Analysis of Ensemble methods applied to Lithology Classification from Well Logs

Lithology classification is an important task in reservoir characterization, one of its major purposes is to support well planning and drilling activities. Therefore, faster and more effective classification algorithms will increase the speed and reliability of decisions made by geologists and geophysicists. This paper analyzes ensemble methods applied to automatic lithology classification. For this, we performed a comparison between single classifiers (Support Vector Machine and Multilayer Perceptron) and these classifiers with ensemble methods (Bagging and Boost). The results are very satisfactory, and confirm the advantages of using ensemble methods. However the trade-off between performance improvements versus resource utilization shows that the use of ensemble methods is only necessary when precision is an extremely determinant factor.

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