Mature fields already account for about 70% of the hydrocarbon liquids produced globally. Since the average recovery factor for oil fields is 30 to 35%, there is substantial quantities of remaining oil at stake. Conventional simulation-based development planning approaches are well established, but their implementation on large, complex mature oil fields remains challenging given their resource, time, and cost intensity. In addition, increased attention towards reduce carbon emissions makes the case for alternative, computationally-light techniques, as part of a global digitalisation drive, leveraging modern analytics and machine learning methods. This work describes a modern digital workflow to identify and quantify by-passed oil targets. The workflow leverages an innovative hybrid physics-guided data-driven, which generates historical phase saturation maps, forecasts future fluid movements and locate infill opportunities. As deliverables, a fully probabilistic production forecast is obtained for each drilling location, as a function of the well type, its geometry, and position in the field. The new workflow can unlock remaining potential of mature fields in a shorter time-frame and generally very cost-effectively compared to the advanced dynamic reservoir modelling and history-match workflows. Over the last 5 years, this workflow has been applied to more than 30 mature oil fields in Europe, Africa, the Middle East, Asia, Australia, and New Zealand. Three case studies’ examples and application environments of applied digital workflow are described in this paper. This study demonstrates that it is now possible to deliver digitalized locating the remaining oil projects, capturing the full uncertainty ranges, including leveraging complex multi-vintage spatial 4D datasets, providing reliable non-simulation physics-compliant data-driven production forecasts within weeks.

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