This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-wellbore stress estimates generated by the Machine Learning (ML) predictive model presented in a previous report, which is linked below as "Machine Learning for Well 16A(78)-32 Stress Predictions". Combined ML and physics-based Finite Element model was applied to translate the near-field stresses to stresses away from the wellbore/cooling-influenced zone. The thermo-poro-mechanical effect by pre-cooling circulation prior to well logging in an enhanced geothermal system (EGS) well was accounted for in the stress predictions at Well 16A (78)-32.