Course Schedule

Machine Learning for Reservoir and Production Engineering

This course introduces reservoir and production engineers to the principles and applications of machine learning, enabling data-driven decisions for optimization and improved efficiency in oil and gas projects.

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Description

In the rapidly evolving landscape of oil and gas, data-driven techniques are transforming traditional approaches to reservoir and production engineering. This course delves into the fundamentals of machine learning, focusing on real-world applications tailored to the industry. Participants will learn to apply machine learning models to analyze complex reservoir and production data, streamline operations, and enhance decision-making processes. The training combines theoretical foundations with practical exercises, empowering engineers with the tools to leverage machine learning effectively in their daily tasks.

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Course Description

Introduction

The oil and gas industry generates vast amounts of data daily, from reservoir modeling to production monitoring. However, extracting meaningful insights from this data requires specialized knowledge in machine learning. This course is designed to bridge the gap, equipping reservoir and production engineers with essential machine learning skills to improve decision-making and operational efficiency.

Objectives

Understand the fundamentals of machine learning and its role in reservoir and production engineering.

Explore data-driven methods for optimizing reservoir management and production workflows.

Develop skills to preprocess, analyze, and model oil and gas data using machine learning techniques.

Learn to implement supervised and unsupervised machine learning models for various field applications.

Apply machine learning methods to enhance predictive capabilities for reservoir behavior and production performance.

Training Methodology

This course combines theoretical instruction with hands-on practice, using industry-relevant datasets to simulate real-world scenarios. Participants will work on guided exercises and case studies to reinforce learning and apply techniques directly to reservoir and production challenges.

Organisational Impact

Upon completing this course, organizations can expect their engineers to bring new skills and perspectives, leveraging machine learning to enhance operational efficiencies, optimize reservoir management, and improve production forecasting. This translates into better resource management and decision-making aligned with data-driven insights.

Personal Impact

Participants will gain a comprehensive understanding of machine learning fundamentals and hands-on experience in applying these techniques to reservoir and production challenges. This skill set enables engineers to drive data-informed decisions, boost their professional capabilities, and remain competitive in the industry.

Who Should Attend?

Petroleum / Reservoir / Production Engineers: engineers with a desire to automate processes, analyze large datasets, optimize operations, and make data-driven decisions using programming and machine learning techniques would be the primary audience. This includes engineers involved in reservoir modeling, production optimization, drilling engineering, and more.

Technical Managers: Managers overseeing technical teams could benefit from understanding the capabilities of Python and machine learning to guide decision-making and project planning in the petroleum industry.

Students and Researchers: Students studying petroleum engineering or related fields could use this course to enhance their technical skills and gain a competitive advantage in the job market. Researchers interested in applying machine learning to solve industry challenges could also benefit.

Course Outline

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

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• 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 & Dashborads

• 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.

Certificates

On successful completion of this training course, PEA Certificate will be awarded to the delegates

About The Trainer

Hesham Mokhtar is a Reservoir Engineering Team Leader at General Petroleum Company (GPC) in Egypt. He has extensive experience in reservoir management, characterization, and production optimization. He is skilled in using various software for reservoir simulation and analysis. He has also delivered training courses on waterflooding, PVT analysis, reservoir simulation, and more.