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

作者：Jie Zhu, Yuhan Zhao, Qiujia Hu, Yang Zhang, Tangsha Shao, Bin Fan, Yaodong Jiang, Zhe Chen, Meng Zhao · 发表于：ACS Omega · 年份：2022 · DOI：10.1021/acsomega.2c00519 · 被引用次数：32 · 研究领域：Coal Properties and Utilization、Hydrocarbon exploration and reservoir analysis、Hydraulic Fracturing and Reservoir Analysis

High Resolution Image Download MS PowerPoint Slide 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 )...