Machine Learning Guide for Oil and Gas Using Python 9780128219294, 9781491989388, 1449369413

Table of contents :
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

Decisio

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