TY - EJOU
AU - Wang, Junhui
AU - Yan, Wanzi
AU - Wan, Zhijun
AU - Wang, Yi
AU - Lv, Jiakun
AU - Zhou, Aiping
TI - Prediction of Permeability Using Random Forest and Genetic Algorithm Model
T2 - Computer Modeling in Engineering \& Sciences
PY - 2020
VL - 125
IS - 3
SN - 1526-1506
AB - Precise recovery of Coalbed Methane (CBM) based on transparent
reconstruction of geological conditions is a branch of intelligent mining.
The process of permeability reconstruction, ranging from data perception
to real-time data visualization, is applicable to disaster risk warning and
intelligent decision-making on gas drainage. In this study, a machine learning
method integrating the Random Forest (RF) and the Genetic Algorithm
(GA) was established for permeability prediction in the Xishan Coalfield
based on Uniaxial Compressive Strength (UCS), effective stress, temperature
and gas pressure. A total of 50 sets of data collected by a self-developed
apparatus were used to generate datasets for training and validating models. Statistical measures including the coefficient of determination (R2) and
Root Mean Square Error (RMSE) were selected to validate and compare
the predictive performances of the single RF model and the hybrid RF–
GA model. Furthermore, sensitivity studies were conducted to evaluate the
importance of input parameters. The results show that, the proposed RF–GA
model is robust in predicting the permeability; UCS is directly correlated to
permeability, while all other inputs are inversely related to permeability; the
effective stress exerts the greatest impact on permeability based on importance
score, followed by the temperature (or gas pressure) and UCS. The partial
dependence plots, indicative of marginal utility of each feature in permeability
prediction, are in line with experimental results. Thus, the proposed hybrid
model (RF–GA) is capable of predicting permeability and thus beneficial to
precise CBM recovery.
KW - Permeability; machine learning; random forest; genetic algorithm; coalbed methane recovery
DO - 10.32604/cmes.2020.014313