RMAG/RPS Short Course: Intro to Statistical Modeling and Big Data Analytics

Date: October 26, 2020
Time: 8:00 am - 4:30 pm
Online via RPS Virtual Classroom Platform

Unfortunately, we have had to cancel this course.

Data Science Webinar Series Companion Course

Learn the fundamentals of data science with this foundational short course

As part of RMAG's on-going partnership with RPS Nautilus Training, we are offering a one-day short course in statistical modelling and data analytics, taught by Dr. Srikanta Mishra, RPS instructor and Senior Research Leader and Discipline Lead for Reservoir Sciences and Data Analytics at Battelle Memorial Institute.

Course description:

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.

The class will be taught on RPS's distance learning platform.

Cost: $200/RMAG members; $235/non-members 

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.

Learning Outcomes

  1. Apply foundational concepts in probability and statistics for basic data analysis
  2. Interpret linear regression for building simple input-output models
  3. Examine multivariate data reduction and clustering for finding sub-groups of data that have similar attributes
  4. Converse with confidence about big data, data analytics and machine learning terminology and fundamental concepts
  5. Differentiate machine learning techniques for regression and classification for developing data-driven input-output models
  6. Critique uncertainty quantification studies for probabilistic performance forecasting

Course Instructor

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 & Accreditation
PhD Stanford University - Petroleum Engineering
MS Stanford University - Petroleum Engineering
BTech Indian School of Mines - Petroleum Engineering

Continuing Education credits

0.8 CEUs

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