This work presents the application of a fit-for-purpose history match workflow to a giant and geologically complex carbonate reservoir with over 60 years of production/injection history and 600+ wells. The target was to deliver, within schedule and spec, a high-quality sizeable model (15+ million grid blocks) that honored the underlying geologic characteristics and reproduced the distinctive production mechanisms present across the different regions of the reservoir, while keeping parametrization of uncertainties at a manageable level.

Practical implementation routes were applied to efficiently translate key reservoir plumbing elements and other identified subsurface uncertainties into dynamic modeling components that could be investigated over large uncertainty ranges via Assisted History Matching (AHM) tools.

To manage the history match process of this vast and mature reservoir, a sophisticated and custom-tailored sector-centered modeling scheme was adopted based on a "Divide & Conquer" approach. This tactic divides the big history match problem into smaller more manageable pieces, allowing for simultaneous history match of different sectors by different engineers while having frequent reassembling of sectors into a full-field model to ensure alignment, preserve consistent reservoir behaviors, and update (flux) boundary conditions.

The iterative sector-based history match scheme applied to the giant field dynamic model made it possible to achieve a good history match within the given time and IT resources available to carry out the history match. The new dynamic model respects the conceptual understanding of the reservoir behavior and honors the available subsurface and production data of approximately 80% of the individual wells within the desired history match criteria.

The use of the sector modeling workflow approach in a large full field model, allowed for faster turnaround of results for history matching purposes. The applied workflow also demonstrated that achieving a good history match in the individual sectors also resulted in a good history match for the full field model, achieved in a faster way.

The final model respects the conceptual understanding of reservoir behavior as well as honors the available performance data at a scale which allows not only more reliable production forecasts but also model-based pattern-level waterflood optimization and its use for well location optimization (WLO) studies. The model supports development planning and reservoir management decisions (20+ new wells drilled annually), with waterflooding aiming to increase ultimate recovery by more than 20%.

The methodology allowed significant time-savings to deliver the dynamic model within a relatively short schedule (~9 months) and required quality specifications. The successful application of the custom-made history match workflow is currently being replicated in other reservoirs of similar scale and complexity in North Kuwait and could also be applied to other massive reservoirs around the world. This work also illustrates a good example of achieving excellent HM results while keeping the parametrization of uncertainties as practical as possible.

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