Enhance Oil And Gas Exploration With Data–driven Geophysical And Petrophysical Models

By (author) Holdaway Keith R.; By (author) Irving, Duncan H. B.
Short description/annotation
Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data.
Description
Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. * Apply data-driven modeling concepts in a geophysical and petrophysical context * Learn how to get more information out of models and simulations * Add value to everyday tasks with the appropriate Big Data application * Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system''s physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.
Table of contents

Foreword xv

Preface xxi

Acknowledgments xxiii

Chapter 1 Introduction to Data-Driven Concepts 1

Introduction 2

Current Approaches 2

Is There a Crisis in Geophysical and Petrophysical Analysis? 3

Applying an Analytical Approach 4

What Are Analytics and Data Science? 5

Meanwhile, Back in the Oil Industry 8

How Do I Do Analytics and Data Science? 10

What Are the Constituent Parts of an Upstream Data Science Team? 13

A Data-Driven Study Timeline 15

What Is Data Engineering? 18

A Workflow for Getting Started 19

Is It Induction or Deduction? 30

References 32

Chapter 2 Data-Driven Analytical Methods Used in E&P 34

Introduction 35

Spatial Datasets 36

Temporal Datasets 37

Soft Computing Techniques 39

Data Mining Nomenclature 40

Decision Trees 43

Rules-Based Methods 44

Regression 45

Classification Tasks 45

Ensemble Methodology 48

Partial Least Squares 50

Traditional Neural Networks: The Details 51

Simple Neural Networks 54

Random Forests 59

Gradient Boosting 60

Gradient Descent 60

Factorized Machine Learning 62

Evolutionary Computing and Genetic Algorithms 62

Artificial Intelligence: Machine and Deep Learning 64

References 65

Chapter 3 Advanced Geophysical and Petrophysical Methodologies 68

Introduc