SPE-GCS Young Professionals: Lessons Learned from Data Mining in Unconventional Reservoirs

Speaker: Randal F. (Randy) LaFollette
Speaker Randal F. (Randy) LaFollette
Speaker Bio: Randy LaFollette works in the Pressure Pumping Production Enhancement Product Line at Baker Hughes in Tomball, Texas, USA. Mr. LaFollette holds a BSc degree in Geological Science from Lehigh University, Bethlehem, Pennsylvania. He has 38 years of experience in the industry and has worked in Pressure Pumping in Tomball ...

Speaker Bio: Randy LaFollette works in the Pressure Pumping Production Enhancement Product Line at Baker Hughes in Tomball, Texas, USA. Mr. LaFollette holds a BSc degree in Geological Science from Lehigh University, Bethlehem, Pennsylvania. He has 38 years of experience in the industry and has worked in Pressure Pumping in Tomball since 1995. He is active in SPE, HGS, and AAPG, aiding with meeting organization and presenting on various reservoir, completion / stimulation, and data-mining topics. He has developed and taught short courses on Hydraulic Fracturing for AAPG and is an SPE Distinguished Lecturer for 2015-16. His Distinguished Lecture is entitled “Lessons Learned from Data Mining in Unconventional Reservoirs.” Mr. LaFollette is a subject matter expert for Baker Hughes in Geoscience and Petroleum Engineering.


Speaker Title: Director


Speaker Company: Applied Reservoir Technology at Baker Hughes

Full Description

The task of identifying key production drivers in unconventional reservoirs remains challenging, even after decades of exploration and production in North America during which tens of thousands of horizontal unconventional wells have been drilled and completed. Tens to hundreds of variables, categorized as reservoir quality, well architecture, completion, stimulation, and production metrics, are involved and there are many different interrelationships among the variables to be considered. Further, formation evaluation is typically minimal and there are unknown variables in the system that can only be guessed at, ignored, or proxied. The author’s team has combined Geographical Information Systems (GIS) analysis and multivariate analysis using boosted regression trees for improved data mining results as compared to univariate methods.

The purpose of this lecture is to discuss key elements of data mining in unconventional reservoirs, in order to raise awareness of cutting-edge statistical tools and methods being brought to bear in the industry. The presentation will provide highlights of real world examples of data mining projects in three different shale plays. If there were only one idea for audiences to take away from the lecture, it would be that exploiting unconventional reservoirs is a highly complex task with many moving parts and data mining is a needed tool to be applied to better understand the importance of specific well productivity drivers. Another way to say it is that the talk is intended to provide the audience with improved statistical methods for the “statistical” plays so that multi-million dollar decisions can be truly data-driven.

All professionals are welcome to attend this event.

Organizer

Sahil Malhotra, Julia Carval, Arash Shadravan


Please contact us if you have any questions:


Call us at 832-854-7885


Email us at sahil.malhotra@chevron.com, julia.carval@us.bureauveritas.com

When?

Tue, Sep. 22, 2015
11 a.m. - 1 p.m. US/Central

How Much?

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Where?

Houston Technology Center
410 Pierce Street
Houston, Texas 77002
United States

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