DAT 611
AI-Based (Top-Down) Reservoir Simulation and Modelling
Petroleum Data Analytics (PDA) is the application of Artificial Intelligence (AI) and Machine Learning (ML) in the oil and gas industry
AI-Based (Top-Down) Reservoir Simulation and Modelling
COURSE SCHEDULE
| Code | Date | Location | price (€)* |
|---|---|---|---|
| DAT 611 | 25 - 29 Sep 2023 | Stavanger | 4590 |
* Prices are subject to VAT and local terms. Ph.D. students, groups (≥ 3 persons) and early bird registrants (8 weeks in advance) are entitled to a DISCOUNT!
COURSE OVERVIEW
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.
COURSE OUTLINE
5 days
INSTRUCTOR
Day 1: Artificial Intelligence and Machine Learning
o Brief History of Artificial Intelligence
o Definitions of Artificial Intelligence and Machine Learning
o Science and Engineering Application of Artificial Intelligence
o Modelling Physics using Artificial Intelligence
o Artificial Intelligence versus Traditional Statistics
o Ethics of Artificial Intelligence (AI-Ethics)
o Explainable Artificial Intelligence (XAI)
Day 2: Machine Learning Algorithms used for Artificial Intelligence
o Artificial Neural Networks:
- Biology of Human Brain
- Parallel, Distributed Information Processing
- Mathematics Behind Neural Networks
- Gradient Descent
- Training, Calibration, and Validation
- Data Handling
- Different Types of Neural Networks
o Fuzzy Set Theory:
- Conventional Set Theory
- Human Logic vs. Aristotelian Logic
- Mathematics Behind Fuzzy Logic
o Evolutionary Computing:
- Darwinian Evolution Theory (Natural Selection)
- Genetic Algorithm for Optimization
Day 3: AI-based (Top-Down) Reservoir Simulation and Modelling
o Difference between Traditional Numerical Reservoir Simulation and Top-Down Modelling
o Top-Down Modelling:
- Components of Top-Down Modelling
- Data QC/QA
o Geo-Analytics – AI-based Geological Modelling:
- Dynamic Conductivity Map
- AI-based Spatial Distribution of Reservoir Characteristics
- Spatial Distribution of OOIP
- Spatio-Temporal Distribution of Remaining Reserves
- Spatio-Temporal Distribution of Reservoir Pressure
Day 4: AI-based (Top-Down) Reservoir Simulation and Modelling Continued
o Development of Spatio-Temporal Database:
- Static and Dynamic Data
- Resolution in Time and Space
- Role of Offset Wells
o Automated History Matching:
- Training, Calibration, and Validation
- Testing TDM Forecasting through Blind Validation of the Top-Down Model
- Optimization of Machine Learning Topology
o Top-Down Modeling Production Allocation
o Field Development Planning and Reservoir Management:
- Forecasting Oil Production, GOR and WC
- Choke Setting/Well-Head Pressure Optimization
- Water/Gas Injection Optimization
- Determination of Infill Locations
- Uncertainty Analysis
Day 5: IMagine™ Software Application for TDM
o Explanation of the IMagine™ Software Application
o Tutorial of IMagine™ Software Application:
- Using an Actual Case Study
- Data Handling
- Geo-Analytics
- Data Importing
- Reservoir Delineation
- Dynamic Mapping
o Descriptive Analytics:
- Spatio-Temporal Dataset
- Intelligent Data Patching
- Well Biography
- Key Performance Indicators (KPI)
o Predictive Analytics:
- Model Development
- History Matching
- TDM Development
o Prescriptive Analytics:
- Production Forecasting
- Sensitivity Analysis
- Operations Optimization
- Infill Well Optimization
- Injection Optimization
Professor Shahab D. Mohaghegh
Professor Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence, Machine Learning and Data Mining in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University and the president and CEO of Intelligent Solutions, Inc. (ISI). He holds B.S., M.S., and Ph.D. degrees in petroleum and natural gas engineering. He has authored three books (Shale Analytics– Data Driven Reservoir Modeling– Application of Data- Driven Analytics for the Geological Storage of CO2), more than 170 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured four times as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT). He is the founder of Petroleum Data-Driven Analytics, SPE’s Technical Section dedicated to AI, machine learning and data mining. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage (2014-2016).
FAQ
DESIGNED FOR
This course is designed for engineers, geoscientist, and managers. Specifically, those involved with reservoir, completion, and production in operating and service companies. In general, those involved in planning, completion, and operation of hydrocarbon assets are the main target audience.
COURSE LEVEL
o Intermediate to Advanced
LEARNING OBJECTIVES
The objective of this course is to demonstrate and increase the skills of the Participants to apply the power of Artificial Intelligence and the differences it can make for informed decision making when it comes to objectives such as infill location optimization and reservoir production and recovery optimization.
REGISTER
Registration is now OPEN!
* Prices are subject to VAT and local terms. Ph.D. students, groups (≥ 3 persons) and early bird registrants (8 weeks in advance) are entitled to a DISCOUNT!
For more details and registration please send email to: register@petro-teach.com
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