. The concept of Hybrid CI (HCI) was presented to overcome this problem as it utilizes the strengths of two or more techniques to compliment their respective weaknesses. However, the HCI techniques are not able to integrate the various expert opinions on the optimization of CI techniques and those that exist in their respective fields of application. The ensemble learning paradigm is presented here as a possible solution.

The ensemble learning paradigm, also called the committee of learning machines, is the latest development in Computational Intelligence and Machine Learning technologies. It is the method of combining the output of several individual learners with different hypotheses employed to solve the same problem in order to produce an overall best result. The success of this paradigm is based on the belief that the decision of a committee of experts is better than that of a single expert. The ensemble method has been successfully applied in other fields such as bio-informatics, hydrology, time series forecasting, soil science, and control systems. Its benefits have not been well utilized in petroleum engineering.

As a continuation of what has become like a "SPE Computational Intelligence Lecture Series" over the past 2 years, this paper presents an overview of the ensemble learning paradigm, a review of its successful application in other fields, a justification of its necessity in petroleum engineering and a general framework for its successful application in reservoir characterization. This paper will be of benefit to interested persons to explore the exciting world of computational intelligence and for the appreciation of the benefits of the latest development in computational intelligence.

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