Pore structure evolution of Qingshankou shale (kerogen type I) during artificial maturation via hydrous and anhydrous pyrolysis: Experimental study and intelligent modeling

Bo Liu, Mohammad-Reza Mohammadi, Zhongliang Ma, Longhui Bai, Liu Wang, Yaohui Xu, Abdolhossein Hemmati-Sarapardeh and Mehdi Ostadhassan

Energy, 2023, vol. 282, issue C

Abstract: In this study, alterations in the pore structure of the Qingshankou shale during hydrous and anhydrous pyrolysis (HP and AHP) over wide ranges of temperature (300–450 °C) were studied to elucidate the role of water. Mineralogy of the original shale sample along with geochemical properties and pore structure after each step of artificial thermal maturation were analyzed using X-ray diffraction (XRD), Rock-Eval pyrolysis, and N2 adsorption, respectively. Complexity of pore structure was assessed via fractal dimension analysis followed by delineating different pore families in the samples' PSD curves. Next, four common artificial neural networks (ANNs) models were implemented along with Bayesian regularization (BR) and Levenberg-Marquardt (LM) optimization algorithms to model the N2 adsorption/desorption volume as thermal maturity progressed. Results showed that all samples represent Type IV hysteresis loop while the amount of N2 adsorption during HP was much higher than that of the AHP revealing the importance of water. Overall, the complexity of AHP samples’ pore structure was less than that of the HP since the count of pores with smaller pore width was reduced more during thermal progression. Deconvolution analysis showed that the original sample has eight pore families (7 in mesopore and 1 in the macropore range), while pore families with larger mean pore width were formed and expanded and pore families with smaller mean pore width were eliminated as thermal maturation progressed in both HP and AHP. Moreover, the average absolute percent relative error (AAPRE) of 3.74% for the entire data inferred that generalized regression neural network (GRNN) model was superior for the estimation of N2 volume adsorbed and desorbed. Finally, sensitivity analysis revealed that the relative pressure and pyrolysis method (AHP or HP) respectively had the strongest linear and non-linear relationships with the N2 adsorption/desorption volume. Collectively, this case confirmed the role that water plays in the evolution of pore structures throughout thermal maturation, which could be even more significant than temperature.

Keywords: Qingshankou shale; Hydrous and anhydrous pyrolysis; N2 adsorption; Deconvolution analysis; Fractal dimension; Artificial neural networks (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s036054422301753x

DOI: 10.1016/j.energy.2023.128359

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