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Characterization of CO2 storage and enhanced oil recovery in residual oil zones

Chen, Bailian, Pawar, Rajesh J.
Energy 2019 v.183 pp. 291-304
Monte Carlo method, Permian period, aquifers, artificial intelligence, basins, carbon dioxide, models, oil fields, oils
Residual oil zones (ROZs) are reservoirs in which oil is swept over geologic time period and exists at residual saturation. The oil in such reservoirs cannot be commercially exploited using conventional oil recovery methods as the oil exists at residual oil saturation. Instead, enhanced oil recovery methods such as CO2 injection are required. Recently, ROZs have been increasingly studied as potential CO2 storage targets. In spite of increased interest in ROZs, there are significant gaps in the knowledge of parameters and processes that impact CO2 storage and oil recovery. In this work, we identify key geologic and operational characteristics that affect CO2 storage capacity and oil recovery potential by performing Monte Carlo simulations and sensitivity analysis. In addition to CO2 storage capacity, we also characterize the long-term CO2 fate in ROZs. The distinction of CO2 storage in ROZs from conventional oil reservoirs and saline aquifers are also characterized. Furthermore, predictive models based on machine learning techniques are developed to estimate CO2 storage and oil production potentials for ROZs. The applicability of the predictive models is demonstrated for five ROZs in the Permian Basin.