Oil and Gas
This training course will provide an introduction to statistical modeling and big data analytics for petroleum engineering and geoscience applications. Topics to be covered include: (a) easy-to-understand descriptions of the commonly-used techniques, and (b) case studies demonstrating the applicability, limitations and value-added proposition for these methods. This course will inform engineers and geologists about techniques for data-driven analysis that can convert data into actionable information for reducing cost, improving efficiency and/or increasing productivity in oil and gas operations.
Duration and Training Method
A one-day classroom course consisting of lectures with worked examples.
Participants will learn to:
- Apply foundational concepts in probability and statistics for basic data analysis
- Interpret linear regression for building simple input-output models
- Examine multivariate data reduction and clustering for finding sub-groups of data that have similar attributes
- Converse with confidence about big data, data analytics and machine learning terminology and fundamental concepts
- Differentiate machine learning techniques for regression and classification for developing data-driven input-output models
- Critique uncertainty quantification studies for probabilistic performance forecasting
- Big data technologies, basic data analytics and machine learning terminology/concepts
- Exploratory data analysis, probability distributions, confidence limits
- Basic linear regression
- Data reduction, cluster analysis and data visualization
- Machine learning basics, techniques for regression and classification problems
- Machine learning case studies
- Uncertainty quantification
Who Should Attend and Prerequisites
This course is for designed for petroleum engineers, geoscientists, and managers interested in learning about the basics of statistical modeling and data analytics.
Dr. Srikanta Mishra is Technical Director for Geo-energy Modeling & Analytics at Battelle Memorial Institute, the world’s largest independent contract R&D organization. He is responsible for leading a technology portfolio related to computational modeling and data analytics for geological carbon storage, improved oil recovery projects, and shale gas/oil development. His recent work has focused on full-physics and reduced-order modeling, and pressure-based monitoring, of CO2 geologic sequestration projects. He has served as PI or co-PI on a number of CO2 geological storage and EOR projects funded by the US Department of Energy with field demonstration sites in the Appalachian and Michigan Basins.
Dr. Mishra has presented lectures and conducted short courses and workshops on CO2 geologic sequestration in many US universities as well as in academic and research organizations in Switzerland, India, South Africa, UK, Mexico and Indonesia. He is an editor of the book “CO2 Injection in the Network of Carbonate Fractures” recently published by Springer, and the author of ~200 technical publications.
Dr. Mishra is a member of the Technical Advisory Board of the SMART initiative (Science Informed Machine Learning for Accelerating Real-time Decisions for Subsurface applications) that is organized by the US Department of Energy’s Carbon Storage Program and involves multiple nationals labs and universities. He was selected as an SPE Distinguished Lecturer for 2018-19 on the topic of Big Data Analytics. He has also served as an Adjunct Professor of Petroleum and Geosystems Engineering at The University of Texas at Austin. Dr. Mishra holds a PhD degree from Stanford University, an MS degree from University of Texas and a BTech degree from Indian School of Mines – all in Petroleum Engineering.
Affiliations and Accreditation
PhD Stanford University - Petroleum Engineering
MS Stanford University - Petroleum Engineering
BTech Indian School of Mines - Petroleum Engineering
N479: Applied Statistical Modeling and Big Data Analytics
N480: Introduction to Statistical Modeling and Big Data Analytics
N535: Carbon Capture Sequestration (CSS)