CO2 flooding can enhance oil recovery by up to 8–16% of the original oil in place and might be suitable for about 80% of oil reservoirs worldwide. In addition to miscibility, displacement efficiency is another factor that needs to be optimized for achieving high oil recovery. Although many techniques have been made available for production optimization in the upstream oil and gas industry, it is still a challenging task to optimize reservoir performance in the presence of physical and/or financial uncertainties. In this paper, a new technique is developed to optimize the displacement efficiency in a CO2 flooding reservoir under uncertainty. More specifically, potential uncertainties influencing reservoir performance are analyzed and assessed by using the geostatistical technique. This enables us to integrate the available information within a unified and consistent framework and to generate multiple geological realizations accounting for uncertainty and spatial variability. Subsequently, the net present value (NPV) is selected as the objective function to be optimized by using the genetic algorithm, while well rates of the injectors and the flowing bottomhole pressures for the producers are chosen as the controlling variables. In addition, corresponding modifications have been made to accelerate the convergence speed of the genetic algorithm. A field case is used to demonstrate the successful application of the newly developed technique. It has also been found that the genetic algorithm combined with the geostatistical technique can be used to optimize the displacement efficiency under uncertainty in a CO2 flooding reservoir.
Enhanced oil recovery (EOR) plays an increasingly important role in the petroleum industry. Among the various EOR processes, CO2 flooding is considered as the most promising and practical process since it not only significantly increases oil recovery, but also considerably reduces greenhouse gas emissions by sequestrating CO2 into the depleted reservoirs. In practice, CO2 flooding performance can be greatly affected by the reservoir heterogeneity, which can severely reduce the displacement efficiency, result in early CO2 breakthrough at the producers, and thus, leave a large amount of bypassed oil in the reservoir. Thus, it is of fundamental and practical importance to optimize production performance of a CO2 flooding reservoir.
The main objective of production optimization in a CO2 flooding reservoir is to monitor and control the propagation of the flood front, delay CO2 breakthrough at the producers, and thus, increase the oil recovery and/or the net present value (NPV). Such optimization is made possible by adjusting a set of controlling variables (e.g., flow rates and/or flowing bottomhole pressures)1. After an oil reservoir is put into production, it will be converted from a static system into a dynamic system. Such a system can be treated as a black box, to which injection fluids including gas and water are considered as the inputs and from which the production fluids are treated as the outputs. Changing flow rate and/or bottomhole pressure of the well in turn changes the dynamic state of the system (i.e., reservoir pressure and fluid saturation distribution). These changes will subsequently affect the cumulative production and then the oil recovery and/or NPV.
