Uncertainty Assessment of Reservoir Modeling for Oilfield in the South of Iraq - Rashid, Mustafa; Hamd-Allah, Sameera M. (Journal of Petroleum Research and Studies, 2023)
A reservoir is formed due to geologic deposition processes and is not created randomly. However, because of subsurface complexity and limited data, there are many uncertainties in reservoir characterization. Uncertainties can be reduced by gathering more data and/or employing improved technology and scientific methods. Under uncertainty and risk, uncertainty analysis should be performed for investigational analyses as well as decision-making. The main focus of uncertainty analysis in reservoir characterization and management should be to understand what needs to be known and what can be known. Therefore, there are several reservoir parameters’ uncertainties and their quantitative influence on cumulative oil production and water cut were studied. In this paper, sensitivity analysis and uncertainty quantification were conducted for several parameters to study their effect on cumulative oil production. The Monte Carlo method was used to carry out the uncertainty quantification. In this study, we examined two methods which are the Monte Carlo simulation using a Reservoir simulator (MCRS) and the Monte Carlo simulation using a Proxy (MCP) to overcome the issue of the high number of simulation runs requirement and to reduce time consumption. The results showed that The MCP method is a very useful and powerful tool to conduct the uncertainty quantification than the MCRS because the MCP performs the objective function with extremely less time-consuming and very accurate and identical results compared to the results of the MCRS method. The results of uncertainty quantification for production forecast show there is a low risk due to the small gap difference between the P50 and P90. While the sensitivity analysis results showed that the oil-water-contact depth is the dominant parameter that affects cumulative oil production while porosity is the less influential parameter.