The aim of this study is to demonstrate the value of an integrated ensemble-based modeling approach for multiple reservoirs of varying complexity. Three different carbonate reservoirs are selected with varying challenges to showcase the flexibility of the approach to subsurface teams. Modeling uncertainties are included in both static and dynamic domains and valuable insights are attained in a short reservoir modeling cycle time.

Integrated workflows are established with guidance from multi-disciplinary teams to incorporate recommended static and dynamic modeling processes in parallel to overcome the modeling challenges of the individual reservoirs. Challenges such as zonal communication, presence of baffles, high permeability streaks, communication from neighboring fields, water saturation modeling uncertainties, relative permeability with hysteresis, fluid contact depth shift etc. are considered when accounting for uncertainties. All the uncertainties in sedimentology, structure and dynamic reservoir parameters are set through common dialogue and collaboration between subsurface teams to ensure that modeling best practices are adhered to. Adaptive pluri-Gaussian simulation is used for facies modeling and uncertainties are propagated in the dynamic response of the geologically plausible ensembles. These equiprobable models are then history-matched simultaneously using an ensemble-based conditioning tool to match the available observed field production data within a specified tolerance; with each reservoir ranging in number of wells, number of grid cells and production history.

This approach results in a significantly reduced modeling cycle time compared to the traditional approach, regardless of the inherent complexity of the reservoir, while giving better history-matched models that are honoring the geology and correlations in input data. These models are created with only enough detail level as per the modeling objectives, leaving more time to extract insights from the ensemble of models. Uncertainties in data, from various domains, are not isolated there, but rather propagated throughout, as these might have an important role in another domain, or in the total response uncertainty. Similarly, the approach encourages a collaborative effort in reservoir modeling and fosters trust between geo-scientists and engineers, ascertaining that models remain consistent across all subsurface domains. It allows for the flexibility to incorporate modeling practices fit for individual reservoirs. Moreover, analysis of the history-matched ensemble shows added insights to the reservoirs such as the location and possible extent of features like high permeability streaks and baffles that are not explicitly modeled in the process initially. Forecast strategies further run on these ensembles of equiprobable models, capture realistic uncertainties in dynamic responses which can help make informed reservoir management decisions.

The integrated ensemble-based modeling approach is successfully applied on three different reservoir cases, with different levels of complexity. The fast-tracked process from model building to decision making enabled rapid insights for all domains involved.

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