<p>A detailed understanding of reservoirs is crucial for effective hydrocarbon exploration and production. However, despite technological advancements, accurately characterizing complex reservoirs, such as those in the Lower Goru Formation (LGF) of the Sawan Gas Field (SGF), Pakistan, remains challenging due to heterogeneous lithology. Existing studies often lack a comprehensive integration of seismic, petrophysical, and machine learning approaches, creating a gap in complete reservoir characterization. To address this problem, this study integrates seismic interpretation, well-log analysis, and rock physics modeling to enhance reservoir characterization in the LGF. The stratigraphic and structural features of the study area were analyzed using 2-D seismic lines. Critical reservoir zones were delineated based on petrophysical parameters obtained from several logs. A Self-Organizing Maps (SOMs) based unsupervised machine learning technique was utilized to categorize electrofacies. Cross-plots of P-impedance vs. Vp/Vs and lambda-rho vs. mu-rho were plotted to distinguish lithologies and fluid distributions within the reservoir. Seismic interpretation identified three horizons (D-sand, C-sand, and B-sand) with a southeast-deepening trend and a shallower profile toward the northwest. The self-organizing map identified four main facies: sandstone, shaly sandstone, sandy shale, and shale. The N/M cross-plot analysis confirmed these classifications and revealed a mineral composition predominantly of quartz, validating the reliability and precision of the electrofacies results. Petrophysical interpretation identified two reservoir intervals in the Sawan-08 well and one in each of the Sawan-01 and Sawan-07 wells, all within the B and C sand levels. Rock physics modeling validated these findings by correlating predicted and actual wireline log data, confirming the precision of the model in estimating P- and S-wave velocities. Additionally, elastic parameter cross-plots effectively differentiated fluid types within the reservoir zones as wet sand, gas sand, shale, and shaly sand. This comprehensive methodology provides significant insights into the reservoir characteristics of LGF. It facilitates more precise hydrocarbon resource assessment, optimizes exploration initiatives, and refines reservoir management tactics, ultimately improving production in the area.</p> Graphical abstract <p>The graphical abstract presents a structured workflow for reservoir characterization in the Lower Goru Formation (LGF) of the Sawan Gas Field, Pakistan, integrating seismic data (SEG-Y) and multi-scale well logs (caliper, gamma ray, sonic, resistivity, photoelectric, den-sity, and neutron). The study begins with seismic interpretation, where horizons were mapped using formation tops and synthetic seismograms, revealing a distinct southeast-deepening, northwest-shallowing trend in the D-Sand, C-Sand, and B-Sand units through structural contour maps. To ground these seismic observations in reservoir properties, petrophysical analysis was conducted, identifying two hydrocarbon-bearing intervals in the Sawan-08 well and one interval each in Sawan-01 and Sawan-07, primarily within the B-Sand and C-Sand levels. Cross-plots of log responses further constrained the lithology, confirming the reser-voir’s sandstone-dominated composition. Building on this lithological framework, an unsu-pervised machine learning approach using Self-Organizing Maps (SOM) was employed to classify electrofacies, delineating four distinct rock types: sandstone, shaly sandstone, sandy shale, and shale. To bridge facies classification with fluid dynamics, rock physics modeling was performed using elastic cross-plots (P-impedance vs. Vp/Vs and lambda-rho vs. mu-rho), which successfully discriminated lithologies and fluid types, including gas sand, wet sand, shaly sand, and shale. Together, this workflow unifies seismic stratigraphy, petrophysical evaluation, machine learning, and rock physics to better understand reservoir potential in the LGF.</p>