Description

Module 1: Integrated Production Modeling

• Integrated Production System

• Types & Values of Petroleum Engineering Data

• Field Development Stages

• Overall System Approach

• Reservoir to Surface Integrated Modeling and Optimization

• Reservoir Fluid and Rock Systems

• Inflow Performance (IPR) Models

• Vertical Lift (VLP) Performance Models

• System Nodal Analysis


Module 2: Reservoir Fluids & PVT

• Applications of PVT Data

• Hydrocarbon Phase Behavior

• Classifications of the Reservoir Fluids

• Analysis of Reservoir Fluid Properties

• PVT Laboratory Experiments and Fluid Studies

• Prediction of PVT Data using Machine Learning Techniques


Module 3: Reservoir Rocks & Core Analysis

• Objectives of Core Analysis and Coring Methods

• Core Analysis Workflow

• Screening, Sampling, and Preparation

• Permeability Measurement Techniques

• Conventional Core Data Analysis

• Special Core Data Analysis

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


Module 4: Production Data Analysis & Reservoir Surveillance

• Reservoir Performance Analysis

• Determination of Remaining Reserves

• Water Breakthrough Diagnostics

• Water Injection Diagnostics

• Production Analysis Dashboards

• Optimization of Surface Network Models using Machine Learning

• Case Studies and Examples


Module 5: Machine Learning and Computing

• Introduction to Machine Learning vs. Traditional Computing

• Evolutionary History of Computers and the AI Revolution

• Features and Impact of the AI Revolution on Petroleum Engineering

• Historical Overview of Programming Languages and Their Significance




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

• Case Studies of Some Machine Learning Projects in Various Petroleum Engineering Aspects.

• Generalized Machine Learning Workflows to Apply Machine Learning Techniques in Petroleum Engineering

• Supervised vs. Unsupervised Learning Techniques




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

• Introduction to 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 and 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.


Demo Class

2 Chapter
Prediction of Lithology While Drilling From Basic Drilling Parameters
Prediction of Lithology While Drilling From Basic Drilling Parameters
This Scale Prediction Dashboard offers a comprehensive suite of four predictive machine learning models (SVM, KNN, Logistic Regression, Decision Tree)
This Scale Prediction Dashboard offers a comprehensive suite of four predictive machine learning models (SVM, KNN, Logistic Regression, Decision Tree)

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