A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States

The recovery factor (RF) is one of the most fundamental parameters that define engineering and economical success of any operational phase in oil and gas production. Despite its importance, it is not easy to predict recovery factors as they are affected by many variables including the type of the recovery process, reservoir type, fluid properties, reservoir heterogeneity, depth, and thickness. The usual and most comprehensive method of estimating recovery factors is laboratory experiments or numerical reservoir simulation, or combination of both, each of which has their own limitations due to data requirements, boundary conditions and scale effects. In this work, a fuzzy inference system approach has been adopted to predict miscible CO2-EOR recovery factors of the major field applications in the United States using a more practical, multi-variable, inexpensive, quicker, and yet accurate alternative to conventional approaches. The fuzzy system was built using Mamdani-type fuzzy logic inference engine. Reservoir data compiled from different sources were used as model input parameters (9 of them) and associated recovery factors obtained from literature survey were used as control variable, i.e. output. Due to the limited number of field cases that could be used for this purpose, 24 sets of applications were included in the study. The model showed reasonable predictive capability for the field observations of recovery factor despite the complexity and dependence of this parameter on different inputs. In addition, the multidimensional fuzzy solution was used to demonstrate response of miscible CO2-EOR recovery factor to different inputs. The fuzzy system introduced in the paper can be used to predict RFs and also as a guidance tool for making decisions based on the sensitivity to different inputs.