N480 Introduction to Statistical Modeling and Big Data Analytics
Course Facts
- Course Code:
- N480
- Duration:
- 1 days
- Type:
- Classroom
- CEU:
- 0.8 Continuing Education Units
- PDH:
- 8 Professional Development Hours
- Certificate:
- Certificate Issued Upon Completion
Summary
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
- Wrap-up
Who should attend
This course is for designed for petroleum engineers, geoscientists, and managers interested in learning about the basics of statistical modeling and data analytics.
Prerequisites and linking courses
Participants should have a basic knowledge of statistics. N480 is a precursor to N479 (Applied Statistical Modeling and Big Data Analytics), which goes into much greater detail on topics dicussed in this course.
Click on a name to learn more about the instructor
Srikanta Mishra
Srikanta Mishra
Background
Dr. Srikanta Mishra is Senior Research Leader and Discipline Lead for Reservoir Sciences and Data 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, shale gas/oil development and improved oil recovery projects. Dr. Mishra has taught short courses on statistical modeling, data analytics and uncertainty quantification at various professional conferences and client locations in the US, China, Spain, Japan, India, Finland, Belgium and Switzerland.
He is author of the book “Applied Statistical Modeling and Data Analytics for the Petroleum Geosciences” recently published by Elsevier as well as ~200 technical publications. Dr. Mishra has been selected as an SPE Distinguished Lecturer for 2018-19 on the topic of Big Data Analytics. He 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.
He has also served as an Adjunct Professor of Petroleum and Geosystems Engineering at The University of Texas at Austin.
Affiliations and Accreditation
PhD Stanford University - Petroleum Engineering
MS Stanford University - Petroleum Engineering
BTech Indian School of Mines - Petroleum Engineering
Courses Taught
N479: Applied Statistical Modeling and Big Data Analytics
N480: Introduction to Statistical Modeling and Big Data Analytics
Dates for this Course
Our Safety Management Systems ensure that every course is risk managed appropriately to enable quality, safe and enjoyable learning to take place in the field environment.
To learn more about how RPS manages your health and safety, visit the HSE section of this site.
New to RPS Training?
Create Account