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.
Author(s): Hoss Belyadi, Alireza Haghighat
Publisher: Gulf Professional Publishing
Year: 2021
Language: English
Pages: 476
City: Cambridge
Front-Matter_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python
Machine Learning Guide for Oil and Gas Using Python
Copyright_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python
Copyright
Biography_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python
Biography
Acknowledgment_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python
Acknowledgment
Chapter-1---Introduction-to-machine-l_2021_Machine-Learning-Guide-for-Oil-an
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
Reference
Chapter-2---Data-import-and-visu_2021_Machine-Learning-Guide-for-Oil-and-Gas
2 -
Data import and visualization
Data import and export using pandas
Data visualization
Matplotlib library
Well log plotting using matplotlib
Seaborn library
Distribution plots
Joint plots
Pair plots
lmplots
Bar plots
Count plots
Box plots
Violin and swarm plots
KDE plots
Heat maps
Cluster maps
PairGrid plots
Plotly and cufflinks
References
Chapter-3---Machine-learning-workf_2021_Machine-Learning-Guide-for-Oil-and-G
3 -
Machine learning workflows and types
Introduction
Machine learning workflows
Data gathering and integration
Cloud vs. edge computing
Data cleaning
Feature ranking and selection
Scaling, normalization, or standardization
Cross-validation
Blind set validation
Bias–variance trade-off
Model development and integration
Machine learning types
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Dimensionality reduction
Principal component analysis (PCA)
PCA using scikit-learn library
Nonnegative matrix factorization (NMF)
Nonnegative matrix factorization using scikit-learn
References
Chapter-4---Unsupervised-machine-learni_2021_Machine-Learning-Guide-for-Oil-
4 -
Unsupervised machine learning: clustering algorithms
Introduction to unsupervised machine learning
K-means clustering
How does K-means clustering work?
K-means clustering application using the scikit-learn library
K-means clustering application: manual calculation example
Silhouette coefficient
Silhouette coefficient in the scikit-learn library
Hierarchical clustering
Dendrogram
Implementing dendrogram and hierarchical clustering in scikit-learn library
Density-based spatial clustering of applications with noise (DBSCAN)
How does DBSCAN work?
DBSCAN implementation and example in scikit-learn library
Important notes about clustering
Outlier detection
Isolation forest
Isolation forest using scikit-learn
Local outlier factor (LOF)
Local outlier factor using scikit-learn
References
Chapter-5---Supervised-lear_2021_Machine-Learning-Guide-for-Oil-and-Gas-Usin
5 -
Supervised learning
Overview
Linear regression
Regression evaluation metrics
Application of multilinear regression model in scikit-learn
One-variable-at-a-time sensitivity analysis
Logistic regression
Metrics for classification model evaluation
Logistic regression using scikit-learn
K-nearest neighbor
KNN implementation using scikit-learn
Support vector machine
Support vector machine implementation in scikit-learn
Decision tree
Attribute selection technique
Decision tree using scikit-learn
Random forest
Random forest implementation using scikit-learn
Extra trees (extremely randomized trees)
Extra trees implementation using scikit-learn
Gradient boosting
Gradient boosting implementation using scikit-learn
Extreme gradient boosting
Extreme gradient boosting implementation using scikit-learn
Adaptive gradient boosting
Adaptive gradient boosting implementation using scikit-learn
Frac intensity classification example
Support vector machine classification model
Random forest classification model
Extra trees classification model
Gradient boosting classification model
Extreme gradient boosting classification model
Handling missing data (imputation techniques)
Multivariate imputation by chained equations
Fancy impute implementation in Python
Rate of penetration (ROP) optimization example
References
Chapter-6---Neural-networks-and-D_2021_Machine-Learning-Guide-for-Oil-and-Ga
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
Descriptive statistics
Date preprocessing
Neural network training
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
Nomenclature
References
Further reading
Chapter-7---Model-evaluat_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-
7 -
Model evaluation
Evaluation metrics and scoring
Binary classification: prediction of sand production
Multiclass classification: facies classification
Evaluation metrics for regression problems
Cross-validation
Cross-validation for classification
Cross-validation for regression
Stratified K-fold cross-validation
Grid search and model selection
Grid search for hyperparameter optimization
Model selection
Partial dependence plots
Size of training set
Save-load models
References
Chapter-8---Fuzzy-logi_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Pyt
8 -
Fuzzy logic
Classical set theory
Set operations
Set properties
Fuzzy set
Definition
Mathematical function
Membership functions type
Fuzzy set operations
Fuzzy inference system
Input fuzzification
Fuzzy rules
Inference
Defuzzification
Fuzzy inference example: choke adjustment
Fuzzy C-means clustering
References
Chapter-9---Evolutionary-optim_2021_Machine-Learning-Guide-for-Oil-and-Gas-U
9 -
Evolutionary optimization
Genetic algorithm
Genetic algorithm workflow
Genetic algorithm example: EUR optimization
Particle swarm optimization
Particle swarm optimization theory
NPV maximization example
References
Index_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python
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