A New Screening Model for Gas and Water Based EOR Processes

Abstract

Abstract Screening for Enhanced Oil Recovery (EOR) processes is a critical step in evaluating future development strategies for depleted reservoirs under primary and secondary recovery. However, selecting the optimum EOR process for a given reservoir is challenging because it requires evaluating and comparing performance for various EOR processes, which is complex and time consuming. This paper presents a new EOR screening model that can predict the performance of various gas- and water-based EOR processes based on simple reservoir properties. The model estimates the oil recovery from miscible and immiscible gas/solvent injection (CO2, N2, and hydrocarbons), low salinity water flood, polymer, surfactant-polymer, alkaline-polymer and alkaline-surfactant-polymer floods. The screening model is based on a set of correlations that were developed using the response surface methodology, which correlates the oil recovery at dimensionless times to the important reservoir and fluid properties and EOR process variables identified for each process. The results of the model have been validated against a number of field test and numerical simulation results. The screening model provides the capability to screen a large set of reservoirs for a wide spectrum of EOR processes, to identify the good EOR targets and the optimum EOR process for the target reservoirs. In addition, this model easily performs sensitivity analysis without the need for numerical simulations, allowing teams to account for uncertainty in reservoir properties and optimization of flood design. Finally, the methodology can be applied for developing screening models for other oil recovery mechanisms such as thermal (steam injection, SAGD), microbial EOR and other methods.