Course Schedule

Description

The "Artificial Intelligence in Reservoir Simulation & Modeling" course equips participants with the skills to integrate AI techniques into traditional reservoir simulation practices. Covering topics from machine learning applications to advanced data processing, this course provides a hands-on approach to enhancing reservoir modeling accuracy, ultimately leading to improved production strategies and cost efficiencies. Whether you are new to AI or looking to refine your expertise, this course is designed to elevate your technical capabilities and maximize reservoir performance.

Demo Class

2 Chapter
Demo Class
Demo Class
Artificial Intelligence Reservoir Simulation & Modeling- Explanation Video By Dr. Shahab D. Mohaghegh
Artificial Intelligence Reservoir Simulation & Modeling- Explanation Video By Dr. Shahab D. Mohaghegh
Course Description

Introduction


As the energy industry advances, artificial intelligence has become a game-changer in optimizing reservoir simulations. This course provides a comprehensive introduction to AI-driven methods that can transform simulation processes. Participants will explore how AI applications can improve decision-making, reduce uncertainty, and increase reservoir productivity.

Objectives


  • Understand the fundamentals of artificial intelligence and machine learning in the context of reservoir simulation.
  • Apply AI techniques to enhance reservoir data analysis and predictive modeling.
  • Develop practical skills in using AI for optimizing production forecasts.
  • Gain insights into the integration of AI with existing reservoir simulation software and workflows.
  • Training Methodology


    The course combines lectures, case studies, and hands-on exercises to provide a well-rounded learning experience. Participants will engage with real-world datasets, applying AI techniques to simulate and model reservoir scenarios. This approach ensures that they acquire both theoretical knowledge and practical skills

    Organisational Impact


    Organizations can expect enhanced reservoir modeling accuracy and improved forecasting capabilities, leading to more informed decision-making and cost-effective operations. By integrating AI-driven techniques, companies can streamline reservoir analysis processes, optimize production strategies, and stay competitive in the evolving energy landscape.

    Personal Impact


    Participants will gain valuable expertise in applying AI to reservoir simulation, a skill set in high demand across the oil and gas industry. This knowledge will enable them to contribute effectively to advanced reservoir projects, making them valuable assets to their teams and enhancing their career potential.

    Who Should Attend?


    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 Outline

    Day 1: Artificial Intelligence & Machine Learning


    ·       Brief History of Artificial Intelligence

    ·       Definitions of Artificial Intelligence and Machine Learning

    ·       Science and Engineering Application of Artificial Intelligence

    ·       Modeling Physics using Artificial Intelligence

    ·       Artificial Intelligence versus Traditional Statistics

    ·       Ethics of Artificial Intelligence (AI-Ethics)

    ·       Explainable Artificial Intelligence (XAI)

     

     

    Day 2: Artificial Intelligence & Machine Learning

     

    Machine Learning Algorithms used for Artificial Intelligence

    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

     

    Fuzzy Set Theory

    ·       Conventional Set Theory

    ·       Human Logic vs. Aristotelian Logic

    ·       Mathematics Behind Fuzzy Logic


    Evolutionary Computing

    ·       Darwinian Evolution Theory (Natural Selection)

    ·       Genetic Algorithm for Optimization

     

     

    Day 3: Artificial Intelligence Reservoir Simulation and Modeling


    Difference between Traditional Numerical Reservoir Simulation and Top-Down Modeling

    Top-Down Modeling (TDM)

    ·       Components of Top-Down Modeling (TDM)

    ·       Data QC/QA

     

    Geo-Analytics AI-based Geological Modeling

    ·       Dynamic Conductivity Map

    ·       AI-based Spatial Distribution of Reservoir Characteristics

    ·       Spatial Distribution of OOIP

    ·       Spatial-Temporal Distribution of Remaining Reserves

    ·       Spatial-Temporal Distribution of Reservoir Pressure

     

    Development of Spatial-Temporal Database

    ·       Static and Dynamic Data

    ·       Resolution in Time and Space

    ·       Role of Offset Wells

     

    Automated History Matching

    ·       Training, Calibration, and Validation

    ·       Testing TDM Forecasting through Blind Validation of the Top-Down Model (TDM)

    ·       Optimization of Machine Learning Topology

     

     

    Day 4: Artificial Intelligence Reservoir Simulation and Modeling

     

    Top-Down Modeling (TDM) Production Allocation

     

    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

     

    Explanation of the IMagine™ Software Application

    Tutorial of IMagine™ Software Application

     

    ·       Using an Actual Case Study

    ·       Data Handling

    ·       Geo-Analytics

    ·       Data Importing

    ·       Reservoir Delineation

    ·       Dynamic Mapping

     

    ·       Descriptive Analytics

    ·       Spatio-Temporal Dataset

    ·       Intelligent Data Patching

    ·       Well Biography

    ·       Key Performance Indicators (KPI)

     

    ·       Predictive Analytics

    ·       Model Development

    ·       History Matching

    ·       TDM Development

     

    ·       Prescriptive Analytics

    ·       Production Forecasting

    ·       Sensitivity Analysis

    ·       Operations

    Certificates


    On successful completion of this training course, PEA Certificate will be awarded to the delegates

    About The Trainer
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    Professor Shahab D. Mohaghegh is a pioneer in AI, Machine Learning, and Data Mining for the oil and gas industry, and serves as a professor at West Virginia University and CEO of Intelligent Solutions, Inc.

     

    He holds advanced degrees in petroleum and natural gas engineering, authored three books, over 170 technical papers, and led 60+ industry projects.

     

    A recognized leader, he is an SPE Distinguished Lecturer, featured author in SPE’s Journal, and founder of the SPE Petroleum Data-Driven Analytics section.

     

    Honored for his contributions, he played a key role in post-Deepwater Horizon efforts and advised on U.S. and ISO standards for Carbon Capture and Unconventional Resources.