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Applied Statistical Modeling and Data Analytics

Book Companion

Applied Statistical Modeling and Data Analytics

Edition 1

Welcome to the website for Applied Statistical Modeling and Data Analytics, 1st Edition.

The essential reference for geoscientists and engineers who apply statistical modeling and data analytics techniques to solve petroleum industry challenges.

1. Authored by internationally renowned experts in developing and applying statistical methods for oil and gas and other subsurface problem domains. 2. Written by practitioners for practitioners 3. Presents an easy-to-follow narrative that progresses from simple concepts to more challenging ones 4. Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences 5. Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications This website contains the online resources for this book. It includes datasets as well as software packages and scripts used in the book.

ABOUT THE BOOK

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. Datasets 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.

Dr. Srikanta Mishra ([email protected]opens in new tab/window) is Institute Fellow and Chief Scientist for Energy at Battelle Memorial Institute, where he leads computational modeling and data analytics activities for geologic carbon storage, shale gas 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 the University of Texas and a BTech degree from the Indian School of Mines – all in petroleum engineering.

Dr. Akhil Datta-Gupta ([email protected]opens in new tab/window) 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 the 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.

Table of Contents

1. Basic Concepts 1.1 Background and Scope 1.2 Data, Statistics, and Probability 1.3 Random Variables 1.4 Summary

2. Exploratory Data Analysis 2.1 Univariate Data 2.2 Bivariate Data 2.3 Multivariate Data 2.4 Summary 3. Distributions and Models Thereof 3.1 Empirical Distributions 3.2 Parametric Models 3.3 Working With Normal and Log-Normal Distributions 3.4 Fitting Distributions to Data 3.5 Other Properties of Distributions and Their Evaluation 3.6 Summary

4. Regression Modeling and Analysis 4.1 Introduction 4.2 Simple Linear Regression 4.3 Multiple Regression 4.4 Nonparametric Transformation and Regression 4.5 Field Application for Nonparametric Regression:The Salt Creek Data Set 4.6 Summary 5. Multivariate Data Analysis 5.1 Introduction 5.2 Principal Component Analysis 5.3 Cluster Analysis 5.4 Discriminant Analysis 5.5 Field Application: The Salt Creek Data Set 5.6 Summary

6. Uncertainty Quantification 6.1 Introduction 6.2 Uncertainty Characterization 6.3 Uncertainty Propagation 6.4 Uncertainty Importance Assessment 6.5 Moving Beyond Monte Carlo Simulation 6.6 Treatment of Model Uncertainty 6.7 Elements of a Good Uncertainty Analysis Study 6.8 Summary 7. Experimental Design and Response Surface Analysis  7.1 General Concepts 7.2 Experimental Design 7.3 Metamodeling Techniques 7.4 An Illustration of Experimental Design and Response Surface Modeling 7.5 Field Application of Experimental Design and Response Surface Modeling 7.6 Summary 8. Data-Driven Modeling 8.1 Introduction 8.2 Modeling Approaches 8.3 Computational Considerations 8.4 Field Example 8.5 Summary 9. Concluding Remarks 9.1 The Path We Have Taken 9.2 Key Takeaways 9.3 Final Thoughts

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