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

• Published in: Energy (Oxford) 2019-09, Vol.183 (C), p.291-304
• Main Authors: Chen, Bailian, Pawar, Rajesh J.
• Format: Article
• Language: English
• Subjects: Aquifers; Basins; Carbon dioxide; Carbon sequestration; CO2 storage; Computer simulation; Earth Sciences; Enhanced oil recovery; ENVIRONMENTAL SCIENCES; Geology; Learning algorithms; Machine learning; Oil and gas production; Oil recovery; Oil reservoirs; Permian; Prediction models; Predictive empirical models; Process parameters; Reservoirs; Residual oil zones; Saturation; Sensitivity analysis; Storage capacity
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.06.142

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. •Characteristics that affect CO2 storage and oil recovery in ROZs are identified.•Characterize distinction of storage in ROZ from conventional oil reservoir/aquifer.•Predictive models are developed to estimate CO2 storage and oil recovery potentials.•The applicability of the predictive models is demonstrated for five ROZ fields.