Application of Machine Learning in Predicting Formation Condition of Multi-Gas Hydrate

Abstract

Thermodynamic models are usually employed to predict formation condition of hydrates. However, these thermodynamic models usually require a large amount of calculations to approach phase equilibrium. Additionally, parameters included in the thermodynamic model need to be calibrated based on the experimental data, which leads to high uncertainties in the predicted results. With the rapid development of artificial intelligence (AI), machine learning as one of sub-discipline has been developed and been widely applied in various research area. In this work, machine learning was innovatively employed to predict the formation condition of natural gas hydrates to overcome the high computation cost and low accuracy. Three data-driven models, Random Forest (RF), Naive Bayes (NB), Support Vector Regression (SVR) were tentatively used to determine the formation condition of hydrate formed by pure and mixed gases. Experimental data reported in previous work were taken to train and test the machine learning models. As a representative thermodynamic model the Chen–Guo (C-G) model was used to analyze the computational efficiency and accuracy of machine learning models. The comparison of results predicted by C-G model and machine learning models with the experimental data indicated that the RF model performed better than the NB and SVR models on both computation speed and accuracy. According to the experimental data, the average AADP calculated by the C-G model is 7.62 times that calculated by the RF model. Meanwhile, the average time costed by the C-G model is 75.65 times that by the RF model. Compared with the other two machine learning models, the RF model is expected to be used in predicting the formation condition of natural gas hydrate under field conditions.