Reservoir simulation is a widely accepted tool for assessing the impact of uncertainties on upstream investment decisions. Currently, the most widely used workflow addressing these uncertainties is a traditional two-step approach: 1) geoscientists performing static uncertainty analysis with earth modeling parameters and selecting a few representative geological models (for example, low-mid-high); 2) reservoir simulation engineers conducting dynamic uncertainty analysis with dynamic parameters combined with the pre-selected geological models and performing history matching, forecasting, or optimization. In this workflow, all the geological uncertainties are lumped into one parameter (the grid) for use in the second step. This severely reduces the flexibility for considering a wider range of alternative static realizations, and thus may bias the history match and forecasts. We implemented an integrated workflow, called "big-loop" that unifies the two-step approach into a single step. This allows for simultaneous and explicit analysis of both types of uncertainties and improvement in reservoir management decision quality. It also allows for direct modification of earth model parameters to achieve a history match with geological consistency. Although the concept is not new to the industry, it is rare to find references of field applications of the "big-loop" workflow. We present the applications of this workflow to both green and brown reservoirs to demonstrate its value in improving accuracy and efficiency. In a Gulf of Mexico green field, the workflow is applied for uncertainty analysis of static parameters (for example sand channel width and salt body extension) and dynamic parameters (for example rock-fluid properties) for probabilistic Original Oil in Place (OOIP) assessment and production forecast. The workflow facilitates the design of uncertainty resolution, upside capture and downside mitigation plans. In an onshore fractured reservoir, the workflow is applied for simultaneous history matching using static fracture parameters (fracture length and aperture) and dynamic parameters. The workflow improves the model accuracy and decision quality for the upcoming IOR/EOR development project. In an offshore gas field, the workflow is used to perform experimental design (ED) studies with static and dynamic uncertainties. This systematic & automatic workflow eliminates manual inputs and reduces the need for recycles. Finally, in another field, the workflow is used to perform probabilistic history matching using static and dynamic parameters. This workflow is capable of delivering a full-cycle solution for uncertainty assessment and probabilistic history matching with high efficiency and high quality results.

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