CO2 sequestration and monitoring has become key in reducing CO2 emissions. Its simula- tion at scale requires a fully integrated multi-physics platform based on multiphase flow, geomechanics and elastic wave propagation advanced solvers. All of them are relying on advanced numerical methods based on unstructured finite element and finite volume approximations. Ensuring the safety and acceptability of CO2 geological storage requires to interconnect and couple all these different physics solvers together and build efficient workflows to simulate the injection and the monitoring of the evolution of the plume of CO2 into the saline aquifer, to model the geomechanical deformation of the subsurface and to improve the image of the plume of CO2, the reservoir properties and geometry, thanks to the use of seismic modeling and full wave form inversion. The safe and acceptable sequestration of CO2 and its monitoring require to simulate whole regions spanning tens of kilometers for a duration up to a few centuries. Consequently, the need to run faster and more correct solvers will demand to increase the accuracy of our workflows with the introduction of more complex physics, advanced numerical methods based on high order approximation both in space and in time relying on optimized hybrid meshes. The setup of realistic uses case and demonstrators at scale will be mandatory to prove the value of our development and will target accelerator based exascale capability resources, the only technology available today and in the short term to offer us the necessary computing power. With this goal in mind, an ambitious public-private joint research project has been set up in 2022 by TotalEnergies and Inria, named “Makutu”, with the objective to extend the platform GEOSX originally designed for fluid flow simulation and geomechanical to seismic wave propagation in complex media.In this work, we present the first-year results of the Makutu project, and its implications in terms of research and industrial impact. In particular, we show how such a close public-private partnership has allowed to develop effective workflows integrating the different phases necessary for the modeling of the injection and monitoring of CO2, exploiting different algorithms requiring different computing powers. We will also discuss the perspectives and future goals of the collaboration, in which machine learning will play an important role to increase the efficiency of the workflows.