A Lithology Identification Approach Based on Machine Learning With Evolutionary Parameter Tuning

Identification of underground formation lithology from well-log data is an important task in petroleum exploration and engineering. Due to the cost or imprecision of some methods applied in this activity, there is a need to automate the procedure of reservoir characterization. Machine learning techniques can be efficient alternatives to lithology identification. To acquire proper performance, usually, some parameters of these techniques should be adjusted, and this can become a hard task depending on the complexity of the underlying problem. This letter integrates the gradient boosting (GB) with a differential evolution (DE) for formation lithology identification using data from the Daniudui gas field and the Hangjinqi gas field. This letter's contributions include the use of an evolutionary algorithm to adjust optimally the hyperparameters of the GB, and the results show improvements when compared with those obtained in the literature.