Neural network boosted with differential evolution for lithology identification based on well logs information

Lithology identification of geological beds in the subsurface is fundamental in reservoir characterization. Recently, automated log analysis has an increasing demand in reservoir research and the oil industry. In this context, Machine Learning (ML) techniques arise as a surrogate model to provide lithology identification in a fast way. However, to achieve suitable performance, ML techniques require the adjustment of some parameters, and that can become a hard task, depending on the difficulty of the problem to be solved. This paper presents an Artificial Neural Network (ANN), assisted by an adaptive Differential Evolution (DE) algorithm to classify petrophysical data in the Southern Provence Basin. The main contribution is searching for a competent ANN configuration, including architecture, activation functions, regularization, and training algorithms. The proposed approach outperformed four classifiers and two results previously published. The computational methodology proposed here is able to assist in the classification of petrophysical data, helping to improve the procedure of reservoir characterization and the idealization of the development of production.