Distributed acoustic sensing (DAS) enables vertical seismic profile (VSP) acquisition along the entire length of a wireline cable during routine logging runs, eliminating a dedicated geophone pass and improving operational efficiency. Accurate DAS measurements in vertical wells, however, require reliable cable–wellbore coupling, typically achieved by introducing controlled slack to induce helical buckling—an operation that is often tuned by trial‑and‑error, with risks of over‑deformation, inconsistent coupling, and depth mis‑registration. We present a finite element analysis (FEA) framework and a fast surrogate for cable slack and coupling prediction tailored to field decision‑making. The cable is modelled as a homogenized beam with calibrated axial‑bending response; the wellbore is treated as a rigid constraint with frictional contact to capture gravity‑driven helical buckling and contact length evolution. High‑fidelity simulations across practical ranges of slack, stiffness, and friction generate a design‑of‑experiments dataset, from which we derive a response‑surface model that predicts buckling onset, pitch, and borehole contact length under varying job conditions. The surrogate model yields near‑instantaneous predictions that (i) inform the de-tensioning required to achieve robust cable–wellbore coupling while avoiding excessive deformation and (ii) provide depth‑correction factors for DAS traces by mapping measured responses to the predicted contact‑length distribution. Integration with field workflows enables calibration using observed DAS‑VSP contact length as a function of applied slack, thereby improving accuracy without additional instrumentation. Relative to manual trial‑and‑error procedures, operational time and risk are reduced while maintaining a transparent, physics‑informed basis for decision‑making. Validation is performed against field observations, and sensitivity analyses quantify the influence of the friction coefficient and cable mechanical properties. Extensions to reliability analysis, uncertainty quantification (UQ), and design optimization are outlined for future work.