  Production forecasting is a key step to design the future development of a
reservoir. A classical way to generate such forecasts consists in simulating
future production for numerical models representative of the reservoir.
However, identifying such models can be very challenging as they need to be
constrained to all available data. In particular, they should reproduce past
production data, which requires to solve a complex non-linear inverse problem.
In this paper, we thus propose to investigate the potential of machine learning
algorithms to predict the future production of a reservoir based on past
production data without model calibration. We focus more specifically on robust
online aggregation, a deterministic approach that provides a robust framework
to make forecasts on a regular basis. This method does not rely on any specific
assumption or need for stochastic modeling. Forecasts are first simulated for a
set of base reservoir models representing the prior uncertainty, and then
combined to predict production at the next time step. The weight associated to
each forecast is related to its past performance. Three different algorithms
are considered for weight computations: the exponentially weighted average
algorithm, ridge regression and the Lasso regression. They are applied on a
synthetic reservoir case study, the Brugge case, for sequential predictions. To
estimate the potential of development scenarios, production forecasts are
needed on long periods of time without intermediary data acquisition. An
extension of the deterministic aggregation approach is thus proposed in this
paper to provide such multi-step-ahead forecasts.
