The oil and gas industry is increasingly striving to “produce in context” rather than “produce at all costs”, with the help of machine learning and AI. This workshop will move beyond the buzzword and demonstrate where machine learning can be applied to upstream activity to improve performance, optimize processes and inform new business models. It will walk you through practical examples of how tools and models are being utilized to uncover and solve business problems. Join this workshop to familiarize yourself with machine learning techniques to identify patterns and
improve your analysis of performance.
Jaijith Sreekantan, Senior Data Scientist, Schlumberger
With the possibilities becoming endless for predictive analytics to improve performance, reduce downtime and minimize risks, engineers must understand the fundamentals of this process to leverage its potential. Building predictive models requires knowledge of your datasets, the context in which it will be applied and your IT capabilities. This session will take you through the model building processes to understand what’s required for data cleansing, algorithm selection, pattern identification, breakage predictions, tool optimization and successful integration into current processes. This workshop will draw on use cases to demonstrate how analytics can identify key drivers for performance change, learning what they look like, predicting how machinery and tools will behave and finally implementing methodology to react quicker to such changes.
Ian McNair, Enterprise Architect – Big Data Data Science, BP
Meena Thandavarayan, Enterprise Architect - Data and Analytics , BP
Meena Thandavarayan is an adaptive and creative Information Management leader passionate about delivering human-centered experiences. His focus is on designing and delivering data driven business solutions to enable organizations derive value from their data assets – Information Products, Machine Learning and Artificial Intelligence.
In his role at BP, Meena is responsible for the defining the strategy and architecture of BP’s operational and analytical environments working closely with Business, IT and Industry leading product vendors. Prior to BP, Meena was a practice lead for AI and Automation with Infosys delivering Fluid and transformational AI and Data science platforms for fortune 500 clients.
Meena is a connector, design thinker and an active speaker in international conferences. His passion is coaching, giving back to the community. He spends his weekends helping Middle and High school kids with SAT preparation, Programming and Junior Achievement.
Real-time data is proving extremely valuable in the role it plays catching operational problems early on, increasing efficacy and safety and responding to real-world demand fluctuations. This workshop will explore how to apply real-time data processing to your wider analytics program to inform new business decisions. It will look at the capabilities and compare why it can be more useful than historical data for certain analyses. You will learn what are the appropriate tools and how to use them in certain scenarios through a practical activity.
Jay Hollingsworth, Chief Technology Officer, Energistics
Jay Hollingsworth is Chief Technology Officer for Energistics®. In this role, he is responsible for the technical adequacy of the standards stewarded by the organization, including WITSML™, PRODML™, and RESQML™ among others.
Jay has a BS plus post-graduate studies in Chemical Engineering at Tulane University in New Orleans. In addition, he attended graduate school in Computer Science at University of Texas in Dallas. As his career advanced as an Environmental and Process Engineer, he focused on technical computing – first as a consultant and then for 20 years at Mobil Oil. At Mobil he was responsible for the data model of their FINDER global master data store and the suite of engineering applications in global use. After leaving ExxonMobil, he spent time in Landmark’s data modeling group before settling at Schlumberger. He spent 10 years at Schlumberger where he was responsible for the data modeling group and was the Portfolio manager for the Seabed database technology. After Schlumberger, he was an Industry Principal at Oracle®, focusing on oil & gas solutions.
Jay is active in numerous industry organizations, including APSG, ISO, SPE and SEG. He was a Technical Editor of the SPE Microcomputer Journal and is currently on the Board of the SPE Digital Energy Technical Section. He was a long-time member of the Board of Directors of PPDM™ and served as past president of APSG.