Empowering Petrophysics: Elevating Thin-Bed Analysis with Integrated Advanced Logging and Digital Workflows

Abstract In response to domestic and international energy demands, oil and gas companies are positioned to discover new reserves by making investments in exploration activities. The oil and gas discovery must meet economic criteria and be feasible for development. Low- resistivity pay reservoirs remain a topic of interest because the characterization of these reservoirs containing laminated shaly sand hydrocarbons and thin-bed layers have traditionally been challenging. The standard resistivity techniques often underestimate hydrocarbon assessments due to presence of conductive laminated shale, resulting in the field or block to fall into marginal or sub commercial categories. Understanding reservoir properties is essential to estimating the hydrocarbons in place because assessing net pay and hydrocarbons saturation presents challenges in reservoirs characterized by high-shale content. This paper presents a novel approach for integrating logging measurements, including nuclear magnetic resonance (NMR), 3D resistivity, and borehole imaging, with advanced laminated sand analysis (LSA) to refine reservoir characteristics and determine accurate formation properties. The LSA method incorporates 3D resistivity and NMR data, employing probabilistic methods to calculate true reservoir properties of sand fraction and resistivity, and quantify laminated shale content. LSA results undergo standard petrophysical interpretation using deterministic approaches, ensuring consistency across the analysis. Formation evaluation employing probabilistic techniques further refines mineral volumes and corrected porosity, essential inputs for interpreting integrated well logs. NMR-factor analysis across the entire interval, with T2 cutoff values distinguishing clay and free fluid, enables computation of clay-silt-sand volume and effective porosity to help calibrate the petrophysical model. Advanced techniques, including 3D resistivity anisotropy, dip-corrected outputs, NMR poro-facies, and factor analysis contribute to precise reservoir property determination. Digital workflows enable rapid data processing and analysis, providing valuable insights within time constraints. This comprehensive approach significantly enhances reservoir understanding, producing improved hydrocarbon estimation. The results indicate substantial increases in net pay and hydrocarbon pore volume, highlighting the effectiveness of this innovative thin-bed analysis approach to maximizing hydrocarbon recovery potential in challenging shaly sand environments.