LEADER 01106nam--2200373---450- 001 990001254550203316 005 20031111115716.0 010 $a0674994418 035 $a000125455 035 $aUSA01000125455 035 $a(ALEPH)000125455USA01 035 $a000125455 100 $a20031111h1965----km-y0itay0103----ba 101 $aeng 102 $aUK 105 $a||||||||001yy 200 1 $aAristotle on sophistical refutations on coming-to-be and passing-away$eOn the cosmos$gby E.S.Forster, D.J.Furley 210 $aCambridge$cHarvard University$d1965 215 $a413 p.$d17 cm 410 $12001 461 1$1001-------$12001 676 $a185 700 1$aARISTOTELES$04207 702 1$aFURLEY,$bD.J. 702 1$aFORSTER,$bE.S. 801 0$aIT$bsalbc$gISBD 912 $a990001254550203316 951 $aVIII A 63/112$b37244 L.M.$cVIII A 959 $aBK 969 $aUMA 979 $aSIAV1$b10$c20031111$lUSA01$h1157 979 $aPATRY$b90$c20040406$lUSA01$h1729 996 $aAristotle on sophistical refutations on coming-to-be and passing-away$9986549 997 $aUNISA LEADER 01393nam1 2200265 i 450 001 VAN0125252 005 20220207093459.420 100 $a20191105d2014 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 200 1 $aAnd Yet It Is Heard$eMusical, Multilingual and Multicultural History of the Mathematical Sciences$fTito M. Tonietti 210 $aBasel$cBirkhäuser$d2014 215 $avolumi$cill.$d24 cm 463 1$1001VAN0125249$12001 $aˆ<<‰And Yet It Is Heard$eMusical, Multilingual and Multicultural History of the Mathematical Sciences>> 1$fTito M. Tonietti$1210 $aBasel$cBirkhäuser$d2014$1215 $axiv, 407 p.$cill.$d24 cm$v1 463 1$1001VAN0125251$12001 $aˆ<<‰And Yet It Is Heard$eMusical, Multilingual and Multicultural History of the Mathematical Sciences>> 2$fTito M. Tonietti$1210 $aBasel$cBirkhäuser$d2014$1215 $aviii, 593 p.$cill.$d24 cm$v2 500 1$3VAN0239383$aAnd Yet It Is Heard : Musical, Multilingual and Multicultural History of the Mathematical Sciences$92961677 620 $dBasel$3VANL002076 700 1$aTonietti$bTito M.$3VANV096693$057167 712 $aBirkhäuser $3VANV108193$4650 801 $aIT$bSOL$c20230616$gRICA 912 $fN 912 $aVAN0125252 996 $aAnd Yet It Is Heard : Musical, Multilingual and Multicultural History of the Mathematical Sciences$92961677 997 $aUNICAMPANIA LEADER 05619nam 2200673 a 450 001 9911006643403321 005 20200520144314.0 010 $a1-281-03881-4 010 $a9786611038816 010 $a0-08-054132-1 035 $a(CKB)1000000000358257 035 $a(EBL)312794 035 $a(OCoLC)182518835 035 $a(SSID)ssj0000072989 035 $a(PQKBManifestationID)12006793 035 $a(PQKBTitleCode)TC0000072989 035 $a(PQKBWorkID)10103265 035 $a(PQKB)10323479 035 $a(MiAaPQ)EBC312794 035 $a(EXLCZ)991000000000358257 100 $a20031106d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSoft computing and intelligent data analysis in oil exploration /$fedited by M. Nikravesh, F. Aminzadeh, L.A. Zadeh 205 $a1st ed. 210 $aAmsterdam ;$aBoston $cElsevier$d2003 215 $a1 online resource (755 p.) 225 1 $aDevelopments in petroleum science,$x0376-7361 ;$v51 300 $aDescription based upon print version of record. 311 $a0-444-50685-3 320 $aIncludes bibliographical references and indexes. 327 $aFront Cover; Development in Petroleum Science: Soft Computing and Intellegent Data Analysis in Oil Exploration; Copyright Page; Foreword; Preface; About the Editors; List of Contributors; Contents; Part 1: Introduction: Fundamentals of Soft Computing; CHAPTER 1. SOFT COMPUTING FOR INTELLIGENT RESERVOIR CHARACTERIZATION AND MODELING; Abstract; 1. Introduction; 2. The role of soft computing techniques for intelligent reservoir characterization and exploration; 3. Artificial neural network and geoscience applications of artificial neural networks for exploration; 4. Fuzzy logic 327 $a5. Genetics algorithms6. Principal component analysis and wavelet; 7. Intelligent reservoir characterization; 8. Fractured reservoir characterization; 9. Future trends and conclusions; Appendix A. A basic primer on neural network and fuzzy logic terminology; Appendix B. Neural networks; Appendix C. Modified Levenberge-Marquardt technique; Appendix D. Neuro-fuzzy models; Appendix E. K-means clustering; Appendix F. Fuzzy c-means clustering; Appendix G. Neural network clustering; References; CHAPTER 2. FUZZY LOGIC; Abstract; 1. Fuzzy sets; 2. Operations on fuzzy sets 327 $a3. Arithmetic of fuzzy intervals4. Fuzzy relations; 5. Fuzzy systems; 6. Fuzzy propositions; 7. Approximate reasoning; 8. Suggestions for further study; References; CHAPTER 3. INTRODUCTION TO USING GENETIC ALGORITHMS; 1. Introduction; 2. Background to Genetic Algorithms; 3. Design of a Genetic Algorithm; 4. Conclusions; References; CHAPTER 4. HEURISTIC APPROACHES TO COMBINATORIAL OPTIMIZATION; 1. Introduction; 2. Decision variables; 3. Properties of the objective function; 4. Heuristic techniques; References; CHAPTER 5. INTRODUCTION TO GEOSTATISTICS; 1. Introduction; 2. Random variables 327 $a3. Covariance and spatial variability4. Kriging; 5. Stochastic simulations; References; CHAPTER 6. GEOSTATISTICS: FROM PATTERN RECOGNITION TO PATTERN REPRODUCTION; 1. Introduction; 2. The decision of stationarity; 3. The multi-Gaussian approach to spatial estimation and simulation; 4. Spatial interpolation with kriging; 5. Beyond two-point models: multiple-point geostatistics; 6. Conclusions; 7. Glossary; References; Part 2: Geophysical Analysis and Interpretation; CHAPTER 7. MINING AND FUSION OF PETROLEUM DATA WITH FUZZY LOGIC AND NEURAL NETWORK AGENTS; Abstract; 1. Introduction 327 $a2. Neural network and nonlinear mapping3. Neuro-fuzzy model for rule extraction; 4. Conclusion; Appendix A. Basic primer on neural network and fuzzy logic terminology; Appendix B. Neural networks; Appendix C. Modified Levenberge-Marquardt technique; Appendix D. Neuro-fuzzy models; Appendix E. K-means clustering; References; CHAPTER 8. TIME LAPSE SEISMIC AS A COMPLEMENTARY TOOL FOR IN-FILL DRILLING; Abstract; 1. Introduction; 2. Feasibility study; 3. 3D seismic data sets; 4. 4D seismic analysis approach; 5. Seismic modeling of various flow scenarios; 6. 4D seismic for detecting fluid movement 327 $a7. 4D seismic for detecting pore pressure changes 330 $aThis comprehensive book highlights soft computing and geostatistics applications in hydrocarbon exploration and production, combining practical and theoretical aspects. It spans a wide spectrum of applications in the oil industry, crossing many discipline boundaries such as geophysics, geology, petrophysics and reservoir engineering. It is complemented by several tutorial chapters on fuzzy logic, neural networks and genetic algorithms and geostatistics to introduce these concepts to the uninitiated. The application areas include prediction of reservoir properties (porosity, sand thic 410 0$aDevelopments in petroleum science ;$v51. 606 $aPetroleum$xProspecting$xData processing 606 $aHydrocarbon reservoirs$xComputer simulation 606 $aSoft computing 615 0$aPetroleum$xProspecting$xData processing. 615 0$aHydrocarbon reservoirs$xComputer simulation. 615 0$aSoft computing. 676 $a622/.18282/0285 701 $aNikravesh$b Masoud$f1959-$01825429 701 $aAminzadeh$b Fred$01342851 701 $aZadeh$b Lotfi Asker$01975 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911006643403321 996 $aSoft computing and intelligent data analysis in oil exploration$94393101 997 $aUNINA