Landmark has sold its first ‘Rapid Prospect Generation Engine’ (RPGE) to Polish geophysical company Geofizyka Torun (GT). The RPGE, introduced at the EAGE earlier this year (see OITJ Vol. 9 N°s 7&8) is a hardware and software solution from Landmark, IBM and Network Appliance. Key components of GT’s solution include IBM’s 32-node (64-CPU) eServer Linux Cluster 1350, NetApp’s FAS270 data storage filer, Landmark’s ProMAX 3D, ProMANAGER and SeisSpace applications and installation, workflow consulting and training.
Jakubowski
GT center manager Adam Jakubowski said, ‘The rapid increase in demand for seismic data processing services required us to expand the Centre’s existing processing power. We needed a solution that was both cost-effective and easy to adapt to constantly changing market needs. We chose the RPGE because the software and hardware had undergone sufficient testing to guarantee faultless operation.”
Cost advantage
GT reports that the cost of the cluster is one third that of its most powerful Unix server (a 16-CPU system purchased in 2001), while the speed is ‘six to seven times faster.’ Overall, the Linux system benefits from a ‘twenty-fold’ price/performance advantage over the older machine, according to GT. A 450 square kilometer 3D pre-stack time migration project that previously required three months to process on the Unix system now takes around two weeks.
Field development engine
Landmark is now offering a similar pre-configured system for field development planning, the Field Development Engine (FDE). The FDE was developed in partnership with IBM and United Devices to support parallel and grid-enabled high performance computing. United Devices’ Grid MP technology, and Landmark’s reservoir management DecisionSpace and Parallel-VIP will be bundled with the hardware.
Evans
Landmark product manager Scot Evans, said, ‘Grid MP creates a single, unified system by virtually sharing compute resources and data storage systems across an enterprise or workgroup. The grid-ready FDE allows customers to expand their cluster into a grid, leveraging additional networked nodes during periods of peak demand. This maximizes the value of all IT resources, and achieves faster scenario and reservoir simulations.’
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