LEADER 05314nam 2200649 450 001 9910808016703321 005 20230807220206.0 010 $a1-5231-1025-2 010 $a1-119-03926-6 010 $a1-119-03920-7 010 $a1-119-03922-3 035 $a(CKB)3710000000440574 035 $a(EBL)1895972 035 $a(SSID)ssj0001515298 035 $a(PQKBManifestationID)12525313 035 $a(PQKBTitleCode)TC0001515298 035 $a(PQKBWorkID)11480846 035 $a(PQKB)11724575 035 $a(DLC) 2015011274 035 $a(Au-PeEL)EBL4040703 035 $a(CaPaEBR)ebr11113813 035 $a(CaONFJC)MIL814355 035 $a(MiAaPQ)EBC4040703 035 $a(OCoLC)905450339 035 $a(EXLCZ)993710000000440574 100 $a20150312h20152015 uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aFundamentals of gas shale reservoirs /$fedited by Reza Rezaee, Department of Petroleum Engineering 210 1$aHoboken, New Jersey :$cWiley,$d[2015] 210 4$dİ2015 215 $a1 online resource (420 p.) 300 $aDescription based upon print version of record. 311 $a1-118-64579-0 320 $aIncludes bibliographical references and index. 327 $aTitle Page; Copyright Page; Contents; Contributors; Preface; CHAPTER 1 GAS SHALE: GLOBAL SIGNIFICANCE, DISTRIBUTION, AND CHALLENGES; 1.1 INTRODUCTION; 1.2 SHALE GAS OVERVIEW; 1.2.1 Shale Gas Geology; 1.2.2 Characteristics of Producing Shale Gas Play; 1.3 THE SIGNIFICANCE OF SHALE GAS; 1.4 GLOBAL SHALE GAS RESOURCES; 1.4.1 Sources of Information; 1.4.2 Resource Estimation Methodologies; 1.5 GLOBAL RESOURCE DATA; 1.5.1 China; 1.5.2 The United States; 1.5.3 Mexico; 1.5.4 Southern South America; 1.5.5 South Africa; 1.5.6 Australia; 1.5.7 Canada; 1.5.8 North Africa; 1.5.9 Poland; 1.5.10 France 327 $a1.5.11 Russia 1.5.12 Scandinavia; 1.5.13 Middle East; 1.5.14 India; 1.5.15 Pakistan; 1.5.16 Northwest Africa; 1.5.17 Eastern Europe (Outside of Poland); 1.5.18 Germany and Surrounding Nations; 1.5.19 The United Kingdom; 1.5.20 Northern South America; 1.5.21 Turkey; 1.6 DATA ASSESSMENT; 1.6.1 Distribution; 1.6.2 Basin Type; 1.6.3 Depositional Environment; 1.6.4 TOC Content; 1.6.5 Clay Content; 1.7 INDUSTRY CHALLENGES; 1.7.1 Environmental Challenges; 1.7.2 Commercial/Economic; 1.8 DISCUSSION; 1.9 CONCLUSIONS; APPENDIX A.1 GLOBAL SHALE GAS RESOURCE DATA; REFERENCES 327 $aCHAPTER 2 ORGANIC MATTER-RICH SHALE DEPOSITIONAL ENVIRONMENTS 2.1 INTRODUCTION; 2.2 PROCESSES BEHIND THE DEPOSITION OF ORGANIC MATTER 2010;RICH SHALE; 2.2.1 Processes Behind the Transport and Deposition of Mud; 2.2.2 Production, Destruction, and Dilution: The Many Roads to Black Shale; 2.3 STRATIGRAPHIC DISTRIBUTION OF ORGANIC MATTER-RICH SHALES; 2.4 GEOGRAPHIC DISTRIBUTION OF ORGANIC MATTER-RICH SHALES; 2.4.1 Background; 2.4.2 Controls on the Geographic Distribution of Black Shales; 2.5 ORGANIC MATTER-RICH SHALE DEPOSITIONAL ENVIRONMENTS; 2.5.1 Continental Depositional Environments 327 $a2.5.2 Paralic Depositional Environments 2.5.3 Shallow Marine Depositional Environments; 2.5.4 Deep Marine Depositional Environments; 2.6 CONCLUSION; ACKNOWLEDGMENTS; REFERENCES; CHAPTER 3 GEOCHEMICAL ASSESSMENT OF UNCONVENTIONAL SHALE GAS RESOURCE SYSTEMS; 3.1 INTRODUCTION; 3.2 OBJECTIVE AND BACKGROUND; 3.3 KEROGEN QUANTITY AND QUALITY; 3.4 SAMPLE TYPE AND QUALITY; 3.5 KEROGEN TYPE and COMPOSITIONAL YIELDS; 3.6 THERMAL MATURITY; 3.7 ORGANOPOROSITY DEVELOPMENT; 3.8 GAS CONTENTS; 3.9 EXPULSION-RETENTION OF PETROLEUM; 3.10 SECONDARY (PETROLEUM) CRACKING; 3.11 UPPER MATURITY LIMIT FOR SHALE GAS 327 $a3.12 GAS COMPOSITION AND CARBON ISOTOPES 3.13 ADDITIONAL GEOCHEMICAL ANALYSES FOR SHALE GAS RESOURCE SYSTEM EVALUATION; 3.14 OIL AND CONDENSATE WITH SHALE GAS; 3.15 MAJOR SHALE GAS RESOURCE SYSTEMS; 3.16 CONCLUSIONS; REFERENCES; CHAPTER 4 SEQUENCE STRATIGRAPHY OF UNCONVENTIONAL RESOURCE SHALES; SUMMARY; 4.1 INTRODUCTION; 4.2 GENERAL SEQUENCE STRATIGRAPHIC MODEL FOR UNCONVENTIONAL RESOURCE SHALES; 4.3 AGES OF SEA-LEVEL CYCLES; 4.4 WATER DEPTH OF MUD TRANSPORT AND DEPOSITION; 4.5 CRITERIA TO IDENTIFY SEQUENCES AND SYSTEMS TRACTS; 4.6 PALEOZOIC RESOURCE SHALE EXAMPLES 327 $a4.6.1 Barnett Shale (Devonian) 330 $aProvides comprehensive information about the key exploration, development and optimization concepts required for gas shale reservoirs Includes statistics about gas shale resources and countries that have shale gas potential Addresses the challenges that oil and gas industries may confront for gas shale reservoir exploration and development Introduces petrophysical analysis, rock physics, geomechanics and passive seismic methods for gas shale plays Details shale gas environmental issues and challenges, economic consideration for gas shale reservoirsIncludes case studies of major producing gas shale 606 $aShale gas reservoirs 615 0$aShale gas reservoirs. 676 $a553.2/85 686 $aTEC031030$2bisacsh 702 $aRezaee$b Reza 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808016703321 996 $aFundamentals of gas shale reservoirs$94021853 997 $aUNINA LEADER 04357nam 22006135 450 001 9910484743003321 005 20200702043108.0 010 $a3-319-97277-4 024 7 $a10.1007/978-3-319-97277-0 035 $a(CKB)4100000006674597 035 $a(DE-He213)978-3-319-97277-0 035 $a(MiAaPQ)EBC5925539 035 $a(PPN)243768850 035 $a(EXLCZ)994100000006674597 100 $a20181111d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Computing in Industrial Mathematics $e12th Annual Meeting of the Bulgarian Section of SIAM December 20-22, 2017, Sofia, Bulgaria Revised Selected Papers /$fedited by Krassimir Georgiev, Michail Todorov, Ivan Georgiev 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (X, 446 p. 130 illus., 85 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v793 311 $a3-319-97276-6 327 $aMethod for indoor localization of mobile devices based on AoA and Kalman filtering -- Cross-Validated Sequentially Constructed Multiple Regression -- How to assess multi-population genetic algorithms performance using intuitionistic fuzzy logic -- Perturbation analysis of a nonlinear matrix equation arising in Tree-like stochastic processes -- On Two-Way Generalized Nets -- Variational methods for stable time discretization of first-order differential equations -- Numerical solutions of ordinary fractional differential equations with singularities -- Preliminary analysis of the dynamics of multifractal parameters of the Dow Jones Industrial Average -- Testing performance and scalability of the pure MPI model versus hybrid MPI-2/OpenMP model on the heterogeneous supercomputer Avitohol -- Analyses and Boolean operation of 2D polygons -- Ant Colony Optimization Algorithm for Workforce Planning: Inuence of the Algorithm Parameters -- Convergence of homotopy perturbation method for solving of two-dimensional fuzzy Volterra functional integral equations -- Iterative method for numerical solution of two-dimensional nonlinear Urysohn fuzzy integral equations. 330 $aThis book gathers the peer-reviewed proceedings of the 12th Annual Meeting of the Bulgarian Section of the Society for Industrial and Applied Mathematics, BGSIAM?17, held in Sofia, Bulgaria, in December 2017. The general theme of BGSIAM?17 was industrial and applied mathematics, with a particular focus on: high-performance computing, numerical methods and algorithms, analysis of partial differential equations and their applications, mathematical biology, control and uncertain systems, stochastic models, molecular dynamics, neural networks, genetic algorithms, metaheuristics for optimization problems, generalized nets, and Big Data. . 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v793 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputer science?Mathematics 606 $aComputer science$xMathematics 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematical Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M13110 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aComputer science?Mathematics. 615 0$aComputer science$xMathematics. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aMathematical Applications in Computer Science. 676 $a510 702 $aGeorgiev$b Krassimir$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTodorov$b Michail$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGeorgiev$b Ivan$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484743003321 996 $aAdvanced Computing in Industrial Mathematics$93006285 997 $aUNINA