Offshore Gas Condensate Field History-Match and Predictions: Ensuring Probabilistic Forecasts Are Built With Diversity in Mind

Abstract

Abstract Ideally, probabilistic forecasts improve the decision quality, capture upsides and mitigate downsides. Unfortunately, one of the risks associated with probabilistic forecasts is the possible creation of a large number of models with similar properties or responses. Methods to identify representative models from an ensemble of models are thus necessary. In this paper, we extend the use of distance-based methods, developed at Stanford Center for Reservoir Forecasting, and Kernel clustering techniques to the selection of models for economic decision-making. Using mathematical tools, the large set of models in a high-dimensional space is mapped to a low-dimensional space and a distance function based on several prediction-related quantities is used to quantify the similarities and dissimilarities among an ensemble of history-matched models. We then construct a small portfolio of models with a diverse range of prediction performances for less cost-intensive probabilistic forecasts. The approach is applied to an offshore gas condensate field in the Asia-Pacific region. Two possible development plans were competing: continue to produce from existing geological structures, or extend the development to accumulations connected through a saddle in the carbonate reservoir. The decision was impacted by the uncertainty on remaining volumes in place and connectivity between structures. Based on the uncertainty management plans, 925 history-matched models are generated with genetic algorithms. Within the context of distance-based modeling, an application-tailored distance function between models is designed, balancing static (scenario independent) and dynamic (scenario dependent) properties of the models. Then, eight diverse and representative models are selected among the 925 models. We show that the eight selected models, while offering an equally good history-match, cover well the uncertainty ranges of the static and dynamic properties considered. The selected models can be used to evaluate different possible development scenarios for final business decision making.