DE: A Data-driven Approach to Modeling and Optimization of a North Sea Asset Using Real-time Data

Speaker Eric Ziegel. Senior Statistician, BP
Eric Ziegel is Senior Statistician at BP and a project manager in the Decision Analytics Center of Expertise in BP Upstream.  He is the senior computational technology advisor for the research and development work in using data mining, predictive analytics, and artificial intelligence in Upstream applications.  He is an SPE ...

Eric Ziegel is Senior Statistician at BP and a project manager in the Decision Analytics Center of Expertise in BP Upstream.  He is the senior computational technology advisor for the research and development work in using data mining, predictive analytics, and artificial intelligence in Upstream applications.  He is an SPE member and active in the Petroleum Data-Driven Analytics Technical Section.  Eric has been a Fellow of the American Statistical Association since 1989 with association experience as chair for program, meetings and publications committees and more than 20 years as a statistics journal editor.


Eric has 44 years of industry experience in oil and gas.  He has an MSc in Applied Statistics from Purdue University.  The early part of his career was spent in support of petrochemicals.  Since 1984 he has been Amoco and subsequently BP’s principal statistician, working from corporate centers in Chicago and Houston.  He consulted and taught statistics globally across both organizations.  Eric became fulltime in BP Upstream in 2005.  He was the co-developer of the original Data Analytics Program in BP’s Field of the Future technology flagship. 

Full Description

Installing increased numbers of sensors on existing and new well stock does not necessarily result in increased production.  New methods of analysis must also be developed to capitalize on the new data streams to maximize safety and value delivery.  A novel approach to modelling gas coning, which has been difficult to characterize using first-principles models, has been developed for one of BP’s assets.  It is based on of the integration of several data-driven models representing different aspects of a well’s performance characteristics.  These models use the well data that is captured by existing sensors.

In this presentation we describes how the data-driven approach has been developed and successfully tested on a North Sea reservoir operated by BP.   The suite of data-driven models provides the capability to predict the fluid and gas rates for use in short-time-loop optimizations.    

When?

Wed, May. 15, 2013
11:30 a.m. - 1 p.m. America/Chicago

How Much?

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Where?

Courtyard on St James
1885 St James Place
Houston, TX 77056

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