To improve the reliability of reservoir performance predictions, subsurface uncertainties must be accounted for in production forecasts. Probabilistic methods are commonly used to understand and quantify the impact of uncertainties on reservoir behavior. This paper presents a structured and practical probabilistic history-matching and production forecasting workflow that was successfully applied to 6 reservoirs in a West-Africa field with several years of production history and a challenging data monitoring environment. The workflow was found to be very efficient as the 6 reservoir models were constructed, history-matched and run in predictions in less than three months. A recent look-back on the probabilistic predictions with a year of new production data proved the robustness of the workflow. The procedure used in this paper starts with a thorough review of subsurface uncertainties. All available static and dynamic data is analyzed to define uncertainty parameters and corresponding ranges. Next, a first set of simulations is performed, with each uncertainty parameter varied independently in order to analyze its effect on history-matched quality and future reservoir performance. The parameters with little impact are screened out during this step. The key parameters retained are then used to define a new set of simulations through experimental design. The models are run and the results are used to generate response surfaces for each history-match parameter and reservoir performance metric. Using a Monte-Carlo sampling procedure, thousands of uncertainty parameter combinations are tested using the response surfaces and screened using tolerances on various history-match parameters. This approach avoids the cumbersome and subjective definition of an objective function and allows the selection of a large number of parameter combinations that yield a history-match. Several models were selected to represent the 10th, 50th and 90th percentile of original oil in place and reservoir ultimate oil recovery. These probabilistic models are then run into prediction under different development scenarios, allowing for optimization of well locations and field operational constraints.