Continuous reservoir pressure data acquisition through intrusive well intervention has always been a challenge to the industry due to a combination of economic, operational and logistical constraints. This paper presents an alternative methodology wherein the reservoir pressure can be computed from the well’s steady-state flow properties and wellhead shut-in data using an advanced transient well algorithm. The key research element was the incorporation of a mass transfer rate model into dynamic wellbore modeling for shut-in well. A mathematical workflow which incorporated the time dimension as inherited in the mass transfer theory was devised. This is a major advance on prior state-of-the-art models that assume instantaneous mass transfer upon equilibrium. This enhanced dynamic well model is able to simulate the fluid redistribution during well shut-in, by determining an accurate volume of each fluid phase at any cell in the well over time. Hence, the bottom-hole pressure over time can be calculated by adding the static head to the wellhead pressure. The bottom-hole near wellbore reservoir pressure is obtained when the well has reached equilibrium. The model also rigorously accounts for various transient behaviors of the reservoir fluids during shut-in including the reservoir fluid influx. A number of engineering concepts were evaluated and all the elements that became the foundation of this novel methodology were presented. A sample well was used to demonstrate the robustness of the model in simulating the hydrocarbon fluid redistribution during well shut-in. A better description of wellbore dynamic behavior was obtained, ensuring an improved accuracy in transient well performance modeling. This enhanced dynamic well model provides a vital tool for reservoir pressure determination, replacing subsurface data acquisition with surface data acquisition without well intervention. The model enables operating costs reduction associated with subsurface data acquisition, in addition to improving key data availability which will optimize production plans and recovery improvement opportunities.