Practice Machine Learning for Petroleum Engineers with Little to No Code

Overview

Explore machine learning applications in petroleum engineering with minimal coding. Gain practical skills for implementing ML solutions in reservoir analysis and production optimization.

Syllabus

• Introduction to Machine Learning
Overview of machine learning concepts
Importance in petroleum engineering
Brief on no-code and low-code platforms
• Data Preprocessing and Management
Types of data in petroleum engineering
Data cleaning techniques
Handling missing data
Introduction to data visualization tools (e.g., Tableau, Power BI)
• Machine Learning Tools and Platforms
Overview of no-code/low-code platforms (e.g., DataRobot, H2O.ai)
Setting up a machine learning environment
• Supervised Learning Techniques in Petroleum Engineering
Intro to regression methods
Classification algorithms for production data
Case studies: Predicting reservoir properties
• Unsupervised Learning Techniques
Clustering for reservoir characterization
Dimensionality reduction for complex data sets
Anomaly detection in sensor data
• Machine Learning in Reservoir Analysis
Historical data analysis for reservoir performance
Forecasting with time series data
Visualizing model results for decision making
• Applications in Production Optimization
Identifying inefficiencies in production processes
Predictive maintenance using ML
Real-time monitoring and adjustments
• Hands-On Project
Define a project related to reservoir analysis or production optimization
Implement a solution using no-code/low-code tools
Present findings and insights
• Ethical Considerations and Best Practices
Data privacy and security in petroleum applications
Ensuring model transparency and interpretability
Avoiding bias in machine learning models
• Course Wrap-Up and Future Directions
Recap of key learnings
Advancing skills through further study
Discussion on emerging trends in AI and ML for petroleum engineering

Subjects

Data Science