Course Overview:
Embark on a transformative journey into the cutting-edge realm of Reservoir Analytics and Machine Learning tailored specifically for the Oil & Gas industry. Explore Machine Learning for PVT analysis and reservoir monitoring. Predict waterflood performance and optimize patterns with advanced methodologies. This comprehensive course offers an in-depth exploration of Python applications in Reservoir Analytics, equipping participants with skills crucial for enhancing reservoir management efficiency

Duration: 20+ Hours

Who Should Attend?
If you're a recent graduate, early-career professional, or someone transitioning into this exciting domain, this program provides a solid foundation. Gain practical skills, hands-on experience, and a comprehensive understanding that will empower you in your early steps toward a successful career in Oil & Gas reservoir management and analytics

Benefits Beyond the Classroom: Why Attend?

• Industry-Relevant Expertise
• Career Advancement Opportunities
• Gain Oil & Gas-Specific Skillset
• Connect with industry professionals, fostering collaborations and expanding your network
• Stay Ahead in Oil & Gas Innovations

Key Learning Objectives: What You'll Achieve

• Master Python for Oil & Gas Reservoir Analytics.
• Advance in Reservoir Production Analysis
• Develop Data-Driven Reserve Evaluation Skills for Oil & Gas Assets.
• Became master to apply Python for Fluid Properties Analysis
• Implement Machine Learning for Reservoir Monitoring in Oil & Gas.
• Excel in Waterflood Optimization Techniques for Oil & Gas.
• Create Customized Dashboards for Informed Decision-Making

Topics to be covered:

1.  Python in Reservoir Analytics

• Relevant Python Libraries for Reservoir Analytics
• Reservoir Production Time Series Analysis
• Saturation Profile Visualization
• Interactive 3D Horizon Surfaces
• 3D Reservoir Grid Construction with Python

2.  Data-driven Reserve Evaluation techniques

• Estimating OOIP and EUR.
• Monte Carlo Simulation with Python
• Predicting EUR for Shale Reservoirs.
•  Sweet-Spot Quality Index (SSQI) for Shale Reservoirs

3.  Enhancing PVT and Fluid Properties Analysis with Machine Learning

• Python libraries for fluid characterization
• Phase envelopes with Python
• Intelligent models for estimating Formation Volume factor etc.
• Predicting PVT properties from compositional data with Machine learning

4.  Machine Learning-assisted Reservoir Monitoring and Surveillance

• Predicting Static BHP with ML
• Predicting Flowing BHP with ML
• Automated Detection of Reservoir Performance Anomaly.
• Reservoir Performance Monitoring Dashboard.

5.  Waterflood Optimization with Machine Learning

• Waterflood performance prediction with ML
• Waterflood Pattern Optimization with ML.