Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications

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
	Index
		A
		B
		C
		D
		E
		F
		G
		H
		I
		J
		K
		L
		M
		N
		O
		P
		Q
		R
		S
		T
		U
		V
		W
		Y
		Z