Performance forecasting for polymer flooding in heavy oil reservoirs

As a supply for future fuel and energy demand, 95% of the bitumen deposits in North America are expected to become a major source. The Steam Assisted Gravity Drainage (SAGD) provides more efficient recovery of unconventional oil resources, such as heavy oil and bitumen, as compared to the other thermal recovery methods. The drawback associated with SAGD or other thermal methods is that they are economically non-profitable when applied to the deep and thin reservoirs. Environmental concerns related to land, water, and air also hinder the application of the aforementioned methods. These issues have provoked reservoir engineers to employ a remarkable alternate such as polymer flooding recovery technique in heavy oil reservoirs. Quick and practical decision-making process in presence of uncertainty-based reservoir development scenarios is a notable stimuli for reservoir management teams to find substitute modeling techniques for future performance forecasting of heavy oil reservoirs. Cognitive data-driven analytics, including artificial and computational intelligence techniques, statistical analyses, and data-mining practices, offers an attractive alternate especially in presence of high-dimensional data space and predictive modeling of an extremely nonlinear system. This study utilizes an extensive data set from the half-century review of laboratory to field scales polymer flooding in heavy oil reservoirs provided by Saboorian-Jooybari et al. (2015) and (2016). The exploratory data analysis is implemented to construct a comprehensive training data set from polymer flooding experimental and field data, which involves various attributes describing characteristics associated with reservoir heterogeneities and pertinent operating parameters. Demonstrated results imply that this advanced data-driven modeling technique has a great potential to be integrated into all reservoir development tools for future performance predictions of the underlying processes.