Data has become a huge factor in all phases of the oil and gas industry, upstream, midstream and downstream. Companies need fresh talent on an ongoing basis to fulfill the need for managers who can effectively manage data, which is constantly increasing in volume and complexity. 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, which focuses on exploration and production as a data-driven business and emphasizes the importance of the curation, exploitation, and analysis of data using data analytics and data science methods within the upstream oil and gas industry. This noncredit, graduate-level certificate offers qualifying work-related education for those interested in gaining employment in this field or to advance their career. This opportunity provides professionals with a robust understanding of subsurface data management, highlighting the importance of Energy Data Management as an emerging discipline alongside geophysics, geology and other oil and gas-related career fields.
ESCI 549 - DATA MANAGEMENT AND DATA GOVERNANCE
Fall, 2020, Thursdays, 6:00 p.m. - 8:30 p.m
Credit Hours: 3
Description: An organization’s data is recognized as the most vital asset of an enterprise, yet far too many fail to appreciate the legal and fiscal responsibilities and liabilities associated with it. This course covers the foundations, principles and methodology of data management and data governance to ensure such high quality data.
ESCI 570 - COMPUTATIONAL AND DATA SCIENCE IN THE ENERGY INDUSTRY
Spring, 2020, Tuesdays, 2:30 p.m. - 5:30 p.m
Credit Hours: 3
Description: 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: 1. Computational Geophysics 2. Reservoir Simulation Fundamentals 3. 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. Computational Geophysics 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.
ESCI 571 - DATA SCIENCE METHODS AND DATA MANAGEMENT
Spring 2020, Thursdays, 6:00 p.m. - 8:30 p.m
Credit Hours: 3
Description: Data has become a critical asset for enabling organizations to be competitive, make better decisions and support diverse stakeholders. In recent years, new methods, tools and techniques for data management and processing have been developed. In this vein, ensuring that users have the knowledge and skills to profit from this wealth of information is critical. In this course, participants will learn a holistic overview about infrastructure, data life cycles, metadata standards, policies and techniques for successfully managing and using data for decision-making. The emphasis of the course will be from the perspective of the Oil & Gas and Energy Industries. Recommended Prerequisite(s): Basic programming, introductory statistics