Oil and Gas Exploration Using HPC Storage

White Papers > Lustre > Oil and Gas Exploration Using HPC Storage

Finding oil and gas has always been a tricky proposition, given that reserves are primarily hidden underground, and often as not, under the ocean as well. The costs involved in acquiring rights to a site, drilling the wells, and operating them are considerable and has driven the industry to adopt advanced technologies for locating the most promising sites. As a consequence, oil and gas exploration today is essentially an exercise in scientific visualization and modeling, employing some of most advanced computational technologies available.

Like all science, oil and gas exploration begins with data collection. Initiating a comprehensive seismic data processing workflow relies on underground imagery for locating and simulating reservoirs, which requires enormous volumes of data collected at the prospective remote drilling site. Gaining more refined images yields more accurate knowledge about the nature of underground structures, but requires more processing power and more capable storage systems. This has led to a demand for collecting, processing and synthesizing analysis on ever more data, as the industry has successively moved from 2D, to 3D to time-lapsed 3D (4D) images.

Oil and gas producers are not just looking for more accurate results, but delivering them in shorter timeframes. Expediting hydrocarbon recovery, for example, is directly tied to ROI and revenue within some of the most profitable companies in the world. The natural progression of computational power, thanks to Moore’s Law and cluster computing scaling, has generally kept pace with the needs of reservoir modeling and seismic analysis. But data ingest and storage capacity needs have been more challenging. As seismic data sets grow to multiple tens of petabytes and ingest rates reach multiple tens to hundreds of gigabytes per second, legacy storage subsystems often struggle to keep up.

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