Module 1: Petroleum Engineering Data

Integrated Production System

Types of Petroleum Engineering Data 

Introduction to Python for Oil & Gas Industry

Introduction to Python

Python Tools & Package Options

Anaconda Navigator

Python IDEs (VS Code, Spyder, Jupyter Notebook, etc.)

Data Types & Exploratory Data Analysis

Data Pre-processing

Python Data Types & Basic Python Functions

Variables, Expressions, and Statements

Values and Data Types

Statements and Expressions, Operators

Lists, Indexing & Slicing

Data container (Lists and Dictionaries)

Operation on Containers

Introduction to Loops

Introduction to Branching (IF Statements)

Complex Data Structures & Control Flow

Dictionaries

Conditional Statements

For Loops

Creating Functions

Module 2: Python Data Analysis & Visualizations

Data Manipulation using Numpy and Pandas

Data Analysis & Visualizations

NumPy 

Panda 

Matplotlib 

Scipy 

Seaborn

Descriptive Statistics

Methods to impute missing values

Outlier/Anomaly Detection

Data Visualization

Histogram

Bar Graph

Scatter Plot

Pie Chart

Box Plot

Python Machine Learning Libraries

Scikit-learn Machine Learning Algorithms.

Workflow of the Machine Learning Model

Deploy Machine Learning Models as Interactive APIs.

Supervised and Unsupervised Learning

Model Evaluation and Validation

Classification and Regression Algorithms

Unsupervised Learning

K-Mean Clustering using Scikit-learn

Clustering Oil wells based on petrophysical properties

Clustering Gas Wells Based on Liquid Loading Index

K Nearest Neighbors

Random Initialization

Choosing the Number of Clusters

Hierarchal clustering and Dendrogram

Hierarchal Cluster for Water Cut 

Correlation matrix and Heat map

Under-fitting vs Over-fitting

Bias-Variance Trade-off

Classification Techniques

Logistic Regression

Support Vector Machines (SVM)

Decision Tree

Random Forest 

Module 3: Supervised Learning

Supervised Learning

Neural Network Architecture and Components

Activation Functions in Neural Networks

Backpropagation and Optimization Algorithms

Hydrocarbon Phase Behavior

Analysis of Reservoir Fluid Properties

Prediction of PVT Data using Machine Learning Techniques

Permeability Measurement Techniques

Permeability Prediction Techniques

Applications of Artificial Neural Networks for Core-Calibrated Permeability Predictions using the Petrophysical Data.

Module 4: Production Data Analysis & Reservoir Surveillance

Time Series Analysis with AI

Time Series Data and Its Characteristics

Long Short-Term Memory (LSTM) Networks for Time Series

Evaluating Time Series Models

Production Analysis Dashboards

Optimization of Surface Network Models using Machine Learning

Case Studies and Examples

Streamlit Library

Designing Dynamic Python Applications with Streamlit 

Interactive Web Applications & Dashboards

Enhancing User Experience with Streamlit Layout Features

State Management and Dynamic Interactions with Streamlit

Module 5: Machine Learning and Computing

Introduction to Machine Learning vs. Traditional Computing

Impact of the AI Revolution on Petroleum Engineering

Deep Learning Python Libraries Tensorflow and PyTorch.

Module 6: Applications of Machine Learning in the Petroleum Industry

Artificial Neural Networks: Definitions, Architectures, Types, Training, and Validation.

Overview of Machine Learning Applications in the Petroleum Industry

Machine Learning Projects in Various Petroleum Engineering Aspects

Machine Learning Workflows

Supervised vs. Unsupervised Learning Techniques

Module 7: Programming Tools and Basics & Python Fundamentals and Dashboard Creation

Useful Tools for Efficient Coding

Setting Up Your Development Environment: Anaconda Distribution

Introduction to Python Programming Basics

Hands-on Practice: Python Basics using Jupyter Notebook

Recap of Python Fundamentals

Python Libraries for E&P

Visualizing and Presenting Data Insights

Python Project 1: Create Dashboard for Nodal Analysis & Vertical Lift Performance (VLP) Calculations

Module 8: Data Analysis & Transformation

Hands-on Project: Data Analysis with Real Petroleum Industry Data

Handling Data from Various Sources: Files and Formats

Data Cleaning and Preprocessing Techniques

Estimating Missing Data and Data Transformation Methods

Machine Learning Project 2: Drilling Data Optimization

Machine Learning Project 3: Pump Intake Pressure Estimation using Regression Techniques

Machine Learning Project 4: Flow Rate Estimation from Pump Intake Pressure

Simple Model Dashboard: Create simple data input dashboard uses the trained model and generates the outputs.

Module 9: Machine Learning Projects (Regression)

Introduction to Regression Machine Learning Methods.

Machine Learning Project 5: Learning for Oil Properties Estimation: Oil FVF or Viscosity

Classification Machine Learning Methods.

Machine Learning Project 6: Decline Curve Analysis (DCA) & prediction.

Machine Learning Project 7: Well Logging Analysis Dashboard & Core-calibrated Permeability Predictions.

Module 10: Machine Learning Projects (Classification) & Time Series Analysis

Machine Learning Project 8: Rock Typing & Hydraulic Flow Units (HFU) Prediction.

Machine Learning Project 9: Flow Assurance & Scale Formation Prediction using Classification Techniques

Machine Learning Project 10: Predicting Production Performance using Time series.