  Reservoir engineers use large-scale numerical models to predict the
production performance in oil and gas fields. However, these models are
constructed based on scarce and often inaccurate data, making their predictions
highly uncertain. On the other hand, measurements of pressure and flow rates
are constantly collected during the operation of the field. The assimilation of
these data into the reservoir models (history matching) helps to mitigate
uncertainty and improve their predictive capacity. History matching is a
nonlinear inverse problem, which is typically handled using optimization and
Monte Carlo methods. In practice, however, generating a set of properly
history-matched models that preserve the geological realism is very
challenging, especially in cases with complicated prior description, such as
models with fractures and complex facies distributions. Recently, a new
data-space inversion (DSI) approach was introduced in the literature as an
alternative to the model-space inversion used in history matching. The
essential idea is to update directly the predictions from a prior ensemble of
models to account for the observed production history without updating the
corresponding models. The present paper introduces a DSI implementation based
on the use of an iterative ensemble smoother and demonstrates with examples
that the new implementation is computationally faster and more robust than the
earlier method based on principal component analysis. The new DSI is also
applied to estimate the production forecast in a real field with long
production history and a large number of wells. For this field problem, the new
DSI obtained forecasts comparable with a more traditional ensemble-based
history matching.
