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

nexusstc/Machine Learning Guide for Oil and Gas Using Python/049128919a3796c7eb09e92b51ab1da7.pdf

lgrsnf/Machine Learning Guide for Oil and Gas Using Python.pdf

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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

Elsevier

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

lg3003802

producers:
Acrobat Distiller 8.1.0 (Windows)

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Machine Learning Guide for Oil and Gas Using Python 3. 10.1016/B978-0-12-821929-4.01001-5
Front-Matter_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python 1
Machine Learning Guide for Oil and Gas Using Python 1
Copyright_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python 2
Copyright 2
Biography_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python 3
Biography 3
Acknowledgment_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python 4
Acknowledgment 4
Chapter-1---Introduction-to-machine-l_2021_Machine-Learning-Guide-for-Oil-an 5
1 - Introduction to machine learning and Python 5
Introduction 5
Artificial intelligence 6
Data mining 6
Machine learning 7
Python crash course 7
Anaconda introduction 7
Anaconda installation 7
Jupyter Notebook interface options 9
Basic math operations 10
Assigning a variable name 11
Creating a string 11
Defining a list 12
Creating a nested list 13
Creating a dictionary 14
Creating a tuple 15
Creating a set 15
If statements 16
For loop 17
Nested loops 18
List comprehension 19
Defining a function 20
Introduction to pandas 20
Dropping rows or columns in a data frame 27
loc and iloc 27
Conditional selection 30
Pandas groupby 33
Pandas data frame concatenation 35
Pandas merging 38
Pandas joining 42
Pandas operation 44
Pandas lambda expressions 46
Dealing with missing values in pandas 48
Dropping NAs 48
Filling NAs 50
Numpy introduction 52
Random number generation using numpy 55
Numpy indexing and selection 57
Reference 59
Chapter-2---Data-import-and-visu_2021_Machine-Learning-Guide-for-Oil-and-Gas 60
2 - Data import and visualization 60
Data import and export using pandas 60
Data visualization 62
Matplotlib library 63
Well log plotting using matplotlib 71
Seaborn library 73
Distribution plots 75
Joint plots 77
Pair plots 78
lmplots 79
Bar plots 80
Count plots 82
Box plots 82
Violin and swarm plots 83
KDE plots 85
Heat maps 86
Cluster maps 87
PairGrid plots 88
Plotly and cufflinks 89
References 98
Chapter-3---Machine-learning-workf_2021_Machine-Learning-Guide-for-Oil-and-G 99
3 - Machine learning workflows and types 99
Introduction 99
Machine learning workflows 99
Data gathering and integration 99
Cloud vs. edge computing 101
Data cleaning 102
Feature ranking and selection 103
Scaling, normalization, or standardization 104
Cross-validation 106
Blind set validation 108
Bias–variance trade-off 109
Model development and integration 109
Machine learning types 110
Supervised learning 110
Unsupervised learning 111
Semi-supervised learning 112
Reinforcement learning 112
Dimensionality reduction 113
Principal component analysis (PCA) 113
PCA using scikit-learn library 116
Nonnegative matrix factorization (NMF) 121
Nonnegative matrix factorization using scikit-learn 122
References 125
Chapter-4---Unsupervised-machine-learni_2021_Machine-Learning-Guide-for-Oil- 126
4 - Unsupervised machine learning: clustering algorithms 126
Introduction to unsupervised machine learning 126
K-means clustering 127
How does K-means clustering work? 129
K-means clustering application using the scikit-learn library 131
K-means clustering application: manual calculation example 139
Silhouette coefficient 140
Silhouette coefficient in the scikit-learn library 140
Hierarchical clustering 141
Dendrogram 144
Implementing dendrogram and hierarchical clustering in scikit-learn library 145
Density-based spatial clustering of applications with noise (DBSCAN) 150
How does DBSCAN work? 152
DBSCAN implementation and example in scikit-learn library 153
Important notes about clustering 158
Outlier detection 159
Isolation forest 159
Isolation forest using scikit-learn 160
Local outlier factor (LOF) 165
Local outlier factor using scikit-learn 166
References 169
Chapter-5---Supervised-lear_2021_Machine-Learning-Guide-for-Oil-and-Gas-Usin 170
5 - Supervised learning 170
Overview 170
Linear regression 170
Regression evaluation metrics 171
Application of multilinear regression model in scikit-learn 172
One-variable-at-a-time sensitivity analysis 182
Logistic regression 187
Metrics for classification model evaluation 188
Logistic regression using scikit-learn 190
K-nearest neighbor 198
KNN implementation using scikit-learn 200
Support vector machine 204
Support vector machine implementation in scikit-learn 207
Decision tree 215
Attribute selection technique 216
Decision tree using scikit-learn 219
Random forest 229
Random forest implementation using scikit-learn 230
Extra trees (extremely randomized trees) 234
Extra trees implementation using scikit-learn 235
Gradient boosting 240
Gradient boosting implementation using scikit-learn 240
Extreme gradient boosting 251
Extreme gradient boosting implementation using scikit-learn 251
Adaptive gradient boosting 255
Adaptive gradient boosting implementation using scikit-learn 256
Frac intensity classification example 259
Support vector machine classification model 263
Random forest classification model 266
Extra trees classification model 268
Gradient boosting classification model 270
Extreme gradient boosting classification model 272
Handling missing data (imputation techniques) 275
Multivariate imputation by chained equations 276
Fancy impute implementation in Python 276
Rate of penetration (ROP) optimization example 282
References 296
Chapter-6---Neural-networks-and-D_2021_Machine-Learning-Guide-for-Oil-and-Ga 297
6 - Neural networks and Deep Learning 297
Introduction and basic architecture of neural network 297
Backpropagation technique 301
Data partitioning 303
Neural network applications in oil and gas industry 305
Example 1: estimated ultimate recovery prediction in shale reservoirs 307
Descriptive statistics 307
Date preprocessing 308
Neural network training 313
Example 2: develop PVT correlation for crude oils 318
Deep learning 379
Convolutional neural network (CNN) 326
Convolution 229
Activation function 328
Pooling layer 328
Fully connected layers 329
Recurrent neural networks 330
Deep learning applications in oil and gas industry 333
Frac treating pressure prediction using LSTM 335
Nomenclature 345
References 345
Further reading 347
Chapter-7---Model-evaluat_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using- 348
7 - Model evaluation 348
Evaluation metrics and scoring 348
Binary classification: prediction of sand production 348
Multiclass classification: facies classification 353
Evaluation metrics for regression problems 139
Cross-validation 358
Cross-validation for classification 359
Cross-validation for regression 360
Stratified K-fold cross-validation 361
Grid search and model selection 363
Grid search for hyperparameter optimization 363
Model selection 370
Partial dependence plots 372
Size of training set 376
Save-load models 378
References 379
Chapter-8---Fuzzy-logi_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Pyt 380
8 - Fuzzy logic 380
Classical set theory 170
Set operations 383
Set properties 383
Fuzzy set 386
Definition 386
Mathematical function 387
Membership functions type 388
Fuzzy set operations 391
Fuzzy inference system 393
Input fuzzification 393
Fuzzy rules 394
Inference 396
Defuzzification 398
Fuzzy inference example: choke adjustment 399
Fuzzy C-means clustering 305
References 190
Chapter-9---Evolutionary-optim_2021_Machine-Learning-Guide-for-Oil-and-Gas-U 418
9 - Evolutionary optimization 418
Genetic algorithm 419
Genetic algorithm workflow 420
Genetic algorithm example: EUR optimization 432
Particle swarm optimization 358
Particle swarm optimization theory 439
NPV maximization example 445
References 451
Index_2021_Machine-Learning-Guide-for-Oil-and-Gas-Using-Python 454
Index 454
A 454
B 454
C 454
D 454
E 455
F 455
G 456
H 456
I 456
J 457
K 457
L 457
M 457
N 458
O 458
P 458
Q 459
R 459
S 459
T 460
U 460
V 461
W 461
Y 461
Z 461

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.

2021-05-06