پرفسور شهاب محقق، پیشگام در کاربرد هوش مصنوعی و یادگیری ماشین در صنعت اکتشاف و تولید، استاد مهندسی نفت و گاز طبیعی در دانشگاه ویرجینیای غربی و رئیس و مدیر عامل شرکت راه حل های هوشمند (ISI) است. او مدیر WVU-LEADS (آزمایشگاه مهندسی کاربرد علم داده) است.
وی با بیش از 30 سال تحقیق و توسعه کاربرد هوش مصنوعی و یادگیری ماشین در مهندسی نفت ، چهار کتاب (تجزیه و تحلیل شیل، مدل‌سازی مخزن مبتنی بر داده، کاربرد تجزیه و تحلیل داده‌محور برای ذخیره‌سازی زمین‌شناسی CO2، مدل سازی پروکسی هوشمند) تالیف کرده است.
شبیه سازی و مدل سازی مخزن مبتنی بر هوش مصنوعی
مشخصات سخنرانی ( دانلود )
هزینه و نحوه‌ی انتخاب کارگاه آموزشی
AI-based (Top-Down)
Reservoir Simulation and Modeling
:Summary
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
AI-based (Top-Down) Reservoir Simulation 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 (SEA-AI).
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 Modeling provides OpEx and CapEx Optimization. This presentation includes actual case studies
