Applied Statistical Modeling and Data Analytics

  • ID: 4226382
  • Book
  • 250 Pages
  • Elsevier Science and Technology
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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

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1. Basic Concepts 2. Exploratory Data Analysis 3. Distributions and Models Thereof 4. Regression Modeling and Analysis 5. Multivariate Data Analysis 6. Uncertainty Quantification 7. Experimental Design and Response Surface Analysis 8. Data-Driven Modeling 9. Concluding Remarks

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Mishra, Srikanta
Dr. Srikanta Mishra is Institute Fellow and Chief Scientist (Energy) at Battelle Memorial Institute, where he leads computational modeling and data analytics activities for geologic carbon storage, shale gas and oil development, and improved oil recovery projects. He joined Battelle in 2010 after a distinguished 20+ year career in geosystems consulting and applied research, including an appointment as adjunct professor of Petroleum Engineering at the University of Texas at Austin. He holds a PhD degree from Stanford University, an MS degree from University of Texas and a BTech degree from Indian School of Mines - all in petroleum engineering.

Drs. Mishra and Datta-Gupta have published extensively on the topics covered in this book, and have taught short courses at professional society meetings and client locations all over the world.
Datta-Gupta, Akhil
Dr. Akhil Datta-Gupta is Regents Professor, University Distinguished Professor, and Peterson '36 Chair in petroleum engineering at Texas A&M University. He directs the "Model Calibration and Efficient Reservoir Imaging” Joint Industry Project carrying out research on statistical modeling, multiphase flow simulation and inverse modeling of reservoir data. Dr. Datta-Gupta joined Texas A&M in 1994 after a brief industry career and was elected to the US National Academy of Engineering in 2012. He holds PhD and MS degrees from University of Texas and a BTech degree from Indian School of Mines - all in petroleum engineering.

Drs. Mishra and Datta-Gupta have published extensively on the topics covered in this book, and have taught short courses at professional society meetings and client locations all over the world.
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