Master Oil & Gas Production Optimization with AI & Machine Learning
Course Objective
This course equips participants with practical skills to apply Machine Learning (ML) techniques for production forecasting and optimization in the Oil & Gas sector. Participants will gain hands-on experience with time series forecasting, well performance optimization, and anomaly detection using statistical and deep learning methods.
Learning Objectives
- Understand ML fundamentals and their application in Oil & Gas production.
- Visualize and analyze time series data using statistical techniques.
- Build ARIMA and LSTM models for production forecasting.
- Apply ML algorithms for gas lift and choke optimization.
- Detect anomalies using advanced ML techniques.
Prerequisites
- No prior ML knowledge required – starts from scratch.
- Basic understanding of data visualization is a plus.
Topics Covered
Module 1: Introduction to ML & Time Series
- ML in Oil & Gas
- Time Series vs Normal Data
- Supervised vs Unsupervised Learning
Module 2: Math & Statistics
- Algebra, Calculus, Hypothesis Testing
- Lag Features, Differencing, Outlier Removal
- Seasonality, Trend, White Noise
Module 3: Time Series Visualization
- Time Plots, Smoothing Techniques
- Fourier Transforms, Recursion Plots
- Signal-based Anomaly Detection
Module 4: Forecasting Techniques
- AR, MA, ARIMA Models
- Decline Curve Analysis
- LSTM for Pressure Forecasting
Module 5: ML for Production Optimization
- Well Performance Optimization
- Sand Production, Gas Lift & Choke Optimization
- Predicting Failures, Infill Well Planning
Module 6: Advanced Techniques & Case Studies
- Hydraulic Fracture Optimization
- Ensemble Methods, Auto Encoders
- Case Studies & Re-Frac Feasibility
- Best Practices & Future Trends
Benefits of Joining
- Gain hands-on experience in real-world ML applications.
- Boost career in data science and production optimization.
- Master advanced statistical & deep learning methods.
- Build a strong portfolio with capstone projects.
- Access recorded sessions and lifelong materials.
