Lessons Learned from IOR Steamflooding in a Bitumen-Light Oil Heterogeneous Reservoir
W.J.M. Al-Mudhafar and S.M. Hosseini Nasab
Event name: IOR 2015 - 18th European Symposium on Improved Oil Recovery
Session: Poster Session
Publication date: 14 April 2015
Info: Extended abstract, PDF ( 2.75Mb )
Price: € 20
The Steamflooding was considered in this research to extract the discontinuous bitumen layers that are located at the oil-water contact for the heterogeneous light oil sandstone reservoir of South Rumaila Field. The reservoir heterogeneity and the bitumen layers impede water aquifer approaching into the reservoir; therefore, Steamflooding would be efficient to extract bitumen layers and improve oil recovery. This research focused on adopting three Design of Experiments (DoE) approaches with thermodynamic reservoir flow simulation to identify the most influential factors that impact the reservoir performance through Steamflooding process. Meanwhile, the thermodynamic simulation was used to evaluate the various what-if scenarios and compute cumulative oil production that was considered as a response in the experimental design procedure. In this paper, full factorial design (FFD) and orthogonal arrays design (OAD) were adopted along with Hammersley Sequence Sampling (HSS) for that purpose. HSS is a low discrepancy and uniform space filling decimal points sampling that provide multiple levels for each factor. The factors are steam injection pressure, steam quality, steam injection rate, steam temperature, and number of injectors. To validate the overall design and each factor, analysis of variance (ANOVA) test was used to assess the influential role for each factor. In comparison with no-injection base case, Steamflooding has proved its feasibility to extract bitumen and improve recovery factor that reached to 80.018% by the end of 12 years prediction period; nevertheless, oil recovery for the base case was only 68.231 %, which is equal to the value with Steamflooding only after 11 months when the Steam injection starts. The linear DoE model of HSS has shown its validity to handle wide variety experiments of the problem. The main influential factors that were identified by DoE models are steam quality, steam injection rate and some of the interaction terms that include other factors.