Shale/tight gas plays an increasingly important role to meet the growing global energy demand and reduce carbon emissions. Unlike conventional reservoirs, shale formations are subject to rock heterogeneity and have pore size distributions ranging from sub-1 nm to a few micrometers. Thanks to the large number of nanosized pores, adsorbed methane capacity plays a dominant role in total shale gas-in-place. Methane adsorption behaviors can vary drastically in micropores and mesopores, and rock surface type may also greatly affect its adsorption. In this review, we provide a systematic discussion on measurements of shale rock properties including rock compositions and pore structures such as specific surface area (SSA) and pore size distribution (PSD), which are important parameters for methane adsorption in shale nanoporous media. We also provide in-depth discussions on experimental measurements on methane (excess) adsorption in shale nanoporous media, methane adsorption behavior characterization based on molecular simulations, and various excess-adsorption-to-absolute-adsorption conversion methods. We pay particular attention to the assumptions and working mechanisms proposed in various interpretation methods which are embedded in pore structures (SSA and PSD) and absolute adsorption characterizations. In the end, we summarize the key challenges in the methane adsorption characterization in shale media.

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Accurate characterization of the bubble point pressure of hydrocarbon mixtures under nanoconfinement is crucial to the prediction of ultimate oil recovery and well productivity of shale/tight oil reservoirs. Unlike conventional reservoirs, shale has an extensive network of tiny pores in the range of a few nanometers. In nanopores, the properties of hydrocarbon fluids deviate from those in bulk because of significant surface adsorption. Many previous theoretical works use a conventional equation of state model coupled with capillary pressure to study the nanoconfinement effect. Without including the inhomogeneous molecular density distributions in nanoconfinement, these previous approaches predict only slightly reduced bubble points. In this work, we use density functional theory to study the effect of nanoconfinement on the hydrocarbon mixture bubble point pressure by explicitly considering fluid−surface interactions and inhomogeneous density distributions in nanopores. We find that as system pressure decreases, while lighter components are continuously released from the nanopores, heavier components accumulate within. The bubble point pressure of nanoconfined hydrocarbon mixtures is thus significantly suppressed from the bulk bubble point to below the bulk dew point, in line with our previous experiments. When bulk fluids are in a two-phase, the confined hydrocarbon fluids are in a single liquid-like phase. As pore size increases, bubble point pressure of confined fluids increases and hydrocarbon average density in nanopores approaches the liquid-phase density in bulk when bulk is in a two-phase region. For a finite volume bulk bath, we find that because of the competitive adsorption in nanopores, the bulk bubble point pressure increases in line with a previous experimental work. Our work demonstrates how mixture dynamics and nanopore−bulk partitioning influence phase behavior in nanoconfinement and enables the accurate estimation of hydrocarbon mixture bubble point pressure in shale nanopores.

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Surface area is an important parameter for the estimation of methane (CH4) adsorption in shale nanoporous media. Kerogen, as the main constituent of shale organic matters, has exceptionally high surface area due to extensive nanoscale pores. The Brunauer–Emmett–Teller (BET) method has been extensively used to characterize the surface area of various porous materials. However, its applicability for the surface area characterization of kerogen mesopores has not been investigated yet. In this work, the effect of geometrical and energetical heterogeneity on N2 adsorption isotherms and the subsequent BET surface area (S BET) characterization is studied by using grand canonical Monte Carlo simulations. We find that N2 adsorption sites are mainly within the “basin” and “valley” regions on kerogen surfaces, while in the “ridge” regions, its adsorption rarely takes place at 77 K from 0.005 to 0.05 bar. On the other hand, surface chemistry shows a significant effect on external potential and N2 adsorption amount. In addition, while S BET agrees well with geometric surface area (S geo) in graphite mesopores, in kerogen and pseudokerogen mesopores, S BET is generally lower than S geo. Interestingly, for our samples, S BET correlates well with CH4 excess adsorption in kerogen mesopores at 333.15 K and 300 bar, outperforming S geo. This work provides some crucially important fundamental understanding about the S BET characterization of kerogen mesopores which can guide the prediction of CH4 adsorption capacity in kerogen nanoporous media and the estimation of shale gas-in-place.

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Summary Gas-alkane interfacial tension (IFT) is an important parameter in the enhanced oil recovery (EOR) process. Thus, it is imperative to obtain an accurate gas-alkane mixture IFT for both chemical and petroleum engineering applications. Various empirical correlations have been developed in the past several decades. Although these models are often easy to implement, their accuracy is inconsistent over a wide range of temperatures, pressures, and compositions. Although statistical mechanics-based models and molecular simulations can accurately predict gas-alkane IFT, they usually come with an extensive computational cost. The Shardt-Elliott (SE) model is a highly accurate IFT model that for subcritical fluids is analytic in terms of temperature T and composition x. In applications, it is desirable to obtain IFT in terms of temperature T and pressure P, which requires time-consuming flash calculations, and for mixtures that contain a gas component greater than its pure species critical point, additional critical composition calculations are required. In this work, the SE model is combined with a machine learning (ML) approach to obtain highly efficient and highly accurate gas-alkane binary mixture IFT equations directly in terms of temperature, pressure, and alkane molar weights. The SE model is used to build an IFT database (more than 36,000 points) for ML training to obtain IFT equations. The ML-based IFT equations are evaluated in comparison with the available experimental data (888 points) and with the SE model, as well as with the less accurate parachor model. Overall, the ML-based IFT equations show excellent agreement with experimental data for gas-alkane binary mixtures over a wide range of T and P, and they outperform the widely used parachor model. The developed highly efficient and highly accurate IFT functions can serve as a basis for modeling gas-alkane binary mixtures for a broad range of T, P, and x.