Logging Lithology Discrimination in the Prototype Similarity Space With Random Forest

Borehole lithology discrimination is the foundation for formation evaluation and reservoir characterization. Due to the limitation of costing or accuracy, direct discrimination methods, such as borehole core and drilling cutting analysis, are unable to widely apply, while logging lithology interpretation provides an alternative solution for this task. To mitigate the influence of subjective bias, several machine learning algorithms, such as neural network, support vector machine, decision tree, and random forest (RF), have already been applied for logging lithology interpretation. However, the vast majority of preceding studies are simple applications that directly apply classification algorithms to the raw input space formed by logging curve values, only limited studies involved feature extraction or learning space transformation. In this letter, we propose a hybrid algorithm that combines the mean-shift algorithm and the RF algorithm for borehole lithology discrimination in the prototype similarity space. Experiments on data collected from nine different areas demonstrate that the proposed algorithm has significant advantages in accuracy compared with other algorithms, which provides a considerable alternative way for further machine learning-assisted logging lithology interpretation.