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Advanced Reservoir Simulation Using Soft Computing

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Book cover Intelligent Problem Solving. Methodologies and Approaches (IEA/AIE 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1821))

Abstract

Reservoir simulation is a challenging problem for the oil and gas industry. A correctly calibrated reservoir simulator provides an effective tool for reservoir evaluation that can be used to obtain essential reservoir information. A long-standing problem in reservoir simulation is history matching, which is to find a suitable set of values for the simulator’s input parameters such that the simulator correctly predicts the fluid (oil, gas, water, etc.) outputs of the wells on the reservoir, over the time period of interest. Due to the sheer size of the problem, completely satisfactory results of history matching have been difficult and expensive to achieve. This paper presents a novel technique of using fuzzy control to solve history matching. Intended for implementation on a cluster of PCs, our technique aims not only to solve history matching faster, but also solves it at a lower cost. Preliminary results and ongoing work are described.

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References (Partial List)

  1. Ammer, J.R., Brummert, A.C., and Sams, W.N., “Miscible Applied Simulation Techniques for Energy Recovery — Version 2.0,” Report to U.S. DOE, Contract No. DOE/BC-91/2/SP, February 1991.

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  2. Chang, S.-H. and Grigg, R. B., “History Matching and Modeling the CO2-Foam Pilot Test at EVGSAU,” paper SPE 39793 presented at the 1998 SPE Permian Basin Oil and Gas Recovery Conference, Midland, TX.

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  3. Jang, J.-S. R., Sun, C.-T., and Mizutani, E., Neural-Fuzzy and Soft Computing, Prentice-Hall, 1997.

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  4. Klir, G.J and Yuan, B., Fuzzy Sets and Fuzzy Logic, Theory and Applications, Prentice-Hall, 1995.

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  5. Sung, A.H., H.J. Li, S.H. Chang, and R.B. Grigg, “Solving Nonlinear Engineering Problems with The Aid of Neural Networks,” Proceedings of SPIE, Vol. 3812 (Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II), 1999, pp.188–198.

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Author information

Authors and Affiliations

  1. Department of Computer Science, USA

    G. Janoski, F. -S. Li, M. Pietrzyk & A. H. Sung

  2. Petroleum Recovery Research Center, New Mexico Institute of Mining and Technology, Socorro, New Mexico, 87801, USA

    S. -H. Chang & R. B. Grigg

Authors
  1. G. Janoski
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  2. F. -S. Li
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  3. M. Pietrzyk
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  4. A. H. Sung
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  5. S. -H. Chang
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  6. R. B. Grigg
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Editor information

Editors and Affiliations

  1. The Center for Advanced Computer Studies, University of Lousiana, 2 Rex Street, Lafayette, LA, 70504-4330, USA

    Rasiah Logananthara

  2. Department of Neural Information Processing, University of Ulm, Oberer Eselsberg, 89069, Ulm, Germany

    Günther Palm

  3. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666-4616, USA

    Moonis Ali

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© 2000 Springer-Verlag Berlin Heidelberg

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Janoski, G., Li, F.S., Pietrzyk, M., Sung, A.H., Chang, S.H., Grigg, R.B. (2000). Advanced Reservoir Simulation Using Soft Computing. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_75

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  • DOI: https://doi.org/10.1007/3-540-45049-1_75

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67689-8

  • Online ISBN: 978-3-540-45049-8

  • eBook Packages: Springer Book Archive

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