We present an innovative approach to simulating flow tests of production wells operating in clastic reservoirs of oil fields in the Perm Region. To solve this issue, modern machine learning solutions (CatBoost, Random Forest, XGBoost, MLP, Gradient Boosting, etc.) were used, which allowed achieving high prediction accuracy. The main parameter for simulating and research is bottomhole pressure at various stages of its recovery during well flow testing. The use of the SHAP model interpretation method for the first time made it possible to assess the impact of geotechnical parameters on bottomhole pressure and identify key ones among them. Analysis of the bottomhole pressure recovery prediction model sensitivity to changes in initial parameters made it possible to evaluate the degree of their influence on the pressure recovery curves (PRC). The uniqueness of the proposed approach lies in studying the significance of parameters at various time stages of bottomhole pressure recovery during flow testing, which allows for a more detailed understanding of the processes occurring under formation conditions. The proposed algorithms made it possible to simulate PRC that are as close as possible to actual data, as well as to study the dynamics of permeability of the remote formation zone in real time. This approach opens up new horizons in simulating flow tests and allows for highly detailed and timely assessment of formation filtration properties across the entire production well stock simultaneously. The process engineering solution is aimed at promptly assessing filtration parameters of remote formation zones and provides the ability to monitor permeability changes, which helps to timely identify areas of reduced oil inflow and develop measures to restore well productivity. This approach significantly reduces economic risks associated with conducting expensive field tests while ensuring reliability and validity of predicted indicators with minimal resource and time costs.