A new approach for oil saturation prediction in marl reservoirs based on organic pore correction

Marlstone reservoirs stand as the pivotal and primary target formations throughout the entire process of shale oil exploration and development. Oil saturation functions as the fundamental core parameter utilized for characterizing the degree of hydrocarbon enrichment within reservoir rocks and assessing the inherent development potential of subsurface reservoir systems, and its corresponding prediction accuracy exerts a decisive influence on both the screening efficiency of favorable sweet-spot zones and the scientific rigor and rationality in the formulation of practical development strategies for shale oil reservoirs. The conventional Archie model was initially formulated and established on the basis of a set of idealized assumptions that are exclusively applicable to relatively homogeneous sandstone reservoirs. When this classical model is implemented and utilized in marlstone reservoirs characterized by highly complex sedimentary environments and significant reservoir heterogeneity, it is confronted with prominent defects such as insufficient accuracy in the prediction of oil saturation. To address the inherent applicability limitations of conventional methodologies, the present study has conducted systematic petrophysical investigations targeting the Es_3x marlstone reservoir intervals within the Shulu Sag, culminating in the development of an advanced Archie model modification methodology incorporating organic porosity corrections. Initially, compute total porosity utilizing an enhanced Herron petrophysical model incorporating joint corrections for pore fluids and organic constituents, subsequently transform mineral mass fractions derived from elemental spectroscopy logging into mineralogical volume fractions. Establish a volumetric quantification model for organic matter fraction through integration of total organic carbon concentration, kerogen-bitumen density characteristics, and formation bulk density measurements, subsequently formulating a multivariate linear regression relationship between transformed mineralogical volume constituents and dry matrix grain density parameters. Calibrate and correct density-derived porosity and neutron-derived porosity measurements, then optimize these values to obtain total porosity. Subsequently, computationally derive organic-hosted porosity through petrophysical integration of total organic carbon concentration and kerogen transformation ratio parameters, then arithmetically isolate inorganic porosity by subtracting this quantitatively determined organic-hosted porosity component from the pre-established total porosity value. Ultimately, apply the Archie formation resistivity relationship, calibrated specifically for organic porosity systems, to predict hydrocarbon saturation distributions throughout the study area's reservoir interval. The results indicate that the organic porosity-corrected Archie model enhances the physical plausibility of the relationship between porosity and saturation, significantly improves prediction accuracy, and demonstrates strong adaptability to the complex geological settings and pronounced heterogeneity of the study area. This model provides technical support for the identification of “sweet spot” zones, productivity potential evaluation, and development plan optimization in marlstone reservoirs.