Assessing productivity impairment of surfactant-polymer EOR using laboratory and field data

Abstract

Surfactant-polymer (SP) flooding is an enhanced oil recovery (EOR) technique used to mobilize residual oil by lowering the oil-water interfacial tension, micellar solubilization, and lowering the displacing phase mobility to improve sweep efficiency. Surfactant-polymer flooding, also known as micellar flooding, has been studied both in the laboratory and field pilot tests for several decades. Surfactant polymer flooding is believed to be a major enhanced oil recovery technique based on laboratory experiments; however, its applications to field has not met the expectations of laboratory results. Successful field applications of SP flooding have been limited because of a number of obstacles, which include the large number of laboratory experiments required to design an appropriate SP system, high sensitivity to reservoir rock and fluid characteristics, complexity of reservoirs, infrastructure required for field implementation, and lack of reliable statistics on successes of field applications. In other words, there are many variables that affect reservoir performance. Traditionally, in SP flooding, a tapered polymer solution follows the injected surfactant slug. However, in recent years co-injection of surfactant and a relatively high concentration of polymer solution have been used in several field trials. Despite significant increase in oil recovery at early times in several surfactant-polymer floods, the increase in oil production period has had short duration followed by significant reduction in oil production. Thus, this research primarily relied on field test data to understand the problem, hoping that an improved solution strategy can be developed for new field applications. Second, current numerical models do not correctly predict the performance of surfactant-polymer floods and tend to over predict. Thus the second objective of this research was to develop a methodology to use combined field and laboratory data in commercial simulators to improve their predictive capability.