Quantitative kinetic modeling of methane generation and δ13C signals for multiple samples in closed-system pyrolysis

Hydrocarbon generation kinetics are influenced by complex factors, including temperature, reaction time, pressure, and molecular structure, which render simple modeling approaches inadequate for accurately simulating methane generation. The closed-system pyrolysis experiment, a common method to study hydrocarbon generation, poses challenges for kinetic parameter regression due to limited data points. This limitation necessitates the application of sophisticated data analysis techniques to extract meaningful insights from sparse experimental data. This paper establishes a quantitative relationship between methane production and the thermal process through closed system pyrolysis experiments. A nonlinear regression model using multiple algorithms is established based on this quantitative relationship. Accordingly, a method that can quantitatively invert the methane generation kinetic parameters corresponding to the samples based on the experimental data is provided. Based on this theoretical model, a computer program capable of processing experimental data is designed and implemented. Practical analyses are performed using the method above for three samples: a coal sample from the Yulong, Guizhou; a solid bitumen sample from Guangyuan, Sichuan; and a marlstone sample containing type I kerogen from Luquan, Yunnan. The results obtained agree with the qualitative estimates based on hydrocarbon generation kinetic theory using the previous method. Thus, the validity of the new data processing method, the new mathematical model, and the data processing procedures are verified. • The inadequacies of current methods for analyzing closed-system pyrolysis data. • The development of a unified model for open and closed-system pyrolysis data analysis. • The derivation of accurate kinetic parameters for hydrocarbon generation. • The application of these parameters to reconstruct hydrocarbon generation history and interpret isotopic signals.