Coalbed Methane Production Model Based on Random Forests Optimized by a Genetic Algorithm

[Image: see text] It is of great significance to evaluate and predict coalbed methane (CBM) production for the exploitation and exploration of CBM. The flow characteristics of gas and water are very complicated and important in the process of CBM exploitation. In recent years, machine learning has been introduced to analyze CBM well production and its influence based on the historical production data. However, there are some problems with the determination of hyperparameters in machine learning algorithms. Some previous random forests (RF) models of CBM production prediction were suitable for individual CBM wells, but for different types of CBM wells, a large amount of time is needed to adjust the hyperparameters. Therefore, a genetic algorithm (GA) was applied to optimize RF, and a hybrid GA–RF algorithm was presented to solve this problem, which can automatically adjust two important hyperparameters, n(tree) and m(try), and adapt different types of CBM wells. Meanwhile, the Pearson method and RF were carried out in this work to analyze the data of CBM well production to avoid multicollinearity caused by the improper selection of the model’s independent variables. The importance and correlation analysis of drainage control parameters, including casing pressure (P(c)), bottom-hole pressure (P(b)), stroke frequency (f(s)), liquid column depth (D(L)), daily decline of bottom-hole pressure (P(bd)), and daily decline of casing pressure (P(cd)) were obtained. It was found that the casing pressure, bottom-hole pressure, and stroke frequency had more effects on the gas production of CBM wells than other drainage control parameters. Furthermore, the correlation and importance order of the influencing factors were: P(c) > P(b) > f(s) > P(bd) > P(cd) > D(L) and P(c) > P(b) > f(s) > D(L) > P(bd) > P(cd), respectively. A CBM production model based on the GA–RF algorithm was constructed to study and predict the gas production of CBM wells in Qinshui Basin, China. Compared with the production model based on RF, this model can automatically optimize its hyperparameters to adapt to different types of CBM wells, and the mean-square-error of the GA–RF algorithm can be reduced by 40–60% than that of RF. 93% of the training errors were less than 5%, and 89% of the prediction errors were less than 10%. The GA–RF model can spot promptly the main influencing factors of CBM production and has high accuracy for the production prediction of CBM wells.