AI-based (Top-Down) Reservoir Simulation & Modeling Course
Numerical Reservoir Simulation that has been used in petroleum industry in the past century is a “Bottom-Up” Reservoir Modeling. An original geological model of the reservoir (Bottom) is developed by the geologists and is used by reservoir engineers to match the history of the hydrocarbon production (Up).
The AI-based Reservoir Simulation is a “Top-Down” Reservoir Modeling. The historical hydrocarbon production that includes all the surface related operational conditions (Top) is used for Geo-Analytics (AI-based geological modeling) to model the geology of the reservoir (Down) for the production forecasting and production optimization. The requirement of AI-based (Top-Down) Reservoir Simulation and Modeling is the combination of expertise in Reservoir Engineering and expertise in Artificial Intelligence.
AI-based (Top-Down) Reservoir Simulation and Modeling is a full-field model that only uses facts and reality through actual field measurements and avoids any assumptions, interpretations, simplifications, preconceived notions, and biases. Since AI-based (Top-Down) Reservoir Simulation follows AI-Ethics it avoids using “Hybrid Model” that includes data that is generated through mathematical equations. Unlike many other approaches that are currently used by petroleum service and vendor companies (Artificial General Intelligence), AI-based (Top-Down) Reservoir Simulation incorporates the Science and Engineering Application of Artificial Intelligence.
AI-based (Top-Down) Reservoir Modeling uses “eXplainable AI (XAI)” and generates AI-based Geological Model (Geo-Analytics), Fully Automated History Matching, Blind Validation Forecasting, and avoids using only Space-related reservoir layer characteristics (k*h) for production allocations and uses both space and time to generate “AI-based Production Allocation”. AI-based (Top-Down) Reservoir Simulation and Modeling provides OpEx and CapEx Optimization.Read more...
Course contents
Chapter 1 - Artificial Intelligence & Machine Learning
1.00 - Introduction (12 min.) Sample Lesson
1.01 - Brief History Of Artificial Intelligence
1.02 - Definitions Of Artificial Intelligence And Machine Learning
1.03 - Artificial Intelligence Versus Traditional Statistics
1.04 - Correlation vs. Causation
1.05 - Science And Engineering Application Of Artificial Intelligence
1.06 - Importance of Domain Experts
1.07 - Modeling Physics Using Artificial Intelligence
1.08 - Ethics Of Artificial Intelligence (Ai-Ethics)
1.09 - Explainable Artificial Intelligence (Xai)
Chapter 2 - AI-based (Top-Down) Reservoir Simulation and Modeling
2.01 - Top-Down Modeling
2.02 - Characteristics of Top-Down Modeling
2.03 - Geo-Analytics – Ai-Based Geological Modeling
2.04 - Dynamic Conductivity Mapping
2.05 - Automated History Matching
2.06 - Top-Down Modeling Production Allocation
Chapter 3 - IMagine™ Software Application for TDM
3.01 - Explanation Of The Imagine™ Software Application
3.02 - Using An Actual Case Study
3.03 - Geo-Analytics
3.04 - Data Importing
3.05 - Reservoir Delineation
3.06 - Dynamic Mapping
3.07 - Intelligent Data Patching
3.08 - Predictive Analytics
3.09 - Prescriptive Analytics
3.10 - Operations Optimization
