SCENARIO DISCOVERY WORKFLOW FOR ROBUST PETROLEUM RESERVOIR DEVELOPMENT UNDER UNCERTAINTY

Subsurface uncertainty creates large economic risks for the development of hydrocarbon reservoirs, driving the need for a decision-making procedure that is robust with respect to this uncertainty. In current practice, decisions are often made based on a single geologic scenario, and uncertainty is modeled in terms of parametric variations around best-estimate values within that scenario. In such a procedure, the impact of other possible geological scenarios upon the performance of a development plan is not explicitly evaluated. To improve decision making, reservoir models with different geological concepts (e.g., different environments of deposition) should be built to capture the full range of uncertainty. However, it is difficult to analyze the results from many models and provide summary information pertinent to business needs. In this paper, a scenario discovery-based outcome analysis workflow is described to systematically explore the result of many (50 to 10,000 or more) reservoir simulation runs. The workflow includes defining performance metrics to reflect business needs, exploring and defining outcome scenarios, searching for relationships between geological parameters and outcomes, and selecting and investigating individual representative cases. Supported by various data mining and data visualization techniques, this workflow may help decision-makers to better understand the potential business impacts of the uncertainty and develop insights concerning geological parameters that control these impacts. We present two examples of this workflow based on a subset of the SAIGUP IManzocchi et al., Petrol. Geosci., 141(1):3-15, 2008] dataset containing 2268 reservoir simulation models, from a full factorial combination of four sedimentology parameters and three structural parameters. In the first example, we examine the shape of production profiles, demonstrating the identification of geological origins for different shapes of production curves using only a small fraction of the full factorial simulation set. In the second example, we analyze the factors influencing water breakthrough time. For both examples, we identify representative reservoir models to ground decision-making in concrete instances.