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Addressing uncertainty during early stage frontier exploration: bringing regional context to basin modellingNormal access

Authors: Natasha Dowey and Christine Yallup
Journal name: First Break
Issue: Vol 37, No 5, May 2019 pp. 75 - 79
Language: English
Info: Article, PDF ( 573.83Kb )
Price: € 30

Summary:
During the early stages of exploration, uncertainties regarding the nature and presence of a petroleum system are significant. The potential risk on charge conditions is typically a key factor in frontier exploration; charge has been a common cause of wildcat well failure in recent years (e.g., Rabat Deep, Morocco and Maria-1, Black Sea). The ability to assess charge risk when even basic geological conditions are poorly understood is therefore a crucial first step in increasing exploration success. Basin modelling is an essential tool for evaluating and quantifying charge timing, phase and volume, and allows the impact of uncertainty to be investigated through sensitivity analysis. However, during the early stages of frontier exploration, there is typically little data available to unequivocally constrain stratigraphic predictions and thermal conditions in subsurface models. This article describes how regional geological and tectonic context provides a fundamental and primary framework for developing predictions and understanding critical factors on charge risk in frontier basin models. The work addresses three key challenges: First, how can we predict input parameters to build basin models in areas of little data? Second, how can we assess uncertainty on inputs and identify critical factors? Third, how can we test critical factors to better understand exploration charge risk and uncertainty? The workflow presented here demonstrates the use of aregional geological and tectonic framework to identify appropriate analogues, constrain lithostratigraphy and thermal boundary conditions, assess input uncertainty, and finally to test plausible scenarios to evaluate charge risk (Figure 1). The work discusses best practices using case studies and illustrates that, even where uncertainty is high, basin modelling can be used to quantify a plausible range of outcomes to help make meaningful predictions.


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