BISC: NEW BOOK ANNOUNCEMENT

From: masoud nikravesh (nikraves@eecs.berkeley.edu)
Date: Fri Jun 08 2001 - 07:04:28 MET DST

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    Berkeley Initiative in Soft Computing (BISC)
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    NEW BOOK ANNOUNCEMENT
    =========================
    P.M. Wong, University of New South Wales, Australia;
    F. Aminzadeh, dGB-USA & FACT, USA;
    M. Nikravesh, University of California, Berkeley, USA (Eds.)
    ===========================================

    Soft Computing for Reservoir Characterization and Modeling

    Series: Studies in Fuzziness and Soft Computing, Volume 80

    2001, 588 pp., 317 figs., 34 tabs, ISBN 3-7908-1421-0, DM 179,90 (Net
    price), approx. USD 115.-Physica-Verlag (http://www.springer.de),
    Heidelberg, to be published in October 2001

    The volume is the first comprehensive book in the area of intelligent
    reservoir characterization written byleading experts in academia and
    industry. It contains state-of-the-art techniques to be applied in
    reservoirgeophysics, well logging, reservoir geology, and reservoir
    engineering. It introduces the basic concepts of soft computing
    techniques including neural networks, fuzzy logic and evolutionary
    computing applied to reservoir characterization. Some advanced
    statistical and hybrid models are also presented. The specific
    applications include different reservoir characterization topics such as
    prediction of petrophysical properties from well logs and seismic
    attributes.

    Keywords: Geology. Geophysics. Reservoir Characterization. Oil and Gas.
    Soft Computing.
    Contents: B. Braunschweig, Foreword;
    P.M. Wong, F. Aminzadeh, M. Nikravesh, Intelligent Reservoir
    Characterization;

    Seismic Characterization: K. Nakayama, J. Hou, Prediction of Reservoir
    Properties by Monte Carlo Simulation and Artificial Neural Network in
    the Exploration Stage; B. Widarsono, S. Munadi, F. Saptono, Application
    of Neural Networks in Determining Petrophysical Properties from Seismic
    Survey; H. Trappe, C. Hellmich, J. Knudsen, H. Baartman, Mapping the Gas
    Column in an Aquifer Gas Storage using Neural Network Techniques; A.
    Consonni, R. Iantosca, P. Ruffo, Interval and Fuzzy Kriging Techniques
    applied to Geological and Geophysical Variables; C.A. Link, J. Conaway,
    Application of Self-Organizing Feature Maps to Reservoir
    Characterization; Well Logging: S. Mohaghegh, Taking One Step Forward in
    Reservoir Characterization using Artificial Neural Networks; R.
    Ramberger, J. Skolnakorn, Inverting SP Logs using Artificial Neural
    Networks and the Application in Reservoir Characterization; J. Finol, X.
    Jing, Predicting Petrophysical Parameters in a Fuzzy Environment; S.J.
    Cuddy, P.W.J. Glover, The Application of Fuzzy Logic and Genetic
    Algorithms to Reservoir Characterization and Modeling; K.W. Wong, T.D.
    Gedeon, C.C. Fung, The Use of Soft Computing Techniques as Data
    Preprocessing and Postprocessing in Permeability Determination from Well
    Log Data; A.M. Dawood, A.A. Ibrahim, S. A. El-Tayeb, A New Technique to
    Estimate the Hydrocarbon Saturation in Shaly Formations: A Field Example
    in the Bahariya Formation, Egypt; Numerical Geology: C.-S. Kim,
    Automated Reconstruction of a Basin Thermal History with Integrated
    Paleothermometry and Genetic Algorithm; F. Mansanné, M. Schoenauer, An
    Automatic Geophysical Inversion Procedure using a Genetic Algorithm; J.
    Caers, S. Srinivasan, Statistical Pattern Recognition and Geostatistical
    Data Integration; R. Soto B., F. Torres, A. Arango, G. Cobaleda, S.
    Holditch, C. Wu, How to Improve Reservoir Characterization Models using
    Intelligent Systems; C. Coll, A. Muggeridge and X. Jing, Regional
    Upscaling: A New Method to Upscale Heterogeneous Reservoirs for a Range
    of Force Regimes; Advanced Algorithms: C.-S. Kim, New Uncertainty
    Measures for Predicted Geological Properties from Seismic Attribute
    Calibration; C.V. Deutsch, Y.L. Xie and A.S. Cullick, Rule Induction
    Algorithm for Application to Geological and Petrophysical Data; L.-Y.
    Fu, Joint Lithologic Inversion; M. Kanevski, A. Pozdnukhov, S. Canu, M.
    Maignan, P.M. Wong, S.A.R. Shibli, Support Vector Machines for
    Classification and Mapping of Reservoir Data; T.-T. Yao, Non-parametric
    Covariance Modeling using Fast Fourier Transform.

    The book can be ordered as of July 1 st , 2001. If you want to order it,
    please send an email to
    orders@springer.de (for Europe and rest of the world) or
    orders@springer-ny.com (for USA and Canada)

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