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Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

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Personen und Körperschaften: Ma, Y. Z (VerfasserIn)
Titel: Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling/ by Y. Z. Ma
Ausgabe: 1st ed. 2019
Format: E-Book
Sprache: Englisch
veröffentlicht:
Cham Springer 2019
Gesamtaufnahme: Springer eBooks
Springer eBook Collection
Schlagwörter:
GeologyxMathematics
Physical geography
Fossil Fuels (incl. Carbon Capture)
Statistics
Operations research
Geotechnical engineering.
Fossil fuels.
Geology—Statistical methods.
Decision making.
Geophysics.
Quelle: Verbunddaten SWB
Zugangsinformationen: Elektronischer Volltext - Campuslizenz
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520 |a Preface -- 1. Introduction and Overview -- Part 1: Reservoir Characterization -- 2. Essential Reservoir Geology and Multi-Scales of Petroleum Reservoir Heterogeneities -- 3. Introduction to Petrophysical Reservoir Characterization -- 4. Practical Seismic Reservoir Characterization -- 5. Statistical and Data Analytical Reservoir Characterization -- 6. Geostatistical Reservoir Characterization -- 7. Integrated Facies and Lithofacies Analysis, Identification and Classification -- Part 2: Geological and Reservoir Modeling -- 8. Constructing a Reservoir-Model Framework -- 9. Geostatistical Modeling Methods -- 10. Facies and Lithofacies Modeling -- 11. Porosity Modeling -- 12. Permeability Modeling -- 13. Fluid-Saturation Modeling -- 14. Uncertainty Analysis and Volumetrics Evaluation -- Part 3: Special and Advanced Topics -- 15. Naturally Fractured Reservoir Characterization and Modeling -- 16. Updating a Reservoir Model and Feedback Loop in Reservoir Modeling -- 17. Ranking Reservoir Models -- 18. Reservoir Model Upscaling, Simulation and Validation -- 19. Common and Uncommon Pitfalls in Integrated Reservoir Characterization and Modeling -- 20. Planning an Integrated Reservoir Characterization and Modeling Project -- 21. Towards a Fully Integrated Reservoir Characterization, Modeling and Uncertainty Analysis for Petroleum Resource Management and Field Development 
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contents Preface -- 1. Introduction and Overview -- Part 1: Reservoir Characterization -- 2. Essential Reservoir Geology and Multi-Scales of Petroleum Reservoir Heterogeneities -- 3. Introduction to Petrophysical Reservoir Characterization -- 4. Practical Seismic Reservoir Characterization -- 5. Statistical and Data Analytical Reservoir Characterization -- 6. Geostatistical Reservoir Characterization -- 7. Integrated Facies and Lithofacies Analysis, Identification and Classification -- Part 2: Geological and Reservoir Modeling -- 8. Constructing a Reservoir-Model Framework -- 9. Geostatistical Modeling Methods -- 10. Facies and Lithofacies Modeling -- 11. Porosity Modeling -- 12. Permeability Modeling -- 13. Fluid-Saturation Modeling -- 14. Uncertainty Analysis and Volumetrics Evaluation -- Part 3: Special and Advanced Topics -- 15. Naturally Fractured Reservoir Characterization and Modeling -- 16. Updating a Reservoir Model and Feedback Loop in Reservoir Modeling -- 17. Ranking Reservoir Models -- 18. Reservoir Model Upscaling, Simulation and Validation -- 19. Common and Uncommon Pitfalls in Integrated Reservoir Characterization and Modeling -- 20. Planning an Integrated Reservoir Characterization and Modeling Project -- 21. Towards a Fully Integrated Reservoir Characterization, Modeling and Uncertainty Analysis for Petroleum Resource Management and Field Development, Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development
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spelling Ma, Y. Z VerfasserIn aut, Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling by Y. Z. Ma, 1st ed. 2019, Cham Springer 2019, 1 Online-Ressource (XXV, 640 p. 294 illus., 167 illus. in color), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Springer eBooks Energy, Springer eBook Collection, Preface -- 1. Introduction and Overview -- Part 1: Reservoir Characterization -- 2. Essential Reservoir Geology and Multi-Scales of Petroleum Reservoir Heterogeneities -- 3. Introduction to Petrophysical Reservoir Characterization -- 4. Practical Seismic Reservoir Characterization -- 5. Statistical and Data Analytical Reservoir Characterization -- 6. Geostatistical Reservoir Characterization -- 7. Integrated Facies and Lithofacies Analysis, Identification and Classification -- Part 2: Geological and Reservoir Modeling -- 8. Constructing a Reservoir-Model Framework -- 9. Geostatistical Modeling Methods -- 10. Facies and Lithofacies Modeling -- 11. Porosity Modeling -- 12. Permeability Modeling -- 13. Fluid-Saturation Modeling -- 14. Uncertainty Analysis and Volumetrics Evaluation -- Part 3: Special and Advanced Topics -- 15. Naturally Fractured Reservoir Characterization and Modeling -- 16. Updating a Reservoir Model and Feedback Loop in Reservoir Modeling -- 17. Ranking Reservoir Models -- 18. Reservoir Model Upscaling, Simulation and Validation -- 19. Common and Uncommon Pitfalls in Integrated Reservoir Characterization and Modeling -- 20. Planning an Integrated Reservoir Characterization and Modeling Project -- 21. Towards a Fully Integrated Reservoir Characterization, Modeling and Uncertainty Analysis for Petroleum Resource Management and Field Development, Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development, GeologyxMathematics, Physical geography, Fossil Fuels (incl. Carbon Capture), Statistics, Operations research, Geotechnical engineering., Fossil fuels., Geology—Statistical methods., Decision making., Geophysics., 9783030178598, Erscheint auch als Druck-Ausgabe 978-3-030-17859-8, https://doi.org/10.1007/978-3-030-17860-4 X:SPRINGER Resolving-System lizenzpflichtig, https://swbplus.bsz-bw.de/bsz1671006496cov.jpg V:DE-576 X:SPRINGER image/jpeg 20191031170138 Cover, https://doi.org/10.1007/978-3-030-17860-4 DE-14, DE-14 epn:3588601252 2020-02-06T11:34:38Z, https://doi.org/10.1007/978-3-030-17860-4 DE-Ch1, DE-Ch1 epn:4027667174 2021-12-29T10:31:26Z, DE-105 epn:3505872059 2019-08-08T14:42:55Z, https://doi.org/10.1007/978-3-030-17860-4 DE-Zwi2, DE-Zwi2 epn:3505752088 2019-08-08T12:54:49Z, https://doi.org/10.1007/978-3-030-17860-4 Zum Online-Dokument DE-Zi4, DE-Zi4 epn:3505752096 2019-08-08T12:54:49Z
spellingShingle Ma, Y. Z, Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling, Preface -- 1. Introduction and Overview -- Part 1: Reservoir Characterization -- 2. Essential Reservoir Geology and Multi-Scales of Petroleum Reservoir Heterogeneities -- 3. Introduction to Petrophysical Reservoir Characterization -- 4. Practical Seismic Reservoir Characterization -- 5. Statistical and Data Analytical Reservoir Characterization -- 6. Geostatistical Reservoir Characterization -- 7. Integrated Facies and Lithofacies Analysis, Identification and Classification -- Part 2: Geological and Reservoir Modeling -- 8. Constructing a Reservoir-Model Framework -- 9. Geostatistical Modeling Methods -- 10. Facies and Lithofacies Modeling -- 11. Porosity Modeling -- 12. Permeability Modeling -- 13. Fluid-Saturation Modeling -- 14. Uncertainty Analysis and Volumetrics Evaluation -- Part 3: Special and Advanced Topics -- 15. Naturally Fractured Reservoir Characterization and Modeling -- 16. Updating a Reservoir Model and Feedback Loop in Reservoir Modeling -- 17. Ranking Reservoir Models -- 18. Reservoir Model Upscaling, Simulation and Validation -- 19. Common and Uncommon Pitfalls in Integrated Reservoir Characterization and Modeling -- 20. Planning an Integrated Reservoir Characterization and Modeling Project -- 21. Towards a Fully Integrated Reservoir Characterization, Modeling and Uncertainty Analysis for Petroleum Resource Management and Field Development, Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development, GeologyxMathematics, Physical geography, Fossil Fuels (incl. Carbon Capture), Statistics, Operations research, Geotechnical engineering., Fossil fuels., Geology—Statistical methods., Decision making., Geophysics.
title Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling
title_auth Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling
title_full Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling by Y. Z. Ma
title_fullStr Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling by Y. Z. Ma
title_full_unstemmed Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling by Y. Z. Ma
title_short Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling
title_sort quantitative geosciences: data analytics, geostatistics, reservoir characterization and modeling
title_unstemmed Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling
topic GeologyxMathematics, Physical geography, Fossil Fuels (incl. Carbon Capture), Statistics, Operations research, Geotechnical engineering., Fossil fuels., Geology—Statistical methods., Decision making., Geophysics.
topic_facet GeologyxMathematics, Physical geography, Fossil Fuels (incl. Carbon Capture), Statistics, Operations research, Geotechnical engineering., Fossil fuels., Geology—Statistical methods., Decision making., Geophysics.
url https://doi.org/10.1007/978-3-030-17860-4, https://swbplus.bsz-bw.de/bsz1671006496cov.jpg
work_keys_str_mv AT mayz quantitativegeosciencesdataanalyticsgeostatisticsreservoircharacterizationandmodeling