Title : Enhancing oil recovery through surfactant-driven wettability alteration in gargzdai reservoir
Abstract:
This study explores enhanced oil recovery (EOR) methods aimed at addressing production decline and improving injection efficiency in Lithuania's Gargzdai hydrocarbon reservoirs. These reservoirs, characterized by an oil-wet nature, suffer from limited water-flooding efficiency, which hinders oil recovery. The analysis is based on field data from a depleted Gargzdai reservoir located in the Baltic Basin, at a depth of approximately 2 km. This field has been in production since the early 1990s and under water-flooding for pressure maintenance since the 2000s. However, production has steadily declined over time. Wettability assessments, including the water droplet method and calcite plate tests, confirmed the oil-wet nature of the reservoir rocks, with the Amott wettability index (IAH) for rock plugs showing values of −0.723 and −0.741, indicative of a strong oil-wet condition. A combination of analytical screening, using Society of Petroleum Engineers (SPE) guidelines, and machine-learning approaches was employed to evaluate the most viable EOR methods. Based on these analyses, techniques such as Low Salinity Water (LSW) flooding, Surfactant-Polymer (SP) flooding, and Alkali-Surfactant-Polymer (ASP) flooding were identified as potential candidates. Several surfactant formulations were tested to evaluate their impact on fluid injectivity, with a focus on understanding the mechanisms of wettability alteration. Surfactant injection was found to alter the wettability of the reservoir rock from oil-wet to water-wet, significantly improving water-flooding efficiency and oil mobilization. Experimental results showed that, on average, for every 6 barrels of water injected, 1 additional barrel of oil is produced. A 10% improvement in injectivity through surfactant injection could potentially replace the need for additional water injection wells, leading to significant operational cost savings. Given the promising results from screening and experimental testing, it is recommended to proceed with core flooding experiments to further investigate the potential of surfactant-based EOR methods. Field trials, including single-well tracer tests (SWCTT) and log-inject-log techniques, will provide further insights into the efficiency of surfactant injection for improving injectivity, recovery, and cost-effectiveness in this heterogeneous, oil-wet reservoir. This research offers a practical solution to improve recovery rates and reduce operational costs in challenging oil-wet reservoirs. Additionally, the use of machine learning for EOR selection provides a robust, data- driven framework for optimizing oil recovery strategies, benefiting both field operators and researchers seeking to apply advanced methods to similar reservoirs.
