This work presents a new open access carbonate reservoir case study that uniquely considers the major uncertainties inherent to carbonate reservoirs using one of the most prolific aggradational parasequence carbonate formation set in the U.A.E; the Late Barremian Upper Kharaib Mb. as an analogue. The ensemble considers a range of interpretational scenarios and geomodelling techniques to capture the main components of its reservoir architectures, stratal geometries, facies, pore systems, diagenetic overprints and wettability variations across its platform-to-basin profile. Fully anonymized data from 43 wells across 22 fields in the Bab Basin, U.A.E from different geo-depositional positions and height above FWL’s (specified to capture multiple structural positions) within an area of 36,000 km2 was used. The data comprises of a full suite of open hole logs and core data which has been anonymized, rescaled, repositioned and structurally deformed; FWL’s were normalized and the entire model was placed in a unique coordinate system. Our petrophysical model captures the geological setting and reservoir heterogeneities of selected fields but now at a manageable scale. The novelty of this work has been to create semi-synthetic open access carbonate reservoir models which enable the geoscience and reservoir engineering community to analyse, study and test number of cases related to new numerical algorithms for reservoir characterisation, reservoir simulation, uncertainty quantification, robust optimization and machine learning. The value of this study is also to expose a model and a dataset to the reservoir simulation engineers so they can explore the impact of different fluid flow physics on sweep and recovery across multiple carbonate reservoir architectures with diverse lateral and vertical rock and fluid complexities – all of which can be history-matched against a ‘truth case’.@en

The average global recovery factor of a typical oil and gas field is approximately 40% at after secondary recovery processes such as gas lifting and water flooding. The low recovery factor is often a result of by passing a considerable amount of oil in the reservoir due to unknown reservoir heterogeneity and incomplete understanding of the geology. Enhanced oil recovery (EOR) methods will play a key role in increasing recovery factors from existing reservoirs. Heterogeneous reservoir sands often show permeability contrast between layers and therefore lead to early water breakthrough when water flooding. In such situations, polymer flooding can potentially be a suitable EOR technique that helps to lower water cut and increase recovery. The addition of polymers to the injected water lowers the mobility ratio, thereby reducing viscous fingering and delaying water breakthrough. This study investigates how a polymer flood design can be optimized while considering geological uncertainty in the reservoir models as well as modelling decisions. We applied an adjoint based technique to match data from a prolonged waterflood in the Watt Field, a synthetic but realistic clastic reservoir that is based on real data and captures a wide range of geological heterogeneities and uncertainties through a range of different model scenarios and model realizations. We apply Latin hypercube experimental designs with the Particle Swarm Optimization algorithm in CMOST. This was used to build a proxy model employing polynomial regression for the optimization of the engineering parameters to maximize NPV. The optimization were performed for both history-matched models (constrained optimization) and the original, non-history-matched models (unconstrained optimization). The aim of this work is to analyse how geological uncertainties inherent to a heterogeneous clastic reservoir as well as modelling decisions impact the design and performance a polymer flood. We further investigate how the different optimization methods impact the predicted reservoir performance and optimal design of the polymer flood. Our findings show that both, geological and engineering uncertainties, impact polymer flooding and that designing the right well controls is essential for successful polymer flooding. Shale cut-offs are identified as a key petrophysical uncertainty when optimizing a polymer flood in a heterogeneous clastic reservoir. Furthermore, forecasts using constrained optimization yielded a much narrower range of incremental oil recovery and NPV during polymer flooding and may underestimate both, risk and opportunities for polymer flooding because the history matching of the water flood emphasizes different geological features compared to the way geology interacts with a more viscous polymer solution.

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This study investigates how a polymer flood design can be optimized while considering geological uncertainty in the reservoir models. Polymer flooding can increase oil recovery, reduce water cut, and improve sweep efficiency by diverting flow to low permeable zones. We applied two different history matching approaches (manual and gradient-based) to match data from a prolonged waterflood in the Watt field, a synthetic but realistic clastic reservoir that is based on real data and captures a wide range of geological heterogeneities and uncertainties through a range of different model scenarios and model realizations. The subsequent polymer flood is then optimized using history-matched models (constrained optimization) and the original, non-history-matched models (unconstrained optimizations). The aim is to study how geological uncertainties innate in clastic reservoir affect polymer flooding, and how different history matching approaches impact the predicted reservoir performance and optimal polymer flood design. A key observation is that shale cut-offs were a major uncertainty when optimizing the polymer flood for this field. In addition, the constrained optimization gave a much narrower forecast for incremental oil recovery during polymer flooding, possibly underestimating both risk and economic opportunities.@en