As the shale revolution continues in North America, unconventional resource markets are emerging on every continent. In the next eight to ten years, more than 100,000 wells and one- to two-million hydraulic fracturing stages could be executed, resulting in close to one trillion dollars in industry spending. This growth has prompted professionals experienced in conventional oil and gas exploitation and development to acquire practical knowledge of the unconventional realm. Unconventional Oil and Gas Resources: Exploitation and Development provides a comprehensive understanding of the latest advances in the exploitation and development of unconventional resources. With an emphasis on shale, this book: Addresses all aspects of the exploitation and development process, from data mining and accounting to drilling, completion, stimulation, production, and environmental issues Offers in-depth coverage of sub-surface measurements (geological, geophysical, petrophysical, geochemical, and geomechanical) and their interpretation Discusses the use of microseismic, fiber optic, and tracer reservoir monitoring technologies and JewelSuite (TM) reservoir modeling software Presents the viewpoints of internationally respected experts and researchers from leading exploration and production (E&P) companies and academic institutions Explores future trends in reservoir technologies for unconventional resources development Unconventional Oil and Gas Resources: Exploitation and Development aids geologists, geophysicists, petrophysicists, geomechanic specialists, and drilling, completion, stimulation, production, and reservoir engineers in the environmentally safe exploitation and development of unconventional resources like shale.

Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key Features Understand the fundamental concepts of exploratory data analysis using Python Find missing values in your data and identify the correlation between different variables Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package Book DescriptionExploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You'll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You'll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you'll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you'll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you'll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learn Import, clean, and explore data to perform preliminary analysis using powerful Python packages Identify and transform erroneous data using different data wrangling techniques Explore the use of multiple regression to describe non-linear relationships Discover hypothesis testing and explore techniques of time-series analysis Understand and interpret results obtained from graphical analysis Build, train, and optimize predictive models to estimate results Perform complex EDA techniques on open source datasets Who this book is forThis EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.