Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs

• Main Authors: Jie Yu, Linqi Zhu, Ruibao Qin, Zhansong Zhang, Li Li, Tao Huang
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Geofluids
• Online Access: http://dx.doi.org/10.1155/2021/9321565

The accurate calculation of the gas content of coalbed bed methane (CBM) reservoirs is of great significance. However, due to the weak correlation between the logging response of coalbed methane reservoirs and the gas content parameters and strong nonlinear characteristics, it is difficult for conventional gas content calculation algorithms to obtain more reliable results. This paper proposes a CBM reservoir gas content assessment method combining K-means clustering and random forest. The K-means clustering is used to divide the reservoirs and distinguish the types to establish a random forest model. Judging from the evaluation effect of the research block, the prediction accuracy of the new method is significantly higher than that of the original method, and more accurate gas content prediction values can be obtained for different types of reservoirs. Studies have shown that this method can help the gas content evaluation of CBM reservoirs, improve the accuracy of gas content evaluation, and better support the exploration and development of CBM reservoirs. The results of this study show that the random forest method based on clustering can effectively distinguish the relationship between different logging responses and gas content. On this basis, the random forest algorithm modeling can effectively characterize the complex relationship between gas content and logging curve response. In the case of poor correlation between gas content and logging curve, the gas content of the reservoir can also be accurately calculated.