  The ability to make accurate predictions with quantified uncertainty provides
a crucial foundation for the successful management of a geothermal reservoir.
Conventional approaches for making predictions using geothermal reservoir
models involve estimating unknown model parameters using field data, then
propagating the uncertainty in these estimates through to the predictive
quantities of interest. However, the unknown parameters are not always of
direct interest; instead, the predictions are of primary importance. Data space
inversion (DSI) is an alternative methodology that allows for the efficient
estimation of predictive quantities of interest, with quantified uncertainty,
that avoids the need to estimate model parameters entirely. In this paper, we
evaluate the applicability of DSI to geothermal reservoir modelling. We first
review the processes of model calibration, prediction and uncertainty
quantification from a Bayesian perspective, and introduce data space inversion
as a simple, efficient technique for approximating the posterior predictive
distribution. We then apply the DSI framework to two model problems in
geothermal reservoir modelling. We evaluate the accuracy and efficiency of DSI
relative to other common methods for uncertainty quantification, study how the
number of reservoir model simulations affects the resulting approximation to
the posterior predictive distribution, and demonstrate how the framework can be
enhanced through the use of suitable reparametrisations. Our results support
the idea that data space inversion is a simple, robust and efficient technique
for making predictions with quantified uncertainty using geothermal reservoir
models, providing a useful alternative to more conventional approaches.
