This study presents a comprehensive thermodynamic modelling and analysis to enhance lubrication assessment in tribotests, focusing on ASTM D2783 extreme pressure (EP) and ASTM D4172 anti-wear (AW) tests. The model facilitates the identification of optimal high-performance lubrication and synergistic additive combinations, even for lubricants yielding similar test results. A case study evaluates 36 lubricant samples, including mineral oil, vegetable oil, and blends. Rooted in the entropy balance, the model employs the dissipative coefficient Uw to simulate friction-wear interplay, providing insights into wear conditions, lubrication mechanisms, and efficacy. Both mineral-based and vegetable-based lubricants exhibit analogous friction-wear dynamics, but their compatibility with additives differs, resulting in distinct synergistic packages. Blended base oils’ compositional complexity significantly influences additive synergies, challenging conventional tribotest evaluations. The Uw parameter-based approach empowers the selection of optimal lubricant formulations, aiding in designing effective lubricant solutions by considering wear conditions, lubrication mechanisms, and additive compatibilities