Abstract

Miscible gas injection in the Kashagan platform started in 2017 and has been ongoing for three years. Gas injection in the platform carries both EOR and a facilities de-bottlenecking component. Initial flood assessment studies done using classical FD simulation have not provided the full picture of the reservoir dynamics to understand the performance of miscible gas injection. Areal reservoir depletion, pressure support and gas breakthrough events were challenging to quantify and characterize in terms of standalone full field FD simulation. Reservoir management strategy for operating the gas injection area was to maximize production from nearby producers and inject the full compressor potential. However, due to the fact that wells have different potentials driven by reservoir heterogeneity, areal distribution of cumulative reservoir fluid withdrawals and injection ended up being significantly different, which lead to flood pattern imbalances.

The operator has implemented a modeling workflow that combines post-processing of history matched numerical simulation model with streamline tracing and integration of time-lapse well allocation factors (WAFs) to quantify and analyze flood performance. This paper presents how to use streamline modeling to estimate well-pair dynamic control volumes and a numerical integration workflow of the dynamic WAFs to evaluate pattern performance to guide flood balancing strategy.

Streamline-based modeling workflow provides additional value by 3D visualization of the dynamic flood patterns and quantification of the individual pattern metrics. Numeric integration of injector-producer allocation factors (WAFs) and control volumes (CVs) allowed the construction of well pair conformance plots. Ranking of the patterns and I-P pairs filtered the outlier patterns with over injected and produced volumes and helped to focus on specific areas in need for pattern balancing.

A list of producers with the highest pore volumes of gas injected were identified as at-risk wells for gas breakthrough and GOR elevation, which was confirmed by well test results. The first three wells with a rise in GOR and breakthrough sequence perfectly matched with the prediction of pattern performance. Those were identified as at-risk producers based on streamline modeling outputs. Verification of the analysis by field surveillance data gave confidence in reliability of the streamline-based flood evaluation approach. The outcomes of this study helped to understand miscible gas front movement and depletion dynamics in the gas injection area. This case study demonstrates how complementing finite-difference modeling with streamline analysis is necessary for achieving a complete assessment of the miscible gas flood performance.

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