Introduction of Two Data-Driven Methods for Determining the Quality of Gas Facies in Western Australia

In determining the optimal points of production drilling, it is important to identify areas of suitable reservoir quality. For this purpose, the use of geochemical data, which is usually small in number, is common. This data discontinuity creates information gaps. If one uses more continuous data so that its modeling accuracy is suitable, the drilling could be then performed with more success. In this study, seismic and well logs data were used to classify the quality of gas facies by two non-parametric statistical (Parzen) and supervised deep learning techniques (long-term short-term memory network (LSTM)). The LSTM network was then also optimized by two heuristic optimization methods (Imperialistic competition algorithm and Whale algorithm). The obtained results indicate that both methods produce good results in classification so that the modeling accuracy of gas facies quality using supervised deep learning technique (87%) is more than that of the non-parametric Parzen (83%) method. Moreover, the application of optimization algorithms has increased the classification accuracy. The best accuracy is related to the LSTM network optimized with the imperialistic competition algorithm (90%). Geochemical reports and well cores data show the high validity of these models.