Redevelopment of a mature field enables reassessment of the current field understanding to maximise its economic return. However, the redevelopment process is associated with several challenges: 1) analysis of large data sets is a time-consuming process, 2) extrapolation of the existing data on new areas is associated with significant uncertainties, 3) screening multiple potential scenarios can be tedious. Traditional workflows have not combatted these challenges in an efficient manner.

In this work, we suggest an integrated approach to combine static and dynamic uncertainties to streamline evaluating of multiple possible scenarios is adopted, while quantifying the associated uncertainties to improve reservoir history matching and forecasting. The creation of a fully integrated automated workflow which includes geological and fluid models is used to perform Assisted History Matching (AHM) that allows the screening of different parameter combinations whilst also calibrating to the historical data. An ensemble of history matched models is then selected using dimensionality reduction and clustering techniques. The selected ensemble is used for reservoir predictions and represents a spread of possible solutions accounting for uncertainty. Finally, well location optimisation under uncertainty is performed to find the optimal well location for multiple equiprobable scenarios simultaneously.

The suggested workflow was applied to the Northern Area Claymore (NAC) field. NAC is a structurally complex, Lower Cretaceous stacked turbidite, composed of three reservoirs, which have produced ~170 MMbbls of oil since 1978 from an estimated STOIIP of ~500 MMstb. The integrated workflow helps to streamline the redevelopment project by allowing geoscientists and engineers to work together, account for multiple scenarios and quantify the associated uncertainties. Working with static and dynamic variables simultaneously helps to get a better insight into how different properties and property combinations can help to achieve a history match. Using powerful hardware, cloud-computing and fully parallel software allow to evaluate a range of possible solutions and work with an ensemble of equally probable matched models. As an ultimate outcome of the redevelopment project, several prediction profiles have been produced in a time-efficient manner, aiming to improve field recovery and accounting for the associated uncertainty.

The current project shows the value of the integrated approach applied to a real case to overcome the shortcomings of the traditional approach. The collaboration of experts with different backgrounds in a common project permits the assessment of multiple hypotheses in an efficient manner and helps to get a deeper understanding of the reservoir. Finally, the project provides evidence that working with an ensemble of models allows to evaluate a range of possible solutions and account for potential risks, providing more robust predictions for future field redevelopment.

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