How the oil and gas industry is igniting a spark with information fusion and 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. Brian Clark and Marco Ippolito introduce a case study from CGG, a builder of common data models to drive analytics of sensor data and associated metadata from fast-changing big data streams, to show how to derive richer value from big data assets.

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.