Huge volumes of subsurface data are generated from the discovery through to production phases of oil/gas fields. These data can include seismic acquisition, well log measurements and well test data. At production start-up, several wells may be brought online in rapid succession. This stream of incoming data imposes a challenge to reservoir modelling, i.e. how to incorporate the new information in an agile and efficient way so it can used to underpin future decisions? In field developments with large CAPEX and a complex subsurface, integrating new data in a timely and efficient manner can impact upcoming well location, well design, completion solutions and scheduling decisions. Fast Model Update (FMU™) is an internally-developed reservoir modeling automation process and it is applied in projects across Equinor’s global portfolio, enabling a fast incorporation of static and dynamic data, from reservoir modeling to simulation, and uncertainty management, leading to new insights into the reservoir development strategy. FMU facilitates multi-realisation modelling though the ability to integrate depth conversion, geological and dynamic parameters in the same workflow manager, spanning the full range of uncertainty. The result is tailored models on demand that allow robust field development decisions to be made in a more iterative and agile way. The main steps of the FMU workflow are summarized below: Provide interpretation of the main reservoir horizons and structural uncertainties. Provide interpretation of the different possible geological environments and distribution of properties based on the well data. Generation of an ensemble of geological models. Identification of the most important dynamic parameters as well as non-subsurface factors (schedule, operational variables) and associated uncertainties. Combination of geological models and dynamic parameters in different scenarios by Monte Carlo simulation. Selection of models that match the available historical dynamic data (well tests and production data) Generation of production profiles to support future decisions