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.
