Compositional simulation model and history-matching analysis of surfactant-polymer-nanoparticle (SPN) nanoemulsion assisted enhanced oil recovery

Nilanjan Pal, Ajay Mandal

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: 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.
Original languageEnglish (US)
JournalJournal of the Taiwan Institute of Chemical Engineers
DOIs
StatePublished - Apr 28 2021

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Chemistry(all)

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