Applied Statistical Modeling and Data Analytics

A Practical Guide for the Petroleum Geosciences

Srikanta Mishra, Akhil Datta-Gupta

• Publisher: Elsevier
• Year: 2017
• ISBN:
  9780128032800
• Topic: Physical Sciences
• Subtopic: Geology & Earth Sciences

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

Chapter 1

Basic Concepts

Abstract

Statistics is the science of acquiring and utilizing data. It provides us with the tools for data collection, summarization, and interpretation, with the goal of identifying the underlying structure, trends, and relationships inherent in the data. This is how we convert data into information.

Keywords

Analysis; Bayes rule; Data; Knowledge; Population; Probability; Statistic

Outline

1.1 Background and Scope

1.1.1 What Is Statistics?

1.1.2 What Is Big Data Analytics?

1.1.3 Data Analysis Cycle

1.1.4 Some Applications in the Petroleum Geosciences

1.2 Data, Statistics, and Probability

1.2.1 Outcomes and Events

1.2.2 Probability

1.2.3 Conditional Probability and Bayes Rule

1.3 Random Variables

1.3.1 Discrete Case

1.3.2 Continuous Case

1.3.3 Indicator Transform

1.4 Summary

Exercises

References

1.1 Background and Scope

We introduce the reader to some fundamental concepts of classical statistics such as probability and random variables, along with basic concepts from the emerging field of data analytics and big-data technologies. We also list some typical applications of the relevant techniques for data analysis in the petroleum geosciences.

1.1.1 What Is Statistics?

Statistics is the science of acquiring and utilizing data. It provides us with the tools for data collection, summarization, and interpretation—with the goal of identifying the underlying structure, trends, and relationships inherent in the data. This is how we convert data into information.

Fundamental to statistics are the concepts of population and sample. A population is the universe of all possible outcomes and events, whereas a sample is a finite subset extracted from the population. Statistical analyses are performed on the sampled data to draw inference about the characteristics of the population, without having to study the entire population. The population is exhaustive and is characterized by its parameters. The sample is limited and is characterized by the statistic that is related to the population parameters.

Fig. 1.1 shows a schematic of the relationship between population and sample. Here, the population represents permeability values for an entire oil reservoir at the scale of a small core plug. To learn more about the distribution of permeability values, in step (1), we randomly sample this population using a finite number of core plugs (e.g., 250). In step (2), we analyze these permeability values to determine the proportion of plugs with permeability greater than 10 mD (e.g., 65%). Finally, in step (3), we determine the representativeness of this result for the entire population (e.g., 95% certain that margin of error is ± 6%).

Fig. 1.1 Schematic showing population—sample relationship.

Application of statistics to any dataset generally begins with exploratory data analysis. Here, the goal is to q...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Preface
• Acknowledgments
• Chapter 1: Basic Concepts
• Chapter 2: Exploratory Data Analysis
• Chapter 3: Distributions and Models Thereof
• Chapter 4: Regression Modeling and Analysis
• Chapter 5: Multivariate Data Analysis
• Chapter 6: Uncertainty Quantification
• Chapter 7: Experimental Design and Response Surface Analysis
• Chapter 8: Data-Driven Modeling
• Chapter 9: Concluding Remarks
• Index