Fluid-pore relationships in tight oil shales: Insights from sequential solvent extraction and advanced rock analysis

This study presents an integrated, multi-scale laboratory workflow designed specifically for organic-rich shales using multistage solvent extraction. Applied to oil shales of the Bazhenov Formation of varying maturity and lithology, the key unconventional play in Western Siberia, it enables the construction of a robust, volumetric fluid saturation model. The workflow combines mineralogical characterization, conventional core testing, low-field nuclear magnetic resonance relaxometry, high-resolution X-ray computed microtomography, Rock-Eval pyrolysis, and sequential saturates, aromatics, resins, and asphaltenes fractionation following a three-stage solvent extraction protocol. The core analysis following three-step extraction provides new insights into the interplay between lithology, pore system architecture, and fluid distribution mechanisms within tight, organically heterogeneous media. Key findings highlight that conventional methods often underestimate producible hydrocarbons trapped in kerogen nanopores and asphaltene aggregates, necessitating revised nuclear magnetic resonance interpretation approaches. Mechanically induced porosity, varying with organic matter maturity, is identified and linked to hydrocarbon release and matrix deformation. Combining nuclear magnetic resonance and gas porosity measurements provides a rapid, accurate porosity estimation method with minimal sample alteration. Finally, a conceptual fluid physical model is proposed to better interpret nuclear magnetic resonance data and pore-scale fluid dynamics in similar oil shales. The refined methodology of express core assessment significantly improves industry conventional practices by enabling a more precise and physically meaningful quantification of in-situ fluid saturation, including differentiation between bound heavy hydrocarbons and mobile fractions. Beyond advancing the fundamental understanding of fluid saturation and storage capacity in unconventional systems, this framework supports improved reservoir characterization and modeling efforts.