Investigating CO2-enhanced oil recovery potential for a mature oil field: a case study based on Ankleshwar oil field, Cambay Basin, India

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作者
Shib Sankar Ganguli
Nimisha Vedanti
Idar Akervoll
Per E. Bergmo
Ravi P. Srivastava
V. P. Dimri
机构
[1] Indian Institute of Technology Kanpur,Department of Earth Sciences
[2] CSIR-National Geophysical Research Institute (NGRI),undefined
[3] SINTEF Petroleum Research,undefined
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Cambay basin; Black oil simulation; Todd and Longstaff parameters; CO; -enhanced oil recovery;
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摘要
CO2-enhanced oil recovery (EOR) is an upcoming technology in India. At present, no Indian field is under CO2-EOR and implementation of this technique to a mature oil field needs a rigorous study. In the present work, we made an attempt to investigate the CO2-EOR potential of a mature oil field, situated in Cambay Basin, India. The field was put on production in 1961, and it has produced approximately 65.36 MMt oil during massive water flooding, leading to residual oil reserves of 6.49 MMt. The operator of the field is interested in incremental oil recovery from this field by injecting CO2. This requires estimation of incremental oil recovery potential of the field by carrying out systematic study. We, therefore, developed a conceptual model inspired by Ankleshwar oil field of Cambay Basin using available information provided by the field operator and carried out systematic studies to establish an optimized strategy for CO2 injection. To achieve this goal, we investigated the effect of various operational parameters on oil recovery efficiency of our conceptual model and selected optimum parameters for reservoir simulations. Simulation results clearly indicate that the field can be a good candidate for CO2-EOR, and an additional oil recovery of 10.4% of hydrocarbon pore volume is feasible. Major outcome of the study is an optimized black-oil simulation model, which is in good agreement with the fine grid compositional model of high accuracy. The proposed black-oil model can easily be implemented and updated compared with compute intensive finer compositional simulation model.
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