Identification of Pore Structure and Clay Content from Seismic Data within an Argillaceous Sandstone Reservoir

Sandstone facies are good reservoirs for the accumulation of hydrocarbons in conventional exploration due to high porosity and permeability. Grain size variations within a sandstone reservoir can range from pebbles to shale, depending on the depositional environment. Increasing amounts of shale become a limiting factor in reservoir quality by creating baffles to fluid flow. Seismic inversion has been used to map reservoir properties such as lithology and porosity. Previous studies have established a relationship between acoustic velocity and porosity, but have not accounted for pore structure, and most methods require data that is not easily available in hydrocarbon exploration and production. Rock physics models have been used to differentiate pore structure of spherical quartz grains and elongated clay minerals. Other studies have developed applicable rock physics models for identifying clay content from experimental and well log data in a shaly sandstone reservoir. The purpose of this study was to correlate a rock physics-based petrophysical parameter with seismic attributes in order to map and predict the location of fluid baffles. The project entailed calculating the clay content within the target reservoir, utilizing the Hertz-Mindlin and Sun (HMS) rock physics model to wells logs within the Norne field, offshore Norway. The HMS model provided the ability to correlate clay content with acoustic impedance. A new variable was established that links acoustic impedance to the product of porosity and the pore structure parameter (γ) from Sun’s rock physics model. This new variable allows pore structure to be identified using post-stack seismic inversion. At the well locations, the relationships for acoustic impedance (AI)-porosity (ϕ) and AI-product (γϕ) were developed using the following two equations : AI = A - B * φ and AI = A - B * (γφ) Upon completion of the petrophysical analysis, deterministic seismic inversion was performed, using well log and seismic data to build an inverse model to identify the spatial distribution of clay content within the reservoir. Deterministic seismic inversion generated a best-case reservoir model, which was used to predict zones of increased clay content within the argillaceous sandstone reservoir. Using the established AI-ϕ and AI-γϕ relationships, the ϕ and γϕ were calculated from the acoustic impedance volume, and were depicted spatially and vertically throughout the target formation. The acoustic impedance-product relationship provided a better method of identifying variations in pore structure than the traditional acoustic impedance-porosity relationship. Additionally, the results also showed an increase in resolution using the AI-γϕ relationship. Mapping levels of clay content and porosity using this method can aid in reservoir characterization, field development, and maximizing hydrocarbon production