Y. Z. Ma

Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 abridge 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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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 640
• Erscheinungstermin: 15. Juli 2019
• Englisch
• ISBN-13: 9783030178604
• Artikelnr.: 57133175

Autorenporträt

Dr. Ma is a scientific advisor for geosciences at Schlumberger, specialized in reservoir characterization, modeling and resource evaluation. In his over 30 years of experience, he has worked on research and application of statistics, data analytics, and geostatistics to integrated reservoir studies for major oil companies in Europe and the US and has provided technical consultancies and training worldwide. Dr. Ma has published over 100 technical papers or book chapters in petroleum geology, geophysics, engineering, geostatistics, applied statistics and economics, and has received numerous awards, including the Schlumberger's Gold Award and Chairman Award, and the Mathematical Geosciences' Best Paper. Dr. Ma has earned a PhD in Mathematical Geology and Geoinformatics from Université de Lorraine (previously Institute National Polytechnique de Lorraine), France, and MSc in Geostatistics from École des Mines de Paris, France, and a BSc in Geology from China University of Geosciences.

Inhaltsangabe

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.