Machine Learning Guide for Oil and Gas Using Python
A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications
- 1st Edition - April 9, 2021
- Authors: Hoss Belyadi, Alireza Haghighat
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 9 2 9 - 4
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 1 9 3 0 - 0
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 h… Read more
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Request a sales quoteMachine 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
Petroleum engineers; data scientists; reservoir engineers; production engineers; completion engineers; drilling engineers; data engineers; data enthusiasts; geologists; technical advisors
- Cover image
- Title page
- Table of Contents
- Copyright
- Biography
- Acknowledgment
- Chapter 1. Introduction to machine learning and Python
- Introduction
- Artificial intelligence
- Data mining
- Machine learning
- Python crash course
- Anaconda introduction
- Anaconda installation
- Jupyter Notebook interface options
- Basic math operations
- Assigning a variable name
- Creating a string
- Defining a list
- Creating a nested list
- Creating a dictionary
- Creating a tuple
- Creating a set
- If statements
- For loop
- Nested loops
- List comprehension
- Defining a function
- Introduction to pandas
- Dropping rows or columns in a data frame
- loc and iloc
- Conditional selection
- Pandas groupby
- Pandas data frame concatenation
- Pandas merging
- Pandas joining
- Pandas operation
- Pandas lambda expressions
- Dealing with missing values in pandas
- Dropping NAs
- Filling NAs
- Numpy introduction
- Random number generation using numpy
- Numpy indexing and selection
- Chapter 2. Data import and visualization
- Data import and export using pandas
- Data visualization
- Chapter 3. Machine learning workflows and types
- Introduction
- Machine learning workflows
- Machine learning types
- Dimensionality reduction
- Chapter 4. Unsupervised machine learning: clustering algorithms
- Introduction to unsupervised machine learning
- K-means clustering
- Hierarchical clustering
- Density-based spatial clustering of applications with noise (DBSCAN)
- Important notes about clustering
- Outlier detection
- Local outlier factor using scikit-learn
- Chapter 5. Supervised learning
- Overview
- Linear regression
- Logistic regression
- Metrics for classification model evaluation
- Logistic regression using scikit-learn
- K-nearest neighbor
- Support vector machine
- Decision tree
- Random forest
- Extra trees (extremely randomized trees)
- Gradient boosting
- Extreme gradient boosting
- Adaptive gradient boosting
- Frac intensity classification example
- Handling missing data (imputation techniques)
- Rate of penetration (ROP) optimization example
- Chapter 6. Neural networks and Deep Learning
- Introduction and basic architecture of neural network
- Backpropagation technique
- Data partitioning
- Neural network applications in oil and gas industry
- Example 1: estimated ultimate recovery prediction in shale reservoirs
- Example 2: develop PVT correlation for crude oils
- Deep learning
- Convolutional neural network (CNN)
- Convolution
- Activation function
- Pooling layer
- Fully connected layers
- Recurrent neural networks
- Deep learning applications in oil and gas industry
- Frac treating pressure prediction using LSTM
- Chapter 7. Model evaluation
- Evaluation metrics and scoring
- Cross-validation
- Grid search and model selection
- Partial dependence plots
- Size of training set
- Save-load models
- Chapter 8. Fuzzy logic
- Classical set theory
- Fuzzy set
- Fuzzy inference system
- Fuzzy C-means clustering
- Chapter 9. Evolutionary optimization
- Genetic algorithm
- Particle swarm optimization
- Index
- No. of pages: 476
- Language: English
- Edition: 1
- Published: April 9, 2021
- Imprint: Gulf Professional Publishing
- Paperback ISBN: 9780128219294
- eBook ISBN: 9780128219300
HB
Hoss Belyadi
Hoss Belyadi is the founder and CEO of Obsertelligence, LLC, focused on providing artificial intelligence (AI) in-house training and solutions. As an adjunct faculty member at multiple universities, including West Virginia University, Marietta College, and Saint Francis University, Mr. Belyadi taught data analytics, natural gas engineering, enhanced oil recovery, and hydraulic fracture stimulation design. With over 10 years of experience working in various conventional and unconventional reservoirs across the world, he works on diverse machine learning projects and holds short courses across various universities, organizations, and the department of energy (DOE). Mr. Belyadi is the primary author of Hydraulic Fracturing in Unconventional Reservoirs (first and second editions) and is the author of Machine Learning Guide for Oil and Gas Using Python. Hoss earned his BS and MS, both in petroleum and natural gas engineering from West Virginia University.
Affiliations and expertise
Founder and CEO, Obsertelligence, LLC, PA, USAAH
Alireza Haghighat
Dr. Alireza Haghighat is a senior technical advisor and instructor for Engineering Solutions at IHS Markit, focusing on reservoir/production engineering and data analytics. Prior to joining IHS, he was a senior reservoir engineer at Eclipse/Montage resources for nearly five years. As a reservoir engineer, he was involved in well performance evaluation with data analytics, rate transient analysis of unconventional assets (Utica and Marcellus), asset development, hydraulic fracture/reservoir simulation, DFIT analysis, and reserve evaluation. He has been an adjunct faculty member at Pennsylvania State University (PSU) for the past 5 years, teaching courses in Petroleum Engineering/Energy, Business and Finance departments. Dr. Haghighat has published several technical papers and book chapters on machine learning applications in smart wells, CO2 sequestration modeling, and production analysis of unconventional reservoirs. He has received his PhD in petroleum and natural gas engineering from West Virginia University and a master’s degree in petroleum engineering from Delft University of Technology.
Affiliations and expertise
Senior Technical Advisor and Instructor for Engineering Solutions, IHS Markit, TX, USARead Machine Learning Guide for Oil and Gas Using Python on ScienceDirect