Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

•An approach is proposed for physics-based formulation of deep-learning problems relevant to CO2 injection.•Deep-learning-based surrogate models were developed using the proposed methodology and are shown to be capable of substituting as computationally efficient surrogates for traditional numerical simulators during history matching.•Reservoir pressure, CO2 saturation plume, and water extraction rate can be rapidly predicted given formation properties, reservoir mesh, well operating conditions, and initial and boundary conditions.