Machine Learning Guide for Oil and Gas Using Python

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. 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