Deep geothermal energy allows clean, non-intermittent, heat and/or power production regardless of weather conditions at any hour of the day or night. It will contribute to the decarbonization of our economy reaching its maximum mitigation potential by 2050 (ANCRE, 2015). However, exploitation of subsurface natural resources is faced with an uncertain environment. This is sometimes coined as the geological risk. Whatever the deep geothermal technology - conventional heat mining of deep aquifers, enhanced/engineered geothermal systems or power production in magmatic settings – and its maturity level, this feature makes geothermal operations high-risk projects with substantial initial investments (several M€) related to drilling costs. Even if insurance policies have recently been adapted to new targets, a single exploration failure may deter operators from a region with assumed good potential but complex geology for decades (e.g. the Hainaut aquifer in Northern France in the early 80’s). Better knowledge of the subsurface is then a key bottleneck for the deployment of deep geothermal technologies. It has been observed that the most efficient way to mitigate the geological risk is the collaborative integration of multidisciplinary data and interpretations into a geomodel of the subsurface. In a geothermal context, the first goal of such conceptual models is the prediction of the spatial distribution of temperature. Then, in order to reach economic profitability, deep geothermal projects need power levels that require convective exchanges with the reservoir at high flow rate through production and injection wells. Parallel to that, transient convective processes, which are ubiquitous in high temperature magmatic settings, also control the temperature distribution and the natural state of many sedimentary basins and basement type geothermal plays. Aforementioned conceptual models must consequently be dynamic by nature and integrate subsurface mass and energy transfers controlled by multiscale geological structures. Numerical simulation has become a powerful method for scientific inquiry on par with experimental and theoretical approaches, especially when data are as scarce and heterogeneous as subsurface data. Moreover, much progress has been made during the last decades in static geological modeling, dynamic geothermal reservoir modeling and performance computing with several contributions from CHARMS’ partners. Yet, many developments are still largely independent and confined to academic circles. There is no off-the-shelf software that integrates all of them in a consistent framework. The main objective of CHARMS is to take that step further and deliver the foundations components of an open framework so that integrated dynamic conceptual models of geothermal systems in complex geological settings can be produced from the early phases of exploration, to increase the probability of success, and evolve continuously through collaborative contributions into operational reservoir models to guarantee sustainable exploitation. The project is based on the three following pillars: • a consistent framework to link evolutionary complex geological models and the definition of the nonlinear physics of geothermal flows, • the improvement of the parallel ComPASS platform, which already has promising results, with numerical schemes tailored to accurately model multiphase multicomponent geothermal flows on unstructured meshes with discontinuities (fault, fractures…), • baseline validation tests and industrial cases, including complex well geometries, to assess the usefulness of the new tools. CHARMS gathers scientists who know each other and have a strong experience in subsurface modeling activities. BRGM (French Geological Survey) will lead the project leveraging the numerical expertise of the University of Nice and Paris 6, and the Maison de la Simulations as well as the industrial experience of Storengy (ENGIE Group).