Nov. 19, 2020


Description

 

The Energy industry needs to constantly adapt to the quickly changing business landscape. The companies are betting on digital transformation as a way to innovate and grow. This webinar event will feature two technical talks demonstrating data analytics as a key ingredient in delivering improved productivity and higher efficiency.  The talks will be followed by a virtual networking opportunity.

Abstract Case Study #1: 

Several years ago the analytic workflow Type-Curve Optimizing Geostatistical Array (TOGA) was introduced in the industry to tie unconventional reservoir and well completion characteristics to historical production and make robust predictions of well performance. TOGA is built on a foundational geologic framework and leveraging random forest machine learning optimization for subsurface data sets. Random forest methods are preferred for unconventional systems because they capture complex, multidimensional, and non-linear interactions in noisy databases. TOGA is part of a portfolio of other machine learning and physically-based reservoir engineering tools used by companies to reduce uncertainty in unconventional reservoir performance prediction. The workflow generates performance prediction maps by applying the random forest multivariate relationships to grids of the key reservoir predictor variables. These production prediction maps are stacked to form an array of locations that each have a unique expected production profile through time. TOGA output may be used to calibrate existing type curve workflows, define reservoir sweet spots, establish reservoir continuity, and predict ultimate recovery. Data science and machine learning approaches have progressed the organization’s understanding of its assets, especially in the Permian Basin. Unconventional plays were once thought to be relatively unpredictable, highly variable, and have little connection to reservoir properties. TOGA and other proprietary data analytic technologies have enabled the identification of key reservoir performance drivers for each unconventional target zone under development.

 

 

 

 


Featured Speakers

Speaker: Shane J. Prochnow
Speaker Shane J. Prochnow
Shane J. Prochnow is Digital Geology Advisor at Chevon Technology Center- Sub-Surface Innovation Lab. He has over 15 years industry, at ExxonMobil and Chevron. He holds a Post-Doc (3 yrs), Ph.D, MS, BS in Geology at Baylor University. He is an Army National Guard Officer (ret) and his research interests include …

Shane J. Prochnow is Digital Geology Advisor at Chevon Technology Center- Sub-Surface Innovation Lab. He has over 15 years industry, at ExxonMobil and Chevron. He holds a Post-Doc (3 yrs), Ph.D, MS, BS in Geology at Baylor University. He is an Army National Guard Officer (ret) and his research interests include unconventionals, reservoir characterization, geostatistics, machine learning, integrating complex systems. Shane has previously worked as Permian Basin First Principles Champion, Reservoir Characterization Advisor for MCBU Asset Development, and Geologist on the ARMT Completions Optimization Group

Full Description

Speaker: Prashant Mehrotra
Speaker Prashant Mehrotra

Prashant Mehrotra is a partner at Boston Consulting Group. 


 


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Date and Time

Thu, Nov. 19, 2020

5 p.m. - 6:30 p.m.
(GMT-0500) US/Central

Pricing

  • Non Members
    $20.00
    (ends 11/12/2020)
  • Members
    $15.00
    (ends 11/12/2020)
  • Students and MiT
    $10.00
    (ends 11/12/2020)
Become a member

Location

Virtual



Group(s): Data Analytics