<p>Reliable reservoir facies modeling is paramount for optimizing hydrocarbon resource management in heterogeneous reservoirs. However, developing a rigorously validated workflow that integrates multi-source subsurface data for mixed clastic-carbonate formations remains a persistent challenge. This study innovatively addresses such challenges by developing and validating a systematic methodology that integrates seismic facies with well-log-based electrofacies for the complex Oligo-Miocene Asmari reservoir in a southern Iran oilfield. Our approach combines electrofacies characterization, calibrated with core data and hydraulic flow units, with the incorporation of a four-class seismic facies cube as a secondary constraint through the Sequential Indicator Simulation (SIS) and Truncated Gaussian Simulation (TGS) frameworks. The innovation lies not only in the integration itself but critically in its rigorous quantitative validation through comprehensive blind well testing, supported by a suite of per-class performance metrics (Precision, Recall, and F1-Score). This validation reveals a substantial enhancement in predictive accuracy: SIS model accuracy surged from 49.36% to 61.87%, and TGS model accuracy increased from 47.05% to 58.36% upon seismic facies integration. The primary importance of this research is the provision of a robust, quantitatively validated workflow that demonstrably reduces uncertainty in facies prediction. This provides a clear pathway toward more reliable geological models and improved decision-making for field development in geologically complex settings.</p>