Machine Learning Guide for Oil and Gas Using Python

Machine Learning Guide for Oil and Gas Using Python

by Hoss Belyadi, Alireza Haghighat
Released April 2021
Publisher(s): Gulf Professional Publishing
ISBN: 9780128219300

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Book description

Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.

  • Helps readers understand how open-source Python can be utilized in practical oil and gas challenges
  • Covers the most commonly used algorithms for both supervised and unsupervised learning
  • Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Biography
  6. Acknowledgment
  7. Chapter 1. Introduction to machine learning and Python
    1. Introduction
    2. Artificial intelligence
    3. Data mining
    4. Machine learning
    5. Python crash course
    6. Anaconda introduction
    7. Anaconda installation
    8. Jupyter Notebook interface options
    9. Basic math operations
    10. Assigning a variable name
    11. Creating a string
    12. Defining a list
    13. Creating a nested list
    14. Creating a dictionary
    15. Creating a tuple
    16. Creating a set
    17. If statements
    18. For loop
    19. Nested loops
    20. List comprehension
    21. Defining a function
    22. Introduction to pandas
    23. Dropping rows or columns in a data frame
    24. loc and iloc
    25. Conditional selection
    26. Pandas groupby
    27. Pandas data frame concatenation
    28. Pandas merging
    29. Pandas joining
    30. Pandas operation
    31. Pandas lambda expressions
    32. Dealing with missing values in pandas
    33. Dropping NAs
    34. Filling NAs
    35. Numpy introduction
    36. Random number generation using numpy
    37. Numpy indexing and selection
  8. Chapter 2. Data import and visualization
    1. Data import and export using pandas
    2. Data visualization
  9. Chapter 3. Machine learning workflows and types
    1. Introduction
    2. Machine learning workflows
    3. Machine learning types
    4. Dimensionality reduction
  10. Chapter 4. Unsupervised machine learning: clustering algorithms
    1. Introduction to unsupervised machine learning
    2. K-means clustering
    3. Hierarchical clustering
    4. Density-based spatial clustering of applications with noise (DBSCAN)
    5. Important notes about clustering
    6. Outlier detection
    7. Local outlier factor using scikit-learn
  11. Chapter 5. Supervised learning
    1. Overview
    2. Linear regression
    3. Logistic regression
    4. Metrics for classification model evaluation
    5. Logistic regression using scikit-learn
    6. K-nearest neighbor
    7. Support vector machine
    8. Decision tree
    9. Random forest
    10. Extra trees (extremely randomized trees)
    11. Gradient boosting
    12. Extreme gradient boosting
    13. Adaptive gradient boosting
    14. Frac intensity classification example
    15. Handling missing data (imputation techniques)
    16. Rate of penetration (ROP) optimization example
  12. Chapter 6. Neural networks and Deep Learning
    1. Introduction and basic architecture of neural network
    2. Backpropagation technique
    3. Data partitioning
    4. Neural network applications in oil and gas industry
    5. Example 1: estimated ultimate recovery prediction in shale reservoirs
    6. Example 2: develop PVT correlation for crude oils
    7. Deep learning
    8. Convolutional neural network (CNN)
    9. Convolution
    10. Activation function
    11. Pooling layer
    12. Fully connected layers
    13. Recurrent neural networks
    14. Deep learning applications in oil and gas industry
    15. Frac treating pressure prediction using LSTM
  13. Chapter 7. Model evaluation
    1. Evaluation metrics and scoring
    2. Cross-validation
    3. Grid search and model selection
    4. Partial dependence plots
    5. Size of training set
    6. Save-load models
  14. Chapter 8. Fuzzy logic
    1. Classical set theory
    2. Fuzzy set
    3. Fuzzy inference system
    4. Fuzzy C-means clustering
  15. Chapter 9. Evolutionary optimization
    1. Genetic algorithm
    2. Particle swarm optimization
  16. Index

Product information

  • Title: Machine Learning Guide for Oil and Gas Using Python
  • Author(s): Hoss Belyadi, Alireza Haghighat
  • Release date: April 2021
  • Publisher(s): Gulf Professional Publishing
  • ISBN: 9780128219300