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
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.