For decades, the Volumetric Heat-in-Place (HIP) method has served as the preferred method for estimating recoverable heat and electrical power generation capacity in geothermal projects. Calculating probabilistic HIP assessments is a well-established practice using Monte Carlo simulations. However, important considerations remain in selecting the appropriate HIP derivation and accurately characterizing input distribution shapes and value constraints. This study re-examines HIP methodologies to address complexities and potential biases that may influence results. Using a simple Python codebase, probabilistic estimates of stored heat and electrical power are made with different HIP formulations. Results indicate fluid and steam contributions to stored heat are minor, but input distribution choices can significantly affect estimates. Lognormal distributions are recommended to mitigate certainty bias for subsurface parameters, focusing on reservoir area, thickness, and temperature. Sensitivity analysis reinforces the importance of selecting reasonable efficiency and recovery factors for accurate resource assessment. Real-world examples illustrate the methods in action for both hydrothermal and Enhanced Geothermal Systems (EGS), offering guidance on generating calibrated estimate ranges for consistent exploration assessments. When combined with an explorer’s mindset and basic statistical checks for bias, the HIP method can provide both consistency and value to a geothermal exploration program.

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