Rheological Screening of HPAM for High-Salinity EOR

Authors

DOI:

https://doi.org/10.29017/scog.v49i3.2125

Keywords:

HPAM, polymer flooding, rheology, high salinity, core flooding

Abstract

Hydrolyzed polyacrylamide (HPAM) polymer flooding is a practical chemical enhanced oil recovery method, but polymer performance can deteriorate under high salinity, elevated temperature, and high shear rate. This study presents an integrated laboratory screening workflow for five commercial HPAM polymers under reservoir-representative conditions. Polymer solutions were prepared at 500–5000 ppm and tested at shear rates of 1–1000 s⁻¹, salinities of 3,000–200,000 ppm, and temperatures up to 90 °C. Rheological screening was followed by adsorption and core flooding tests for the most suitable candidates, Polymers C and E. These two polymers were carried forward because they achieved the highest composite scores in the technical screening, combining strong viscosity retention with favorable injectivity and salinity/temperature tolerance. All polymers showed shear-thinning behavior, while salinity and temperature reduced apparent viscosity. Polymer A provided the highest bulk viscosity; however, Polymer C gave the best balance among viscosity retention, salinity tolerance, injectivity, adsorption behavior, and recovery performance. Core flooding confirmed that Polymer C at 3000 ppm produced the highest incremental recovery, approximately 10.0% in Core 1 and 8.8% in Core 2. The results show that HPAM selection should be based on integrated rheology, adsorption, and recovery testing rather than viscosity alone. The main contribution of this work to polymer technology is a transferable, six-stage integrated screening protocol that links rheological behavior, salinity and thermal tolerance, adsorption loss, and core-scale recovery response within a single laboratory workflow, providing a practical decision basis for selecting HPAM polymers for high-salinity EOR projects rather than relying on isolated viscosity or adsorption data.

Author Biographies

  • Adel M. Salem, Suez University

    Professor of Petroleum Engineering at the Department of Petroleum Engineering, Faculty of Petroleum and Mining Engineering, Suez University, Egypt. His teaching and research interests include reservoir engineering, production engineering, well performance, enhanced oil recovery, and the application of machine-learning techniques in petroleum engineering.

  • Ahmed Alsabagh, Egyptian Petroleum Research Institute (EPRI)

    Ahmed M. Al-Sabagh is a Professor in the Department of Petroleum Applications at the Egyptian Petroleum Research Institute, Cairo, Egypt. He holds a PhD in chemistry and has extensive research experience in applied petrochemicals, surfactants, polymers, nanotechnology, petroleum applications, enhanced oil recovery, adsorption, and the development of chemical solutions for the petroleum industry.

  • Omar Elfarouk, University of Antwerp

    Omar Elfarouk holds a PhD and is a researcher affiliated with the University of Antwerp, Belgium. He has experience in artificial intelligence and data-driven modelling, alongside research interests in industrial engineering, engineering management, operations research, optimization, and the application of analytical methods to engineering and energy-related problems.

  • Mahmoud Tantawy, Suez University

    Mahmoud A. Tantawy is a Professor of Petroleum Engineering at Suez University, Egypt. He has extensive academic and industrial experience in reservoir engineering, reservoir simulation, well-test analysis, production engineering, enhanced oil recovery, water shut-off, and permeability modification. His research also includes unconventional reservoir performance, production forecasting, and artificial-intelligence applications in reservoir characterization and petroleum engineering.

  • Muhammad Saafan, K. Zhubanov Aktobe Regional University

    Senior Lecturer in Petroleum Engineering at the Zhubanov University–Heriot-Watt University Partnership, Kazakhstan. His research interests include reservoir and production engineering, capillary pressure modelling, and artificial intelligence applications in the petroleum industry.

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Published

21-08-2026

Issue

Vol. 49 No. 3 (2026)

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Articles

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