Chemical flooding is one of the challenging EOR methods to improve the oil recovery. The objective of this work is to examine a systematic approach for upscaling Alkaline Surfactant Polymer (ASP) coreflood data to field scale and design the single well chemical tracer (SWCT) test. Appropriate upscaling can help to determine the effect of crucial parameters on process mechanisms and oil recovery. Besides, uncertainty assessment should be conducted thoroughly to evaluate the impact of key parameters.
In this paper, a robust approach for modeling the ASP flood from core to reservoir scale including the data uncertainty would be presented. Experimental work was aimed to screen and select the suitable chemicals for implementation in the field. Coreflood tests include ASP flood with and without polymer chase with the objective to evaluate the effectiveness of chemical flood and sweep efficiency. Sensitivity analysis by response surface methodology (RSM) would help to find the crucial parameters during the history matching of coreflood tests and reservoir modeling for ASP implementation.
Coreflood modeling was performed to represent the flow behavior of lab tests and investigate the mechanisms through the experimental efforts. Assisted history matching of the coreflood test was carried out to incorporate waterflood and chemical flood processes. Some variables such as relative permeability characteristics, trapping number, adsorption, and residual resistance factor were included as matching parameters. The next step was to upscale the model from core scale to reservoir scale by an appropriate method. Velocity and pressure were preserved during the scaling procedure. The parameters obtained from scaling exercise were used for SWCT design and full field model. Thus, radial models were used to describe and improve the design of SWCT tests for candidate wells. The next step was to evaluate the ASP flood on reservoir model. Sensitivity analysis was conducted on key parameters e.g. adsorption, injector-producer spacing, residual oil reduction by chemical, and ASP slug size to identify the impact of these parameters on oil recovery. RSM was applied to develop a suitable proxy model based on the results of sensitivity study. The proxy model can be used to find the optimum well spacing and slug size for field implementation.
Appropriate technique of chemical flood modeling is presented in this work. Moreover, upscaling of lab data to reservoir scale for pilot design and evaluation of ASP flood on reservoir scale by considering how to address risks and uncertainties are other outcomes of this work.