For example, if you want to increase the strength of a device, without adding too much extra weight to it, the contradiction matrix tells you that you can use "Principle 1: Segmentation," or "Principle 8: Counterweight," or "Principle 15: Dynamicity," or "Principle 40: Composite Materials.

Intelligent Reservoir Characterization.- 1. Seismic Characterization.- Prediction of Reservoir Properties by Monte Carlo Simulation and Artificial Neural Network in the Exploration Stage.- Application of Neural Networks in Determining Petrophysical Properties from Seismic Survey.- Mapping the Gas Column in an Aquifer Gas Storage with Neural Network Techniques.- Interval and Fuzzy Kriging Techniques Applied to Geological and Geophysical Variables.- Application of Self-Organizing Feature Maps to Reservoir Characterization.- 2. Well Logging.- Taking One Step Forward in Reservoir Characterization Using Artificial Neural Networks.- Inverting SP Logs Using Artificial Neural Networks and the Application in Reservoir Characterization.- Predicting Petrophysical Parameters in a Fuzzy Environment.- The Application of Fuzzy Logic and Genetic Algorithms to Reservoir Characterization and Modeling.- The Use of Soft Computing Techniques as Data Preprocessing and Postprocessing in Permeability Determination from Well Log Data.- A New Technique to Estimate the Hydrocarbon Saturation in Shaly Formations: A Field Example in the Bahariya Formation, Egypt.- 3. Numerical Geology.- Automated Reconstruction of a Basin Thermal History with Integrated Paleothermometry and Genetic Algorithm.- An Automatic Geophysical Inversion Procedure Using a Genetic Algorithm.- Statistical Pattern Recognition and Geostatistical Data Integration.- How to Improve Reservoir Characterization Models Using Intelligent Systems.- Regional Upscaling: A New Method to Upscale Heterogeneous Reservoirs for a Range of Force Regimes.- 4. Advanced Algorithms.- New Uncertainty Measures for Predicted Geological Properties from Seismic Attribute Calibration.- Rule Induction Algorithm for Application to Geological and Petrophysical Data.- Joint Lithologic Inversion.- Support Vector Machines for Classification and Mapping of Reservoir Data.- Non-parametric Covariance Modeling Using Fast Fourier Transform.- About the Editors.

The volume is the first comprehensive book in the area of intelligent reservoir characterization written by leading experts in academia and industry. It contains state-of-the-art techniques to be applied in reservoir geophysics, 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.