As a popular programming language for statistical computing and machine learning, R provides an ideal environment for geoscientists and petroleum engineers to analyze oil and gas data using powerful data wrangling, visualization, and engineering packages, mathematical and machine learning algorithms, and reporting tools.
In the first part of the workshop, participants will learn about the essentials of R programming including data structures, importing data, functional programming, and data visualization. “Rmbal” package, a material balance tool for reservoir engineering studies, will be reviewed in the second part of the course. Fundamentals and the theoretical background will be discussed first, followed by some field examples to highlight the practical applications of the package.
By the end of the workshop, participants will be able to write codes to import, analyze, and visualize oil and gas data within the R environment.
1- Learn how to work with various types of data structures
2- Learn how to write technical functions
3- Learn the fundamentals of the generalized material balance model and its applications in reservoir engineering studies
4- Learn how to use “Rmbal” package for hydrocarbon-in-place estimation and reservoir performance forecasting
Dr. Tabasinejad is currently a research associate in the “Tight Oil Consortium” at the University of Calgary where he conducts numerical simulations, production data analyses, and phase behavior modelling studies for oil and gas companies. He also consults and develops petroleum engineering software through his consulting firm, Susa Energy Inc.
He has more than 15 years of industry and research experience in the Canadian/US tight oil, shale gas and oilsands resources. His industry experience extends across research and development studies, EOR pilot design and operations, and field performance optimization. He has worked on various thermal projects in the Canadian oilsands including in-situ combustion, steam-assisted gravity drainage (SAGD), and solvent-aided SAGD modelling and optimization. He is currently involved in reservoir characterization, hydraulic fracturing and EOR studies of unconventional tight oil and gas reservoirs across North America.
His interests include phase behavior of hydrocarbons at nano-scale, enhanced oil recovery, production data analysis, machine learning applications in the oil and gas industry, and development of petroleum engineering software.
Dr. Tabasinejad holds a B.Sc. and an M.Sc. both in petroleum reservoir engineering from the Petroleum University of Technology (Iran), and a Ph.D. in petroleum engineering from the University of Calgary (Canada).