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

lgli/Hoss Belyadi, Alireza Haghighat - Machine Learning Guide for Oil and Gas Using Python_ A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications-Gulf Professional Publishing (2021)-min.pdf

lgrsnf/Hoss Belyadi, Alireza Haghighat - Machine Learning Guide for Oil and Gas Using Python_ A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications-Gulf Professional Publishing (2021)-min.pdf

zlib/Computers/Cybernetics/Hoss Belyadi, Alireza Haghighat/Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications_24593074.pdf

Введение в машинное обучение с помощью Python: руководство для специалистов по работе с данными: [полноцветное издание]

Machine learning with Python cookbook : practical solutions from preprocessing to deep learning

Introduction to Machine Learning with Python : A Guide for Data Scientists

Машинное обучение с использованием Python. Сборник рецептов

Андреас Мюллер, Сара Гвидо; [перевод с английского и редакция А. В. Груздева]

Крис Элбон; перевод с английского А. Логунова

Belyadi, Hoss, Haghighat, Alireza

Andreas C. Mueller, Sarah Guido

Andreas C. Müller; Sarah Guido

Müller, Andreas, Guido, Sarah

Мюллер, Андреас

Albon, Chris

Chris Albon

Элбон, Крис

Gulf Professional Publishing, an imprint of Elsevier

O'Reilly Media; O'Reilly Media, Inc.

Elsevier Science & Technology Books

Academic Press, Incorporated

O'Reilly Media, Incorporated

Morgan Kaufmann Publishers

БХВ-Петербург

Brooks/Cole

Диалектика

First edition, Beijing Boston Farnham Sebastopol Tokyo, 2018

First edition, third release, Sebastopol, CA, 2017

Kidlington ; Cambridge (Mass.), cop. 2021

United States, United States of America

O'Reilly Media, Sebastopol, CA, 2017

First edition, Beijing, [China, 2018

First edition, Sebastopol, CA, 2016

First edition, Sebastopol, CA, 2018

Санкт-Петербург, Russia, 2022

First edition, Beijing, 2016

Москва [и др.], Russia, 2017

September 25, 2016

Apr 01, 2018

1, FR, 2016

1, PS, 2018

1, PS, 2021

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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

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you'll learn:
Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models

With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book. -- Provided by Publisher

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 Is Utilization In Various Oil And Gas Scenarios, Such As Well Testing, Shale Reservoirs And Production Optimization. While Similar Resources Are Often Too Mathematical, This Book Balances Theory With Applications, Including Use Cases That Help Solve Different 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

Книга содержит около 200 рецептов решения практических задач машинного обучения, таких как загрузка и обработка текстовых или числовых данных, отбор модели, уменьшение размерности и многие другие. Рассмотрена работа с языком Python и его библиотеками, в том числе pandas и scikit-learn. Решения всех задач сопровождаются подробными объяснениями. Каждый рецепт содержит работающий программный код, который можно вставлять, объединять и адаптировать, создавая собственное приложение. Приведены рецепты решений с использованием: векторов, матриц и массивов; обработки данных, текста, изображений, дат и времени; уменьшения размерности и методов выделения или отбора признаков; оценивания и отбора моделей; линейной и логистической регрессии, деревьев, лесов и к ближайших соседей; опорно-векторных машин (SVM), наивных байесовых классификаторов, кластеризации и нейронных сетей; сохранения и загрузки натренированных моделей

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. -- Provided by publisher

Vectors, Matrices, And Arrays -- Loading Data -- Data Wrangling -- Handling Numerical Data -- Handling Categorical Data -- Handling Text -- Handling Dates And Times -- Handling Images -- Dimensionalit Reduction Using Feature Extraction -- Dimensionality Reduction Using Feature Selection -- Model Evaluation -- Model Selection -- Linear Regression -- Trees And Forests -- K-nearest Neighbors -- Logistic Regression -- Support Vector Machines -- Naive Bayes -- Clustering -- Neural Networks -- Saving And Loading Trained Models. Chris Albon. Includes Index.

2023-03-04