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Dependence of Methane Transport on Pore Informatics in the Amorphous Nanoporous Kerogen Matrix.

Wenhui LiYiling NanZhehui Jin
Published in: Langmuir : the ACS journal of surfaces and colloids (2023)
Fluid transport in kerogen is mainly diffusion-driven, while its dependence on pore informatics is still poorly understood. It is challenging for experiments to identify the effect of pore informatics (such as pore connectivity and tortuosity) on fluid transport therein. Therefore, in this work, we use molecular dynamics simulations to study methane transport behaviors in amorphous kerogen matrices with broad pore properties. The pore properties including porosity, pore connectivity, pore size, and diffusive tortuosity are characterized. Next, self-diffusion coefficients in the connected pores ( D eff S ) and in the total pores without distinguishing its connectivity ( D tot S ) are calculated in all the kerogen matrices based on the free volume theory. We find that both D eff S and D tot S exponentially decreases with methane loading with two controlled parameters: fitting constant α eff and D eff S(0) ( D eff S at infinitely small loading) for D eff S and fitting constant α tot and D tot S(0) ( D tot S at infinitely small loading) for D tot S . However, in the kerogen models with relatively low pore connectivity, α eff and α tot as well as D eff S(0) and D tot S(0) can be quite different, inducing the different estimations of D eff S and D tot S . Since methane in the unconnected pores does not contribute to the actual transport, it is important to recognize connected pores when evaluating the fluid transport in kerogen. On the other hand, D eff S(0) strongly depends on the effective limiting pore size ( r lim_eff ) of the dominant flow path and effective diffusive tortuosity (τ eff ), in which D eff S(0) linearly increases with ( r lim_eff /τ eff ) 2 . We also find that α eff is a multivariable function of ϕ eff , τ eff , and r lim_eff , but their generalized relation requires more data to obtain. This work provides important insights into fluid transport in kerogen based on the kerogen pore informatics, which are important to shale gas development.
Keyphrases
  • molecular dynamics simulations
  • big data
  • electronic health record
  • carbon dioxide
  • artificial intelligence