Sensitivity Analysis of Oil Production Models to Reservoir Rock and Fluid Properties


  • Bikash Sharma
  • Ali Moradi
  • Britt Margrethe Emilie Moldestad



sensitivity analysis, OLGA, ROCX, Norne field, oil production


Improving the efficiency and optimization of oil recovery with a special focus on digitalization is on the spotlight. Achieving an optimized and successful automatic production highly depends on the ability to monitor and control the well performances. This requires a suitable dynamic model of the oil field and production equipment over the production lifetime. One of the main barriers to developing such dynamic models is that generally, it is difficult to observe and understand the dynamic of fluid in a porous medium, describe the physical processes, and measure all the parameters that influence the multiphase flow behavior inside a reservoir. Predicting the reservoir production over time and respond to different drive and displacement mechanisms has a large degree of uncertainty attached. To develop long-term oil production models under uncertainty, it is crucial to have a clear understanding of the sensitivity of such models to the input parameters. This helps to identify the most impactful parameters on the accuracy of the models and allows to limit the time of focusing on less important data. The main goal is to analyze sensitivity for the uncertainty effects in each reservoir parameter on the outputs of oil production models. Two simulation models for oil production have been developed by using the OLGA-ROCX simulator. By perturbation of reservoir parameters, the sensitivity of these model outputs has been measured and analyzed. According to the simulation results after 200 days, the most affecting parameter for accumulated oil production was the oil density with sensitivity coefficients of -1.667 and 1.610 and relative permeability (-0.844 and 0.969). Therefore, decreasing the degree of uncertainty in those input parameters can highly increase the accuracy of the outputs of oil production models.


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