Data-Driven Analytics

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This is group of webinars that cover various topics on Data-Driven Analytics.

Session I: Shale Asset Management via Advanced Data-Driven and Predictive Analytics

Session II: Smart Fields: A Data-Driven Approach to Making Oil Fields Smart

Session III: The Game Changing Impact of Data and Data-Driven Solutions in the Upstream Oil and Gas Industry

Session IV: Transforming E&P Applications through Big Data Analytics

Session V: Smart Proxy Modeling for Numerical Reservoir Simulations – Big Data Analytics in E&P

Session VI: Data-Driven (Fact-Based) Reservoir Modeling of Mature Assets

This webinar is categorized under the Data Science and Engineering Analytics discipline.

SPE Webinars are FREE to members courtesy of the

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Dr. Srikanta Mishra

Institute Fellow & Senior Research Leader, Battelle Memorial Institute

Dr. Srikanta Mishra is Institute Fellow & Senior Research Leader at Battelle Memorial Institute, the world's largest independent contract R&D organization. He is responsible for developing and managing a technology portfolio related to reservoir modeling and data analytics for geological carbon storage, shale gas development and improved oil recovery projects. Dr. Mishra is the author of 175+ technical publications, and an Associate Editor of Journal of Petroleum Science & Engineering. He holds a PhD in Petroleum Engineering from Stanford University.

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).

Shale Asset Management via Advanced Data-Driven and Predictive Analytics

Advanced Data-Driven Analytics provides much needed insight into hydraulic fracturing practices in Shale. Unlike analytical and numerical modeling that are based on “Soft Data", Advanced Data-Driven Analytics incorporates “Hard Data". “Hard Data" refers to field measurements (facts) such as drilling information, well logs (Gamma ray, density, sonic, etc.), fluid type and amount, proppant type, amount and concentration, ISIP, breakdown and closure pressures, and rates, while “Soft Data" refers to variables that are interpreted, estimated or guessed (and never measured), such as hydraulic fracture half length, height, width and conductivity or the extent of the Stimulated Reservoir Volume (SRV).

Shahab Mohaghegh
WEST VIRGINIA UNIVERSITY & INTELLIGENT SOLUTIONS, INC.

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds B.S., MS, and PhD degrees in petroleum and natural gas engineering

He has authored more than 150 technical papers and carried out more than 50 projects for NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured, four times, in the Distinguished Author Series of SPE's Journal of Petroleum Technology (JPT). He is the founder of SPE's Petroleum Data-Driven Analytics Technical Section that focuses on the application of AI and data mining in the upstream. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources (2008-2014). He represents the United States in the International Standard Organization (ISO) on Carbon Capture and Storage.

Smart Fields: A Data-Driven Approach to Making Oil Fields Smart

This webinar will explore the challenges that our industry face in turning data into information. Where the bottlenecks are. Where we can look for solutions. How solutions can be implemented, and if we are the only industry that has faced or is facing such challenges.

Shahab Mohaghegh
WEST VIRGINIA UNIVERSITY & INTELLIGENT SOLUTIONS, INC.

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds B.S., MS, and PhD degrees in petroleum and natural gas engineering

He has authored more than 150 technical papers and carried out more than 50 projects for NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured, four times, in the Distinguished Author Series of SPE's Journal of Petroleum Technology (JPT). He is the founder of SPE's Petroleum Data-Driven Analytics Technical Section that focuses on the application of AI and data mining in the upstream. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources (2008-2014). He represents the United States in the International Standard Organization (ISO) on Carbon Capture and Storage.

The Game Changing Impact of Data and Data-Driven Solutions in the Upstream Oil and Gas Industry

The buzz regarding data and what it can do for business today is everywhere. It has captured imaginations. Some imaginations have turned into reality, giving rise to real-time translation tools and self-driving cars. More exciting products will hit the market soon. The excitement that has been stirred by this new trend has made managers and engineers in the oil and gas industry excited.

Autonomous, self-drilling rigs, completely automated, smart completion and hydraulic fracturing, building comprehensive, full field, and fast reservoir models based on facts (field measurements), building smart proxy of complex numerical reservoir simulation models that run in fractions of a second, and coupling subsurface to wellbore and to the surface facilities, in real-time modeling and optimization, are among the rising applications of data driven solutions in the upstream oil and gas industry.

Data-Driven Analytics have already made important contributions to the oil and gas industry. In situations where our understanding of the physics is still in the developmental phase and the number of unknowns are overwhelming (such as production from shale), data-driven analytics have proven to be valuable in understanding the complex nature of the production process, and to help us optimize our completion design and production. Some operators are taking advantage of existing data-driven know-how in the industry while others are contemplating the possibilities, and yet others have gone stray with disappointing outcomes.

The journey of data analytics in our industry has not been without pain and confusion. However, it seems that the long battle against traditionalists that view the pathway of starting with first principle physics in explaining nature more as a religion than a scientific endeavor has been largely victorious. But some still find it hard to move into the new millennium. On the other hand, amongst the enthusiasts of this new technology, the overlap between IT and Engineering and Geo-sciences has caused much confusion. Attempts to clarify such confusion have resulted in two separate SPE Technical Sections each dedicated to certain aspects of including data in our everyday operations. However, latest activities in these Technical Section point to the fact that some confusion still remains.

Large numbers of start-ups, many from areas with minimal historical relevance to the oil industry, are examining their luck and other's investments in the upstream. This has resulted in large number of claims being made with slick marketing techniques. What most of these start-ups seem to lack is actual domain expertise which has proven to be most crucial in successful implementation of data-driven solutions in our industry.

Now operators are asking what their strategy should be, to take maximum advantage of this technology. Should they develop in-house expertise or should they outsource all or part of their data-driven analytics work? Should they develop or buy the required tools? Do they need statistical and visualization tools or comprehensive workflows and solutions? Should they hire statisticians and teach them petroleum engineering, or train their existing petroleum professionals the art and science of machine learning and pattern recognition? Do we need data scientists in our workforce? Do we need “Petroleum Data Scientists," or will anyone with a background in statistics do? What is the definition of a “Petroleum Data Scientist"? The perspective on these issues that this presentation provides has been formed through more than two decades of direct experience in petroleum data-driven analytics research and development.

Speaker

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds B.S., MS, and PhD degrees in petroleum and natural gas engineering

He has authored more than 150 technical papers and carried out more than 50 projects for NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured, four times, in the Distinguished Author Series of SPE's Journal of Petroleum Technology (JPT). He is the founder of SPE's Petroleum Data-Driven Analytics Technical Section that focuses on the application of AI and data mining in the upstream. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources (2008-2014). He represents the United States in the International Standard Organization (ISO) on Carbon Capture and Storage.

Transforming E&P Applications through Big Data Analytics

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.

Speakers

Dr. Srikanta Mishra
Institute Fellow & Senior Research Leader, Battelle Memorial Institute

Dr. Srikanta Mishra is Institute Fellow & Senior Research Leader at Battelle Memorial Institute, the world's largest independent contract R&D organization. He is responsible for developing and managing a technology portfolio related to reservoir modeling and data analytics for geological carbon storage, shale gas development and improved oil recovery projects. Dr. Mishra is the author of 175+ technical publications, and an Associate Editor of Journal of Petroleum Science & Engineering. He holds a PhD in Petroleum Engineering from Stanford University.

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.

Shahab Mohaghegh
WEST VIRGINIA UNIVERSITY & INTELLIGENT SOLUTIONS, INC.

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds B.S., MS, and PhD degrees in petroleum and natural gas engineering

He has authored more than 150 technical papers and carried out more than 50 projects for NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured, four times, in the Distinguished Author Series of SPE's Journal of Petroleum Technology (JPT). He is the founder of SPE's Petroleum Data-Driven Analytics Technical Section that focuses on the application of AI and data mining in the upstream. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources (2008-2014). He represents the United States in the International Standard Organization (ISO) on Carbon Capture and Storage.


Smart Proxy Modeling for Numerical Reservoir Simulations – Big Data Analytics in E&P

Computational science, addressing numerical solution to complex multi-physic, non-linear, partial differential equations, is at the forefront of engineering problem solving and optimization. Numerical Reservoir simulation, the application of computational science in petroleum engineering, is computationally expensive. Proxy models (statistical response surfaces, or reduced physics) attempt to make it practical to use the simulation models for field development planning and uncertainty quantification by addressing their computational footprint (with limited success rate).

Data-Driven Smart proxies take advantage of the “Big Data" solutions (machine learning and pattern recognition) to develop highly accurate replicas of numerical models with very fast response time. The novelty of Smart Proxy Modeling stems from the fact that it is a complete departure from traditional approaches to modeling in the oil and gas industry and constitutes a major advancement in utilization and incorporation of Big Data solution in the E&P industry.

Instead of starting with first principle physics, smart proxies are models that are built based on observation of system behavior, through data, much like how human brain learns. Just imagine that a single run of a one-million grid block reservoir simulation model that includes 100 time-steps will generate 1,000,000 x 100 = 1x108 examples of pressure and saturation changes at the grid block level to learn from. Furthermore, only by making 10 simulation runs, the number of training examples will increase to a billion records. A large amount of information and knowledgeis embedded in this one billion example of how pressure and saturation in a reservoir changes as a function of initial and boundary conditions as well as a function of all other static and dynamic characteristics of the reservoir being modeled. Surrogate Reservoir Model (SRM) is the smart proxy of numerical reservoir simulation.

This web event includes:

Introduction to Big Data Analytics in E&P

Introduction to Numerical Reservoir Simulation

Description of Smart Proxy Model

Surrogate Reservoir Model (Smart Proxy of Reservoir Simulations)

Case Studies (Production optimization in Carbonates, CO2 Storage, History Matching)


Speaker

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds B.S., MS, and PhD degrees in petroleum and natural gas engineering

He has authored more than 150 technical papers and carried out more than 50 projects for NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured, four times, in the Distinguished Author Series of SPE's Journal of Petroleum Technology (JPT). He is the founder of SPE's Petroleum Data-Driven Analytics Technical Section that focuses on the application of AI and data mining in the upstream. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources (2008-2014). He represents the United States in the International Standard Organization (ISO) on Carbon Capture and Storage.

In this webinar a novel approach to reservoir modeling that is based on measured data is presented. This technology that has been named “Top-Down Modeling – TDM" integrates fundamentals of reservoir and production engineering with latest advances in machine learning and predictive analytics. It is a formalized, comprehensive, empirical, and multi-variant, reservoir model, developed solely based on field measurements (logs, cores, well tests, seismic, etc.) and historical production/injection data. Presented by Shahab D. Mohaghegh

Speaker

Shahab D. Mohaghegh is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. A pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, he holds B.S., MS, and PhD degrees in petroleum and natural gas engineering.He has authored more than 150 technical papers and carried out more than 50 projects many of them with major international companies. He is a SPE Distinguished Lecturer and has been featured in the Distinguished Author Series of SPE's Journal of Petroleum Technology (JPT) four times. He is the program chair of Petroleum Data-Driven Analytics, SPE's Technical Section dedicated to data mining. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and is a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources. Shahab is the designated U.S. liaison (WG4) representing the Unites States in the International Organization for Standardization (ISO) for CO2 capture and storage.

SPE Webinars are FREE to members courtesy of the

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Shale Asset Management via Advanced Data-Driven and Predictive Analytics
Recorded 11/12/2013
Recorded 11/12/2013 90 Minutes
Certificate CEU
0.15 CEUs credits  |  Certificate available
0.15 CEUs credits  |  Certificate available Shale Asset Management via Advanced Data-Driven and Predictive Analytics
Smart Fields: A Data-Driven Approach to Making Oil Fields Smart
Recorded 09/09/2013
Recorded 09/09/2013 60 Minutes
CEU's
0.15 CEUs credits  |  Certificate available
0.15 CEUs credits  |  Certificate available CEUs offered.
The Game Changing Impact of Data and Data-Driven Solutions in the Upstream Oil and Gas Industry
Recorded 11/23/2015
Recorded 11/23/2015 Scheduled for 90 minutes.
CEU Credit
0.15 CEU credits  |  Certificate available
0.15 CEU credits  |  Certificate available CEU Credit
Transforming E&P Applications through Big Data Analytics
Recorded 07/09/2015
Recorded 07/09/2015 Scheduled for 90 minutes.
CEU Credit
0.15 CEU credits  |  Certificate available
0.15 CEU credits  |  Certificate available CEU Credit
Smart Proxy Modeling for Numerical Reservoir Simulations – Big Data Analytics in E&P
Recorded 10/06/2015
Recorded 10/06/2015 Scheduled for 90 minutes.
CEU Credit
0.15 CEU credits  |  Certificate available
0.15 CEU credits  |  Certificate available CEU Credit
Data-Driven (Fact-Based) Reservoir Modeling of Mature Assets
Recorded 02/21/2016
Recorded 02/21/2016 90 Minutes
CEUs
0.15 CEUs credits  |  Certificate available
0.15 CEUs credits  |  Certificate available CEUs offered