Yohei Nishitsuji , Jalil Nasseri

One of major technical competitions in energy industry relates to how optimally deep-learning architectures we can design. Optimization of hyperparameters is treated as labor-intensive. However, it is important to tune the parameters especially when we deal with relatively small targets, yet high-impact consequences can be resulted. In this study, we adapt Optuna, the global optimizer, for tuning the hyperparameter of the deep-learning scheme of the extended long-short term memory with forget gates. We apply this framework for predicting lithological facies. Although the macro difference with and without Optuna is not significant in this study, our results indicate that Optuna could make large commercial impacts when targets are small yet difficult to be captured.