As the deployment of advanced sensor networks continues to grow in sectors within the Industrial IoT, many businesses are recognizing the value in information fusion—the process of providing contextual insight on fast, streaming data and big, static data by enabling metadata analytics. Oil and gas organizations are at the forefront of big data, adopting technologies such as Hadoop and Spark to develop next-generation fusion systems. However, much of seismic analysis involves data formats and algorithms that do not lend themselves to modern parallel architectures. By adopting technology to support a parallel seismic format and a parallel access pattern, a Lambda-compliant framework could provide the ability to leverage all available data and perform more analysis in less time, thereby achieving more accurate scientific results.
To increase profitability of their reservoirs through quantitative fusion of all information, oil and gas organizations look to CGG for software solutions for geophysics, petrophysics and model building. Brian Clark and Marco Ippolito explore the use of data from well sensors and other Industrial IoT devices, deep dive into CGG’s parallel seismic analytic framework based on Objectivity’s ThingSpan, and explain why big data challenges in oil and gas are relevant for all enterprises.
Brian Clark is VP of product management at Objectivity. Brian has nearly 30 years of software and technology experience and was one of the early architects of Objectivity/DB. Before joining Objectivity, Brian worked at Automation Technology Products, providing leading tools in the MCAD market. Prior to that, he was with Project Management Services at International Computers Limited, one of Europe’s leading computer companies at the time. Brian holds a BS degree in computer science from Sheffield University, England.
Marco M. Ippolito is the data model architect for French-based geophysical services company, CGG, Inc., a fully integrated geoscience company providing leading geological, geophysical, and reservoir capabilities to a broad base of customers primarily from the global oil and gas industry. Since joining CGG in 2007, Marc’s focus has been developing scalable, object-oriented data models capable of satisfying the data-management challenges of geoscience applications.
Prior to joining CGG Marc’s primary career focus was real-time virtual-reality simulation—a field also fraught with data-management challenges and performance constraints. Marc’s passion for computers started at an early age and grew out of necessity due to his passion for flight simulation and the ever-growing demands graphics applications place on computing hardware. He has an appreciation for classical computer science problems and is an active contributor to a number of open source projects, where his interests range from home automation to streaming graphics. Marc received a BS in computer science from the University of Texas at Dallas, where he is also currently pursuing a master’s degree in geosciences.
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