PHREIs: a Python-based screening tool for rapid deterministic and stochastic simulation of geochemical clogging in the near-wellbore zone of geothermal reinjection wells

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Clogging in the near-wellbore zone (NWBZ) of reinjection wells represents a major operational constraint for geothermal doublet systems, leading to progressive injectivity decline and reduced long-term performance. This study presents PHREIs (“PHREEQC-based Reinjection Evaluation of Injectivity with stochastic mode”), an open-source numerical screening framework designed to quantify geochemical clogging and its hydraulic consequences in the NWBZ. PHREIs couples equilibrium geochemical simulations using PHREEQC with a porosity–permeability relationship based on the Kozeny–Carman equation, enabling direct evaluation of mineral precipitation, porosity loss, and permeability reduction. The framework is implemented with a graphical user interface, allowing its application by non-expert users for rapid preliminary assessments. Deterministic simulations show that porosity may decrease approximately linearly under constant precipitation rates, whereas permeability exhibits a strongly non-linear decline. A critical threshold is identified at about 50% of the initial porosity, beyond which permeability decreases rapidly and injectivity loss accelerates. Sensitivity analyses demonstrate that clogging behaviour is strongly controlled by mineral selection, redox conditions, and operational parameters. In particular, open-to-air conditions enhance iron precipitation, while reinjection temperature represents a primary control on clogging dynamics. Stochastic simulations highlight the importance of uncertainty in subsurface properties. Variability in initial permeability produces a wide range of clogging trajectories, including rapid failure scenarios that cannot be captured using deterministic approaches alone. Applications to field-inspired geothermal systems demonstrate that PHREIs can be effectively used as a screening tool to evaluate clogging risk in the NWBZ and support risk-informed decision-making.