Petroleum Data Analytics
Shahab D. Mohaghegh
Professor at West Virginia University and President of Intelligent Solutions, Inc.
Petroleum Data Analytics
Application of Artificial Intelligence and Machine Learning in Petroleum Engineering
?
Course Description
Petroleum Data Analytics (PDA) is the application of Artificial Intelligence (AI) and Machine Learning (ML) in the oil and gas industry. The future of our industry will be highly influenced by PDA. Engineering-domain experts who become highly skilled AI and ML practitioners are the ones who will control the future of engineering disciplines, including petroleum engineering. Becoming an engineering-related AI and ML expert practitioner requires fundamental understanding and extensive experience using AI and ML to solve engineering-related problems.
The objective of this week-long course is to provide the required and realistic foundations of Petroleum Data Analytics to the new generation of petroleum professionals that have recognized the potential of AI and ML in our industry. Clearly, short courses will not cover or provide all that is necessary for petroleum professionals to become true PDA experts. However, a week such as this will play a crucial role for the enthusiast of this technology in identifying the scientific realities associated with the foundation of AI and ML and its true application in PDA.
?
Petroleum Data Analytics (PDA)
Petroleum Data Analytics experts are expected to be able to use AI and ML to solve petroleum-engineering-related problems in ways that are different from how such problems have been addressed in the past 70 years in our industry. The difference has to do with avoiding assumptions and simplifications that have played a significant role in addressing most of the traditional petroleum-engineering solutions. Traditional engineering approaches to problem solving in our industry incorporate simplified mathematical equations that are overwhelmed by assumptions in order to model highly complex physical phenomenon that is not directly observable. Continuing to use such traditional approaches and combining them with AI and ML algorithms (hybrid models) minimizes the contribution of this highly capable technology to engineering problem solving.
Petroleum Data Analytics is a realistic, scientific, and domain expert related data-driven analysis, modeling, and decision-making technology using Artificial Intelligence & Machine Learning to solve highly complex petroleum-engineering-related problems. Expertise in Petroleum Data Analytics requires three characteristics:
Domain Expertise: PDA experts must have domain expertise in the specific areas of petroleum engineering that they are addressed using AI & ML. Examples of such domain expertise are being drilling expert, completion expert, reservoir-engineering expert, reservoir-modeling expert, reservoir-management expert, CO2 Storage expert, surveillance expert, production expert, or surface facility expert. Lack of domain expertise takes us back to application of traditional statistics rather than Artificial Intelligence in our industry.
Expertise in Practicing AI & ML: PDA experts must develop expertise in solving and addressing petroleum-engineering-related problems using AI & ML. Understanding the mathematics behind the ML algorithms or having the capabilities to code such algorithms does not turn petroleum-engineering professionals into PDA experts. While understanding the mathematics behind the ML algorithms is an absolute requirement, they are not enough to turn petroleum engineers into expert practitioners of the application of AI and ML in engineering-related problems.
A lack of expertise in the application of AI & ML by domain experts currently is being experienced by some operating and service companies. When some petroleum professionals who have been exposed to AI & ML technology for a couple of years have applied this technology to a few problems, some of these applications end up being unsuccessful. They then move toward “hybrid models,” which means back to traditional engineering approaches in order to get the AI & ML to provide reasonable results. This is just as bad as the lack of domain expertise when AI & ML experts from Silicon Valley were used solve petroleum-engineering-related problems.
Realistic Understanding of AI and ML: Having some fundamental knowledge of AI & ML is incredibly attractive and can end up being highly addictive (once one becomes involved in it in a realistic manner, moving away from it can be very difficult). AI & ML is a complete paradigm shift in science and technology. As one becomes interested in learning more about this technology, one may become very interested in biology, neuroscience, evolution, philosophy, statistics, mathematics, and computer science. The more of this understanding that one develops, the more one becomes interested in researching this topic and developing new approaches to engineering problem solving.
Details of the Petroleum Data Analytics Short Course:
·??????Artificial Intelligence and Machine Learning
o??Definitions
o??Brief History
o??Modeling Physics Using Artificial Intelligence
o??Engineering Application of Artificial Intelligence
o??Traditional Statistics versus Artificial Intelligence
o??Hybrid Models
?
·??????Petroleum Data Analytics
o??Artificial Neural Network
o??Fuzzy Set Theory
o??Evolutionary Computing
o??Explainable Artificial Intelligence (XAI)
o??Ethics of Artificial Intelligence (AI-Ethics)
?
·??????AI-based (Top-Down) Reservoir Simulation and Modeling
o??Top-Down Modeling
§?AI-based Modeling using Space and Time Field Measurements
§?Spatio-Temporal Database Generation
§?Automated, Full Field History Matching
§?Blind Validation of History Match in Space and Time
§?Production Forecasting
§?Sensitivity Analysis
§?Production Optimization
领英推荐
§?Injection Optimization
§?Infill Location Optimization
o??Geo-Analytics: AI-base Geological Modeling
o??Dynamic Production Allocation
o??Actual Case Studies
§?Mature Field Production Optimization in the Middle East
§?Mature Field Production Optimization in the Southeast Asia
?
·??????AI-based Proxy Modeling of Numerical Reservoir Simulation
o??Traditional Proxy Modeling
§?Reduced Order Modeling (ROM)
§?Response Surface Modeling (RSM)
o??Smart Proxy Modeling
§?Expertise in Numerical Reservoir Simulation
§?Spatio-Temporal Database Generation
§?Accuracy of Smart Proxy Model in Space and Time
§?Blind Validation of Numerical Reservoir Simulation Runs
o??CCS-Analytics (AI-based Carbon Capture and Storage)
§?Geological Realizations
§?Reservoir Pressure Distribution in Space and Time
§?CO2 Saturation Distribution in Space and Time
o??Case Studies
§?Smart Proxy Model of Mature Field in Middle East
§?Smart Proxy Model of CO2 Injection in Saline Aquifer
?
·??????AI-based Completion and Production Optimization of Shale Wells
o??Shale Analytics
§?Modeling Production from Shale using Conventional Techniques
·??????Decline Curve Analysis
·??????Rate Transient Analysis
·??????Numerical Reservoir Simulation
§?Shale Descriptive Analytics
§?Shale Predictive Analytics
§?Shale Prescriptive Analytics
o??Case Studies
§?Gas Production from Marcellus Shale
§?Oil Production from Permian Basin
o??AI-based Modeling of Frac-Hit
o??AI-based Production Allocation of Stages/Cluster via Fiber Optics
?
User Success Engineer @ Dassault Systèmes | Cloud Architect & DevOps Enthusiast
1 年Farzain Ud Din Kirmani
Principal - Technical Product Management at Baker Hughes
1 年What’s the cost to take this course?
Operations Eng. Manager | Petroleum Engineer | Data Science | Machine Learning | Data Analytics
1 年Dear Shahab D. Mohaghegh, will that be a on site or a online course? Thank you
Production F.A Engineer & Instructor | Trained +800 Petroleum Engineers on Python Data Stack|
1 年Keep it up, this looks very good.