The concept of uncertainty, risk, and probabilistic assessment is increasingly employed as a standard in the E&P industry to assist in development and investment decisions. The Tor field in the Greater Ekofisk Area of the North Sea is a producing chalk field, which has a 35-year production history and aging facilities. This naturally fractured chalk reservoir has had limited water injection and experienced rapid decline. An integrated subsurface uncertainty study has been performed to support a potential redevelopment of the Tor field. This paper will demonstrate the integrated workflow for the uncertainty study and the methodologies used to overcome challenges in reservoir modeling and forecasting. The results of the sensitivity analysis and assisted history matching (AHM) process will be illustrated as well as how the results were applied in the evaluation of redevelopment options and in preparing future reservoir management plan. The main challenges in reservoir modeling, forecasting and overall evaluation of the Tor field are: 1) Uncertainties outside the well control area. This results in a significant structure uncertainty, hence an even more increased uncertainty in structural dependent properties. 2) Uncertainty and implementation of inter-dependent static properties and their spatial distribution. The deterministic base case model is only one of thousands of property realizations from the geostatistical modeling process. 3) Uncertainty and systematic implementation of effective permeability. Effective permeability in the chalk reservoir is a combination of enhanced matrix permeability and "highways". Predictability of potential "highways" not identified by existing wells is especially challenging. 4) Simulation time. These uncertainties will directly influence the determination of hydrocarbon in place, well placement, and waterflooding efficiency and add risk to the production forecast used to justify field redevelopment. The workflow was: 1) Identification and framing of uncertainty parameters. 2) Complete static and dynamic parameters analysis and integration. 3) Comprehensive sensitivity analysis and AHM. 4) Forecasting based on multiple calibrated models to reach the rigorous probabilistic production profiles. The approach used include: 1) Realization of structure uncertainty and associated properties by a robust approach, which is advantageous for the AHM process. 2) Employment of multiple property realizations. 3) Use of a 3D seismic attribute for capturing potential highways uncertainty and for systematic effective permeability implementation. 4) Addressing uncertainty in water flood sweep efficiency. From the integrated workflow and robust methodology, a suite of "good quality" AHM models with equal probability are obtained. AHM has narrowed down the uncertainty range and from post-AHM analysis the initial resource range and main influential parameters on development are determined. As one of the best practices, we recommend using across sampled representative models with well & operation uncertainties rather than a specific P10, P50 or P90 model to make final probabilistic forecasts.