An Effective Medium Model for Assessment of Wettability in Organic-Rich Mudrocks

Characterizing the wettability of organic-rich mudrocks (ORMs) is essential for accurate reservoir evaluation and hydrocarbon production. However, the complex composition and pore structure of ORMs limit the effectiveness of conventional wettability assessment methods. This work introduces a new workflow that combines water adsorption measurements with a forward model and inversion algorithm to analytically estimate water adsorption isotherms for wettability evaluation in ORMs. The forward model estimates water adsorption isotherms using input parameters that describe the adsorption behavior and concentration of each rock component. These water adsorption parameters are derived through an inversion algorithm, which uses rock composition and experimental adsorption data to determine component-specific adsorption properties for a given rock type. Once these parameters are estimated, the forward model can be used to analytically estimate the water adsorption behavior on rock samples from the same rock type without further laboratory measurements. The inversion algorithm was applied to five powdered ORM samples from the same formation and rock type, with one sample excluded for verification. Adsorption parameters obtained for each rock component, combined with rock composition data, were used to analytically estimate the water adsorption isotherm of all core samples. The estimated isotherm closely matched experimental measurements, with mean relative differences below 14%. These results demonstrate that, once adsorption parameters are established, wettability in other samples can be reliably assessed without further experiments. The proposed method presents a dependable method for assessing the wettability of rocks with complex composition. It also has the potential of enabling real-time evaluation of water wettability throughout the formation at any given depth using borehole geophysical measurements, enhancing reservoir characterization.