Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains Written by practitioners for practitioners Presents an easy to follow narrative which progresses from simple concepts to more challenging ones Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications
This book reviews some of today’s more complex problems, and reflects some of the important research directions in the field.
Castaldi P, Dahabreh I, Ioannidis J (2011). “An Empirical Assessment of Validation Practices for ... Chambers J (2008). Software for Data Analysis: Programming with R. Springer ... Cohen G, Hilario M, Pellegrini C, Geissbuhler A (2005).
The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area.
Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design.
This volume includes contributions selected after a double blind review process and presented as a preliminary version at the 45th Meeting of the Italian Statistical Society.
This book on elementary topics in mathematical modeling and data analysis is intended for an undergraduate “liberal arts mathematics”-type course but with a specific focus on environmental applications.
The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material.
This text teaches engineering and applied science students to incorporate empirical investigation into such design processes.
... Mario Angst, John Ahlquist, Kyle Beardsley, Pablo Beramendi, Nathaniel Beck, Roger Bivand, Mirjam Anna Bruederle, Xun Cao, Lars-Erik Cederman, Hannah Dönges, Cassy L. Dorff, Ulrich Eberle, Jos Elkink, Shauna Fisher, Max Blau Gallop, ...
This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research.