Uncertainty Analysis In Simulation For Reservoir Management: Case Study From Niger Delta

Abstract

Uncertainty analysis of flow performance predictions involves identifying significant reservoir parameters impacting the flow response. Uncertainty analysis often requires conducting large number of reservoir simulation runs. In this work, experimental design theory (DoE), Response Surface Methodology (RSM) and numerical simulation were integrated to reduce the number of simulations and simplify the optimization process. Various investigators have applied DoE/RSM to approximate a complex process with regression polynomials within a certain well defined region. The application of these DoE/RSM methods is more often based on the experimenter’s discretions or company practices with no attention paid to the risks involved. Hence, in this work, it is necessary to examine the basis for the various DoE/RSM methods for the purpose of developing guidelines for the construction of valid response surfaces. First, three families of linear experimental design methodologies, the Placket-Burman, Fractional and Relative variation, are evaluated for uncertainty screening by computing sensitivity coefficients of all identified uncertainty relative to system parameters. This procedure has useful applications in simulation for the optimization of production and management of a petroleum asset. Second, probable reservoir proxies were developed based on linear sensitivity analysis. Threelevel experimental design algorithms, i.e., Box-Behnken, Central Composite, D-Optima and Full Factorial designs, and Adaptive neuro-Fuzzy Inference System were rigorously examined. The methods were analyzed for their capabilities to reproduce actual production forecasts. The best method was selected from the case study and recommendations were made on how to select best DoE methods for similar applications.