SPE Online Education
Transforming E&P Applications through Big Data Analytics
Recorded On: 07/09/2015
Data Driven Analytics in the Upstream Oil and Gas Industry
Shahab D. Mohaghegh
When it comes to data driven analytics there seem to be more questions than there are answers. This is quite natural when a novel technology tries to find its rightful place in a well-established industry. In this section of this three-part presentation, we attempt to address the questions that usually arise by petroleum professionals when data driven analytics is brought up. The objective is to clarify some of the confusion that seem to be surrounding the practical and useful implementation this technology in the upstream oil and gas industry.
The questions that will be addressed are: What is Data-Driven Analytics / Data Analytics? What are the main components of this technology? Who should be the owners and the champions of Data-Driven Analytics in a company? Is this an IT related technology or is more related to drilling, completion, geosciences, reservoir and production engineering? What are the commonalities and differences between the application of this technology in the oil and gas industry as compared to other industries? Who are the main players? What expertise are required? What is the relationship between Data-Driven Analytics and Physics? Is it really a black box? How does “Big Data” relate to data driven analytics? What is the manifestation of “Big Data” in our industry? How should this technology be incorporated in the operating companies? Who are the vanguards in our industry? Why is the academia not involved?
Geoscience Data Analytics: What can it do for me?
Dr. Srikanta Mishra
“Data analytics” has become quite the buzzword in recent years across a multitude of disciplines ranging from marketing to science and engineering. It involves analyzing data using advanced statistical methods to understand hidden patterns of association and input-output relationships in large, complex, multivariate data sets. The field of geoscience data analytics, i.e., the application of data analytics in E&P operations, is also emerging as an exciting new development. In this talk, I will provide an overview of various data analytics techniques that can be applied to understand “what the data say” in the context of oil and gas operations. Examples from reservoir characterization, production evaluation and reservoir performance will be used to demonstrate the applicability of these techniques and their potential for extracting data-driven insights as an aid to improved decision making.
Exploiting data value in real-time upstream production operations
Dr. Luigi Saputelli
To increase the opportunities for profitability, upstream operators have deployed sophisticated hardware for remote sensing and actuation of wells and facilities. However, efficient transformation of data acquisition into value-added actions still a challenge. Assimilation of real time data offer unique advantages in answering crucial questions for improving asset performance. The combination of data-driven predictive analytics, large computer power and multidimensional data visualization propose opportunities for discovering deep knowledge previously hidden by traditional engineering and statistical methods. During the last decades, a number of predictive models, from outside E&P industry, have been adapted to upstream production and real-time data surveillance. Typical real-time production applications include reservoir model response, virtual metering and soft sensing. In this presentation, some success stories using linear time series, neural networks, fuzzy logic and reduced order modeling will be displayed and analyzed. Key challenges and R&D requirements in applying data driven techniques in real-time data analysis will also be addressed.
This webinar is categorized under the Data Science and Engineering Analytics discipline.
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Srikanta Mishra is the Technical Director for Geo-energy Modeling and Analytics at Battelle Memorial Institute, the world's largest independent contract R&D organization. His recent work on multiple applied research and field demonstration projects has focused on physics-based and data-driven modeling for performance prediction and field data interpretation of geological CO2 storage, as well as extensions of reservoir analysis concepts for hydrogen underground storage. Mishra is the recipient of the 2022 Data Science and Engineering Analytics Award and the 2021 Distinguished Member Award from SPE, and also served as SPE Distinguished Lecturer for 2018-19 on the topic of Big Data Analytics. He is the author of 200+ technical publications, one text book and three edited volumes on various aspects of data analysis and modeling for subsurface energy resource management. He holds a BTech degree from IIT(ISM) Dhanbad, an MS degree from The University of Texas at Austin, and a PhD degree from Stanford University – all in Petroleum Engineering.
Dr. Luigi Saputelli
President, Frontender Corporation
Dr. Luigi Saputelli is a petroleum engineer with 24 years' experience in reservoir engineering, field development, production engineering, drilling engineering, production operations and oilfield automation projects. He holds a PhD in Chemical Engineering (2003) from the University of Houston, a M.Sc. in Petroleum Engineering (1996) from Imperial College, London, and a B.Sc. in Electronic Engineering (1990) from Universidad Simon Bolivar, Caracas, Venezuela
Luigi worked as technical lead for the development of the integrated reservoir management framework for ADNOC and its operating companies in the UAE (2014-2015), senior advisor in the North Kuwait Sabriyah-Mauddud, SEK Burgan GC01 and WK Minagish Oolite Kuwait Oil Company Digital Oil field efforts (2012-2014), Production Engineering Senior Advisor in Hess Corporation (2009-2012), Halliburton (2003-2009) where he acted as technical lead of various major projects such as Petrobras Barracuda-Caratinga fields Real Time Operations project (2005-2006), KOC KwIDF Digital Oilfield projects (2012-2014), PDVSA integrated modeling and field exploitation plans for Carito and Orocual fields (2006-2007); he later acted as Production Operations Regional Practice Manager and Field Development Global Practice Manager for Halliburton. He also worked in PDVSA E&P (1990-2001) and acted as a well planning senior advisor, production technologist, reservoir modeling and simulation engineer.
He has worked in several countries such as Venezuela, Argentina, Brazil, Colombia, Saudi Arabia, Kuwait, United Arab Emirates, Nigeria, Thailand, Malaysia, Indonesia, England, Scotland and USA. He is an industry recognized researcher, lecturer, SPE Liaison and member of various SPE committees.
He has published more than 60 industry papers and three patents on applied technologies for reservoir management, real time optimization and production operations.
He is currently the president of Frontender Corporation, and he is currently engaged in various Digital Oilfield implementations around the world.
Dr. Shahab Mohaghegh
West Virginia University and Intelligent Solutions Inc.
Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University and the president and CEO of Intelligent Solutions, Inc. (ISI). He is the director of WVU-LEADS (Laboratory for Engineering Application of Data Science).
Including more than 30 years of research and development in the petroleum engineering application of Artificial Intelligence and Machine Learning, he has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs.
He is a SPE Distinguished Lecturer (2007 and 2020) and has been featured four times as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2005). He is the founder of SPE’s Technical Section dedicated to AI and machine learning (Petroleum Data-Driven Analytics, 2011). He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico (2011) and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).
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