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

Ma, Y. Z.

Softcover

ISBN 10: 3030178625 ISBN 13: 9783030178628

Verlag: Springer, 2020

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

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.

Von der hinteren Coverseite

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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2020
• Sprache: Englisch
• ISBN 10: 3030178625
• ISBN 13: 9783030178628
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 665
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland