This course will be dedicated to problems and topics occurring in the energy industry, both in R&D and in operations. It has three main components:
- Computational Geophysics
- Reservoir Simulation Fundamentals
- Machine Learning
The first two components will be taught together in the first 10 weeks by dedicating half of the class-time to each subject. The Machine Learning component will, in part, build on the first two fundamental components and will be taught using the full class time.
The participants in this geophysics part of the course are expected to be interested into learn how to use modern seismic data to image the subsurface with awareness of the computational costs of the techniques involved. The main focus will be given to current seismic imaging tools including cutting-edge Machine Learning (ML) applications. As the result of the successful completion of this course part, the course participants should be able to: (1) Understand the context and value of imaging tools for the hydrocarbon exploration business. (2) Relate the imaging tools with their computational costs for modern computer resources. (3) Properly use wave-based geophysical imaging and ML-based tools and (4) Understand main seismic processing and interpretation decisions.
Applied Reservoir Simulation
This component of the course will introduce participants to the practice of reservoir simulation. This class will be an applied course on reservoir simulation. Theoretical descriptions will be provided as warranted but will be kept to minimum. Class participants will learn about the fundamentals of applied reservoir simulation, use of a reservoir simulator, and how to select the proper model for a simulation study. This course will also cover data preparation, grid design, calibration of the reservoir model, forecasting of future performance, and interpretation of simulation results. Participants will also be introduced to the role of simulation in reservoir management, limitations of reservoir simulation, and the structural aspects of the models. Upscaling and recent advances simulation techniques will also be discussed. A realistic open-source reservoir simulation software will be used during the tutorials and computer projects.
Machine Learning for Oil & Gas
This part of the course will introduce the fundamentals of statistical learning, present a few of the popular learning paradigms and algorithms, and culminate in a small student project applying them to an oil reservoir data set using the R programming language (solutions to class problems will be accepted in any programming language or system). Much of the material presented here is also known under the names “Big Data”, “Data Analytics”, “Artificial Intelligence”, “Data Mining”, “Petroleum Data Driven Analytics” and other terms. Weeks 11 and 12 are theory only, weeks 13-15 will have small hands-on exercises incorporated and week 16 and 17 are dedicated to solving a simple oil reservoir problem using machine learning.
Participation in this certificate program fulfills industry demand for a university-based curriculum that trains employees to become educated data management – developed with the industry for the industry.
- Tools, Methods & Best Practices for Data Science and Analytics
- Computational/Data Science in the Energy Sector
- Energy Data Management and Governance
- Manage, maintain, monitor and measure organizational data governance plan
- Understand the importance of organization-wide standards
- Apply well-know algorithms, methods and best practices for analyzing diverse data sets
- Become familiar with tools and environments for data management
- Use robust data workflows and life-cycles
- Entry-level geoscientists/ geophysicists
- Mid-career professionals seeking to redirect their career path
- Mid-level managers seeking to advance their career into energy data
- Managers seeking to update their knowledge and understanding of energy data practices
Rice University’s Department of Earth, Environmental and Planetary Sciences (EEPS) and Glasscock School of Continuing Studies have partnered to offer the Energy & Data Management Certificate.
The estimated Cost of Attendance (COA) for the Energy and Data Management Certificate is up to $10,500.00. Financial assistance information can be found on Rice Financial Aid's Visiting and Continuing Education Students page.
Due to the high demand for courses, registrations are considered final as of 10 working days before class starts. No refunds will be issued after these dates and credits will not be given for future classes. No refunds will be granted for participants who miss a portion of a program. Refund requests before the deadline are subject to a 10% processing fee and must be made in writing to firstname.lastname@example.org. If books have been issued, the cost of the books and any shipping fees will be deducted. Refunds for credit card payments will be processed as credits to the accounts from which they were paid and may not appear as a credit until the following statement. Refunds for enrollments paid by check take up to four weeks to be processed and mailed by the Rice University accounting office. There is a $30 charge for any check returned for insufficient funds.
Applies Towards the Following Certificates
- Energy & Data Management Certificate : Energy & Data Management Certificate