Recent Advances in Enhanced Oil Recovery with Low-Salinity Waterflooding and Its Hybrid Methods in Carbonate Reservoirs

Abstract

Low-salinity waterflooding (LSWF) has emerged as a promising enhanced oil recovery (EOR) technique due to its cost-effectiveness and suitability for complex carbonate reservoirs. This critical and comprehensive review focuses on the latest advancements in LSWF within carbonate reservoirs, which pose unique challenges, such as heterogeneity, mixed-to-oil wettability, high-temperature (over 90 °C), and high-salinity (up to 200 000 ppm) conditions, particularly prevalent in the Middle East region. We provide an in-depth analysis of the physicochemical mechanisms underlying LSWF to advance the development of more effective EOR strategies. Specifically, the review thoroughly examines the roles of specific ions, including sulfate, calcium, and magnesium, in altering wettability and enhancing oil recovery. Sulfate ion concentrations ranging from 2000 to 10 000 ppm have been shown to often increase oil recovery in specific cases by up to 15%. Maintaining a SO42–/Ca2+ ratio greater than 2 has been recommended to enhance wettability alteration and prevent scale precipitation. Additionally, the integration of surfactants and polymers in LSWF is discussed, highlighting potential synergistic effects that can boost recovery rates by an additional 5–10%. Recent studies employing techniques such as zeta potential, contact angle, capillary rise, X-ray computed topography (CT), and atomic force microscopy (AFM) are reviewed to elucidate the mechanisms behind wettability alteration. Furthermore, research on shear viscoelasticity related to oil droplet detachment and mobilization is examined to understand its impact on oil recovery processes. Future directions include optimizing the salinity and ionic composition of injection water and developing novel surfactants and polymer formulations to decrease interfacial tension and improve oil recovery, mainly for challenging high-temperature and high-salinity conditions, while leveraging machine learning and artificial intelligence algorithms to utilize predictive modeling and perform informed decisions

Open Access funding provided thanks to the CRUE-CSIC agreement with American Chemical Society (ACS)