Compositional simulation model and history-matching analysis of surfactant-polymer-nanoparticle (SPN) nanoemulsion assisted enhanced oil recovery
KAUST DepartmentPhysical Science and Engineering Division
Embargo End Date2023-04-28
Permanent link to this recordhttp://hdl.handle.net/10754/669150
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AbstractBackground: Simulation plays a pivotal role in the design of enhanced oil recovery (EOR) processes based on reservoir and in-situ fluid conditions. A robust compositional model, using a complicated multi-component nanoemulsion injection fluid, was developed to describe the performance of nanoemulsion flooding to predict their feasibility for pilot oilfield projects. Method: Gemini surfactant/polymer/nanoparticle stabilized Pickering nanoemulsions were prepared by high-energy method and characterized to assess core-flooding performance. During simulation, a Cartesian grid model with fixed bulk volume, injection flow rate, well completion parameters and rock-fluid properties was employed. Core-flooding experiments were performed in steps, involving ~2.16 pore volume (PV) brine injection, ~0.90 PV nanoemulsion injection and ~1.50 PV chase water injection. Significant findings: Oil saturation map and relative permeability data analyses showed that the wetting nature of sandstone core altered from intermediate-wet to strongly water-wet condition. Tertiary recoveries were obtained in the range of 21-27% of the original oil in place (OOIP) for different surfactant/polymer/nanoparticle (SPN) compositions of injected nanoemulsion fluids. Flooding simulation studies showed good history matching of production data within ± 6% between experimental and simulated results. In summary, the efficacy of SPN nanoemulsions as an EOR fluid was corroborated with the aid of numerical simulation analyses.
CitationPal, N., & Mandal, A. (2021). Compositional simulation model and history-matching analysis of surfactant-polymer-nanoparticle (SPN) nanoemulsion assisted enhanced oil recovery. Journal of the Taiwan Institute of Chemical Engineers. doi:10.1016/j.jtice.2021.04.022
SponsorsAuthors gratefully acknowledge the financial assistance provided by Council of Scientific & Industrial Research (Ref. No. 22(0821)/19/EMR-II), and Oil Industry Development Board, Govt. of India, New Delhi (Ref. No. 4/3/2020-OIDB). The authors are also thankful to Computer Modelling Group Ltd. for providing (educational) licensed version of CMG Software.