Understanding the properties of kerogen is necessary for a better assessment of shale gas and shale oil prospects. It is also essential to understand the physics of in-situ oil shale retorting. 

Thanks to the availability of well-tested computational methods, molecular modeling can aid in directing and optimizing the value of costly and time-consuming experimentation. This talk illustrates the capabilities of molecular modeling to predict thermodynamic properties of kerogen as well as the transport of fluids through kerogen.

First, we create model kerogen structures for type I (Green River Shales) and type II (marine origin) at various maturity levels. Using literature data about atomic composition in H, O, N, S, aromaticity, average chain length, size of polyaromatic units, and type of NSO containing units, we build models of moderate size (200 to 500 carbon atoms) [1].

These model structures are relaxed by molecular dynamics (MD), using successive NPT simulations at decreasing temperatures with the pcff+ forcefield [2, 3]. Final densities agree with reported experimental measurements for type I and type II kerogens and with known structural features (spontaneous organization of polyaromatic units in parallel stacks). Thermal expansion and bulk elastic moduli can be deduced. Interestingly, kerogen structure appears glassy by some features (the location of polyaromatic nuclei changes very slowly) and not by others (alkyl chains are mobile at the local level).

Finally, the interactions of kerogen with fluids are investigated. MD simulations allow determination of thermodynamic properties (partial enthalpies, partial molar volume of fluids in kerogen phase). Grand Canonical Monte Carlo (GCMC) provides adsorption isotherms of gases like methane or ethane in the kerogen micropores under shale gas reservoir conditions [1, 4-7]. Diffusion of gas molecules through kerogen is modeled using non-equilibrium molecular dynamics (NEMD). Comparison of computed properties with experimental data is encouraging, and ways of improving predictions are outlined in the conclusions.