Chemical flooding is one of the enhanced oil recovery (EOR) methods to mobilize the residual oil after waterflooding by reduction of oil-water interfacial tension or wettability alteration and consequently increase of capillary number. The objective of this study is to assess the potential of alkaline surfactant (AS) flooding on a major oil reservoir at offshore Malaysia, besides to conduct the uncertainty assessment. High capital and operating expenditure are associated with chemical EOR (CEOR) projects. Therefore, key subsurface uncertainties quantified thoroughly for a robust assessment of influential parameters.
The considered field is planned to be a pioneer in offshore CEOR. After the completion of pilot and matching the results in the simulation model, the key step is to upscale the results to the full field level. A critical step of the chemical EOR is to find the relative contribution of the influential parameters on the objective function like oil recovery. To do so, a detailed modeling work was performed for sensitivity and uncertainty analysis of AS flooding. Some of the important parameters used in the model are interfacial tension, chemical adsorption, slug size, and reduction of residual oil saturation by chemical.
A single well pilot test project was successfully conducted in 2007. The pilot entailed the injection of Alkaline-Surfactant chemicals and chemical tracer test into a waterflooded reservoir and produced from the same well. The pilot test indicated significant reduction of residual oil saturation (Sorw) in the range of 50% to 80% of Sorw. After running the uncertainty cases for the targeted reservoir, the probability ranges of the objective function were established. Based on the results of this uncertainty analysis, a proxy model has been built and subsequently quality checked to ensure that it can reproduce the simulated data with high accuracy. The developed proxy model was used to capture all combination of parameter ranges and do better decision making on the project. The results showed that based on the corresponding ranges of parameters, the residual oil reduction and slug size exhibited the highest and lowest impact on oil recovery, respectively. Therefore, the uncertainty of the objective function can be reduced by mitigating the uncertainty of the most influential parameters. Moreover, this work presents a proper workflow of CEOR modeling in addition to detailed and systematic approach for uncertainty evaluation.