Advanced Data-Driven Analytics for Reservoir and Production Management of Shale Assets
The main idea behind data-driven solutions is that “Data” can provide the foundation of new solutions. Using “Data” as the main building block of models is the new paradigm in science and technology. Data-driven analytics and predictive modeling incorporates pattern recognition capabilities of artificial intelligence and data mining, through machine learning, to solve complex and non-linear engineering problems.
In the context of oil and gas production from shale assets, ‘Hard Data’ refers to field measurements. This is the data that can be readily and usually is, measured during the operation. In most shale assets ‘Hard Data’ associated with hydraulic fracturing is measured and recorded in reasonable detail and are usually available.
Attendees will become familiar with the fundamentals of data-driven analytics and the most popular techniques that are used to perform such tasks such as conventional statistics, artificial neural networks and fuzzy set theory.
This course will demonstrate through actual case studies (real field data from hundreds of shale wells) how to build data-driven predictive model and how to use them in order to perform analysis.
- How to treat data in the context of data-driven analytics
- Organize end prepare the data for predictive modeling
- How to make sure that the physics of fluid flow in shale in honored during the predictive analytics
- How to build predictive models using data as the main building block
- How to avoid over-training (memorization) while promote generalization
1 or 2 Days
Why You Should Attend
Application of data-driven analytics and predictive modeling in the oil and gas industry is fairly new. A handful of researchers and practitioners have concentrated their efforts on providing the next generation of tools that incorporates this technology, for the petroleum industry.
These advance techniques are an integrated part of many new technologies used by everyone on their day-to-day lives such as smart automatic-transmission in many cars, detecting explosives in the airport security systems, providing smooth rides in subway systems and preventing fraud in use of credit cards. They are extensively used in the financial market to predict chaotic stock market behavior, or optimize financial portfolios.
Who Should Attend
This course is intended for completion engineers, production engineers and managers, reservoir engineers, geoscientists, asset managers, and team leaders.
0.8 or 1.6 CEUs (Continuing Education Units) will be awarded for this 1-day course.
To receive a full refund, all cancellations must be received in writing no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Send cancellation requests by email to firstname.lastname@example.org; by fax to +1.866.460.3032 (US) or +1.972.852.9292 (outside US); or mail to SPE Registration, PO Box 833836, Richardson, TX 75083.
For more details, please contact us at email@example.com.
Shahab D. Mohaghegh is the president and CEO of Intelligent Solutions, Inc. (ISI) the leading company in providing the E&P industry with solutions based on artificial intelligence and data mining (AI&DM). He is also professor of petroleum and natural gas engineering at West Virginia University. With more than 18 years of experience, Mohaghegh has been a pioneer in the application of AI&DM in petroleum engineering, applying hybrid forms of neural networks, genetic algorithms and fuzzy logic to smart wells, smart completions, and smart fields as well as to drilling, completion, well stimulation, surface facility optimization, formation evaluation, seismic inversion, reservoir characterization, reservoir simulation, and reservoir management.
He has authored more than 150 technical papers and carried out more than 50 projects many of them with major international companies. He is a SPE Distinguished Lecturer and has been featured in the Distinguished Author Series of SPE’s Journal of Petroleum Technology (JPT) four times. 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 is a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources. Shahab is the designated U.S. liaison (WG4) representing the Unites States in the International Organization for Standardization (ISO) for CO2 capture and storage.
Shahab was an SPE Distinguished Lecturer for the 2007-08 lecture season. He was the former chair of the SPE Global Training Committee and currently is chairs SPE Book committee.