The Smart Fields collaboration between Shell and Schlumberger is developing an Uncertainty Management Framework aimed at reducing both Hydrocarbon Development Planning cycle time and improving the quality of decisions made under uncertainty. The objective is to better manage the impact of the full range of project uncertainties on key development decisions.

Schlumberger, working closely with Shell, have developed an Uncertainty Management Tool (UMT) for the purpose of capturing uncertainties, their ranges, additional qualitative and contextual information, and associated risks and action plans. Uncertainty assessments are typically made during the course of discipline and application specific technical work. Whilst not performing uncertainty analysis itself, the UMT aims to maintain an internally consitent view of uncertainties throughout the multi-disciplinary reservoir modeling workflow, which is input to the field development decision making process. Teams would continue to use existing techniques and applications for uncertainty analyis (monte carlo simulation, experimental design etc) but use the UMT for tracking the reduction of uncertainty and risk over time. The UMT tool provides a central repository where this information can be collected, monitored and managed throughout the life of the asset, and archived for future analysis.

Another challenge faced by the asset team is in managing the large numbers of realizations that may be required in order to address the full range of uncertainty. This can be a laborious and time-consuming process, if it is attempted at all. The prototype has the ability to interactively create and edit a realization tree using captured uncertainty information.

The realization tree serves as a guide to the team as they construct their reservoir models. It also allows the team to conveniently track their progress, capture decisions made along the way, and work toward a final concept selection.

In addition to the prototype tool, a model-building workflow using Petrel was developed to assist in ranking and screening static model realizations prior to full dynamic simulation.

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