Data Science for Oil & Gas


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About the Course

Introduction to Data Science

  • What is Data Science (topic) and what is a Data Scientist (role). How is DS used in O&G; the importance of data; the value of data driven and physics driven processes.

The Structure and Process of a Data Team

  • The roles: DS, DA, DE, SE, etc; the project lifecycle: understanding business requirements; preparing data; defining a model, building a prototype, testing it, and deploying it.

Descriptive Analytics: Finding Patterns and Cleaning Data

  • Removing outliers; imputing missing data; etc.

Descriptive Analytics: Unsupervised Learning Techniques

  • Clustering; Recommendation systems; etc.

Course Blog

The 6 Key Quality Requirements of Oil and Gas Data

Data Science + Oil and Gas = Huge Opportunities

Meet the Oil and Gas Data Science Team

Your Instructor

Hector Klie, PhD
Hector Klie, PhD

Dr. Hector Klie is an experienced computational and data scientist with a passion to develop innovative physics- and data-driven solutions for a wide range of engineering and geoscientific applications in Oil & Gas. Dr. Klie is currently Data Science Expert and CEO of and has recently been appointed as Adjunct Professor at the Department of Computational and Applied Mathematics (CAAM) at Rice University.

Before his current positions, Dr. Klie was Director of Enterprise Data Solutions and Data Science Technical Lead at Sanchez Oil & Gas Corporation (2016-2017). He worked as Staff Reservoir Engineer and Lead Data Scientist at ConocoPhillips (2008-2016). He was previously Associate Director and Senior Research Associate at the Center for Subsurface Modeling in The University of Texas at Austin (2003-2008). He spent his first 14 years of professional career working for PDVSA-Intevep (1989-2003), the research and technological branch of the Venezuelan oil industry. During a span of almost 30 years, Dr. Klie has been involved in numerous multi-disciplinary projects and published over 80 papers in the fields of Reservoir Engineering, Geophysics, Applied Mathematics and Computational Science. He has made valuable contributions in the areas of sparse linear solvers, stochastic optimization, uncertainty quantification, high performance computing, reduced order modeling and machine learning. He has chaired multiple technical venues at SPE, SIAM, EAGE and SEG and has patented 5 inventions. Dr. Klie completed his Ph.D. in Computational Science and Engineering at the Dept. of Computational and Applied Mathematics at Rice University, 1996, and a Master Degree in Computer Science at the Simon Bolivar University, Venezuela, 1991.