Gravity measurements as a calibration tool for geothermal reservoir modelling

•A suite of Python scripts calculates change in gravity signals from geothermal reservoir model outputs.•Combining forward models with PEST allows semi-automated model calibration.•Gravity data is most sensitive to porosity, permeability, fracture volume and relative permeability.•Calibrating against gravity data highlights aspects of a reservoir model that may need refining conceptually. Gravity measurements are sensitive to changes in mass due to subsurface fluid flow, which is vital to understand for sustainable management of production and reinjection at geothermal reservoirs. We here present a methodology to calculate changes in gravity from TOUGH2 numerical reservoir models, combining it with PEST analysis to create a semi-automated methodology for geothermal reservoir model calibration. This process can also provide statistical information about model parameter sensitivity. Comparing a simplified geothermal reservoir model with a real-world, high-temperature case study shows that gravity data is most sensitive to porosity, permeability, fracture volume and relative permeability. Refining several model parameters simultaneously in the real-world case study allows us to reduce the misfit between modelled and measured gravity changes by 20% compared to calibrating against well data alone. This process also highlights aspects of the reservoir model that may need refining conceptually.