Massively parallel electrical-conductivity imaging of hydrocarbons using the IBM Blue Gene/L supercomputer

Abstract

Large-scale controlled-source electromagnetic (CSEM) threedimensional (3D) geophysical imaging is now receiving considerable attention for electrical-conductivity mapping of potential offshore oil and gas reservoirs. To cope with the typically large computational requirements of the 3D CSEM imaging problem, our strategies exploit computational parallelism and optimized finite-difference meshing. We report on an imaging experiment utilizing 32,768 tasks (and processors) on the IBM Blue Gene/Le (BG/L) supercomputer at the IBM T. J. Watson Research Center. Over a 24-hour period, we were able to image a large-scale marine CSEM field dataset that previously required more than 4 months of computing time on distributed clusters utilizing 1,024 tasks on an InfiniBandt fabric. The total initial data-fitting errors (i.e., ''misfits'' ) could be decreased by 67% within 72 completed inversion iterations, indicating the existence of an electrically resistive region in the southern survey area below a depth of 1,500 m underneath the seafloor. The major part of the residual misfit stems from transmitter-parallel receiver components that have an offset from the transmitter sail line (broadside configuration). Modeling confirms that improved broadside data fits can be achieved by considering anisotropic electrical conductivities. While delivering a satisfactory gross-scale image for the depths of interest, the experiment provides important evidence for the necessity of discriminating between horizontal and vertical conductivities for maximally consistent 3D CSEM inversions.

Marine Electromagnetic Studies of Seafloor Resources and Tectonics

“…Bayesian inversion approaches, while demanding a vastly longer computational effort when compared to traditional gradientbased inversion methods, offer promise since they provide a measure of the model uncertainty that could be used to gauge the likelihood of a given layer's conductivity. Gradient-based 3D inversion of a full marine EM data set poses a severe computational challenge, as illustrated by Commer et al (2008) using 32,768 processors on the IBM Blue Gene/L supercomputer to conduct a 3D inversion of marine CSEM data from a large grid of transmitters and receivers. Commer and Newman (2009) use synthetic studies to show that joint inversion of both CSEM and MT data offers better resolution of a reservoir and nearby salt body than possible when inverting either data set alone.…”

Parallel PPI Prediction Performance Study on HPC Platforms

“…37 the Cell BE to the Blue Gene/L on two of the most popular image processing kernels, image resizing, and edge detection. We have developed a parallel HMM algorithm in Ref.…”

A look inside the earth

“…A similar strategy can be used for solving wave propagation problems with explicit methods that exploit some type of time stepping scheme. Commer et al [4] report one demonstration of the efficiencies gained with such distributed computations, where 32,768 computing tasks (compute cores) were employed on the IBM Blue Gene Machine to solve a 3-D EM imaging problem. While this application, at the time, required enormous resources to execute, it clearly demonstrated 3-D imaging problems could be solved in days, rather than weeks, physical imaging software on GPUs show encouraging cost versus performance metrics compared to MIMD architectures, where cost includes not only money but also time.…”