Machine learning guide for oil and gas using Python : ǂa ǂstep-by-step breakdown with data, algorithms, codes, and applications

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 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 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.