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

Description

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. 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