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The Role of Streamline Models for Dynamic Data Assimilation in Petroleum Engineering and Hydrogeology

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Quantitative Information Fusion for Hydrological Sciences

Part of the book series: Studies in Computational Intelligence ((SCI,volume 79))

Geological models derived from static data alone often fail to reproduce the dynamic response from the aquifers, for example transient pressure or tracer response or from petroleum reservoirs, for example multiphase production history such as water cut or gas-oil ratio. Reconciling geologic models to the dynamic response of the reservoir is critical for subsurface characterization and building reliable reservoir performance models. Available information on subsurface heterogeneity can be broadly categorized into two major types: static and dynamic. Static data are time-invariant direct or indirect measurements of reservoir properties, such as cores, well logs, and 3-D seismic data. With recent advances in reservoir characterization, these data can now be integrated efficiently into coherent 3-D reservoir descriptions (Dubrule, 1998). Dynamic data are the time dependent measurements of flow responses such as pressure, flow rate, fractional flow and, with the use of 4-D seismic, time-lapse saturation and pressure. Integration of dynamic data generally leads to an inverse problem and requires solution of the flow equations several times using an iterative procedure (Hyndman et al., 1994; Kitanidis, 1995; Mclaughlin and Townley, 1996; Medina and Carrera, 1996; Anderman and Hill, 1999; Vasco and Datta-Gupta, 1999; Yeh and Liu, 2000; Oliver et al., 2001). The process is commonly referred to as “history matching” and is usually the most tedious and time-consuming aspect of subsurface flow and transport simulation study.

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Datta-Gupta, A., Devegowda, D., Oyerinde, D., Cheng, H. (2008). The Role of Streamline Models for Dynamic Data Assimilation in Petroleum Engineering and Hydrogeology. In: Cai, X., Yeh, T.C.J. (eds) Quantitative Information Fusion for Hydrological Sciences. Studies in Computational Intelligence, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75384-1_5

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  • DOI: https://doi.org/10.1007/978-3-540-75384-1_5

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