Bayesian Identifying of Influential Factors Affecting Steamflooding Processes Performance in Heterogeneous Bitumen-Oil Sandstone Reservoir

Abstract

AbstractThe Steamflooding was considered in this research to extract discontinuous bitumen layers and improve oil recovery in the Upper Sandstone Member of South Rumaila Oil Field, located in Southern Iraq. The reservoir heterogeneity and bitumen layers, which are located at the water-oil contacts, impede water approaching into the reservoir from an infinite active aquifer; therefore, Steamflooding came over this situation by efficiently extracting the bitumen. This research focused on using a Bayesian framework of Experimental design with thermodynamic reservoir flow modeling for the purpose of identifying the most influential factors that impact the reservoir performance through Steamflooding processes. Thermodynamic reservoir simulation was used to compute flow response, cumulative oil production, to evaluate the various what-if scenarios that are created by experimental design. Latin Hypercube Sampling was adopted for its discrepancy and uniformity to generate many experimental runs that capture the variety of Steamflooding process evaluation.The factors that were considered to test reservoir response are steam injection pressure, steam quality, steam injection rate, steam temperature, maximum oil production rate, minimum bottom hole pressure, maximum water cut, skin factor. Additionally, the reservoir properties of horizontal permeability, porosity, anisotropy ratio, aquifer radius, and rock compressibility are included. In order to find out the most influential factors on the Steamflooding performance, Bayesian Model Averaging (BMA) was adopted as model selection in the experimental design in comparison with the conventional stepwise linear regression. Model selection process in BMA considers the model’s posterior probability and Bayesian Information Criterion (BIC). BMA produces a posterior distribution of the outcome factor that represents the weighted average of the posterior distributions of that factor for each likely model. Among multi models, few ones were selected that their posterior probabilities sum equals one. The best model selection has the maximum posterior probability and minimum Bayesian Information Criterion; nevertheless, the reduced model is determined when the non-zero probability of coefficient for a given predictor equals more than 50% for all the sampled models.All these variables are shown in the computed Occam’s window. The results showed that BMA algorithm has efficiently eliminate the non-influential factors on the Steamflooding performance. The most influential factors on the Steamflooding performance were identified by LHS are maximum oil production rate, maximum water cut, in addition to permeability, porosity, anisotropy ratio and rock compressibility. However, the conventional stepwise regression has shown that porosity is the most influential factor with limited effect for all other factors that is physically meaningless.