Distributed computing of Seismic Imaging Algorithms

Abstract: The primary use of technical computing in the oil and gas industries is for seismic imaging of the earth's subsurface, driven by the business need for making well-informed drilling decisions during petroleum exploration and production. Since each oil/gas well in exploration areas costs several tens of millions of dollars, producing high-quality seismic images in a reasonable time can significantly reduce the risk of drilling a "dry hole". Similarly, these images are important as they can improve the position of wells in a billion-dollar producing oil field. However seismic imaging is very data- and compute-intensive which needs to process terabytes of data and require Gflop-years of computation (using "flop" to mean floating point operation per second). Due to the data/computing intensive nature of seismic imaging, parallel computing are used to process data to reduce the time compilation. With introducing of Cloud computing, MapReduce programming model has been attracted a lot of attention in parallel and distributed systems [1, 2] to execute massive processing algorithms such as Bioinformatics[3], Astronomy[4], Geology[5] and so on. In this report, we will investigate and discuss current approaches to fit seismic algorithms to MapReduce programming model.