APPLICATION OF PARALLEL PROCESSING - A CASE STUDY ON OIL EXPLORATION Siti Nurhidayah binti Zul #1, Mohamed Faidz Mohamed Said #2 # Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA 70300 Seremban, Negeri Sembilan, MALAYSIA 1 sitinurhidayahzul9@gmail.com 2 faidzms@ieee.org Abstract—The oil and gas industry is certainly never out of the technology business. Since both oil and gas is not far from production problems and increased reserves, they are synonymous with technology. Without adequate technology, it would not be possible for oil and gas to be drained properly. Especially with the characteristics of national oil blocks that are already old. The Enhance Oil Recovery or EOR is a term commonly used in oil and gas exploration. However, it is one of the incorrect technologies used to maximize the rate of dewatering. Not only the technology requires a lot of investment, it is also dangerous to the environmental health since EOR is known to pump out large amounts of toxic water to the surface thus contributes to the natural pollution. Oil exploration is defined as a research done by an expert in geology to discover the oil and gas underneath the earth. To enhance the process of exploring the oil and natural gas, several methods of parallel processing is utilized in order to increase the manufacture of oil while decrease the pollution made during the exploration process. Keyword: oil, electromagnetic, HPC, CSEM, MPD REFERENCES [1] Wikipedia. "Hydrocarbon Exploration." https://en.wikipedia.org/wiki/Hydrocarbon_exploration (accessed June 11, 2017). [2] S. Constable, "Ten years of marine CSEM for hydrocarbon exploration," Geophysics, vol. 75, no. 5, pp. 75A67-75A81, 2010. [3] B. Thander, A. Sircar, and G. P. Karmakar, "Hydrocarbon Resource Estimation: A Stochastic Approach," Journal of Petroleum Exploration and Production Technology, vol. 5, no. 4, pp. 445-452, 2015. [4] P. Sundararajan, "High performance computing using FPGAs," Xilinx White Paper: FPGAs, pp. 1-15, 2010. [5] H. M. Bücker, A. I. Kauerauf, and A. Rasch, "A Smooth Transition from Serial to Parallel Processing in The Industrial Petroleum System Modeling Package PetroMod," Computers and Geosciences, vol. 34, no. 11, pp. 1473-1479, 2008. [6] T. Finance, "Using Genetic Programming to evolve Trading Strategies," n.d. [7] M. Commer et al., "Massively Parallel Electrical-Conductivity Imaging of Hydrocarbons using The IBM Blue Gene/L Supercomputer," IBM Journal of Research and Development, vol. 52, no. 1.2, pp. 93-103, 2008. [8] Z. He, W. Hu, and W. Dong, "Petroleum electromagnetic prospecting advances and case studies in China," Surveys in Geophysics, vol. 31, no. 2, pp. 207-224, 2010. [9] K. Key and J. Ovall, "A Parallel Goal-Oriented Adaptive Finite Element Method for 2.5-D Electromagnetic Modelling," Geophysical Journal International, vol. 186, no. 1, pp. 137-154, 2011. [10] A. J. Plaza, "Parallel Techniques for Information Extraction from Hyperspectral Imagery Using Heterogeneous Networks of Workstations," Journal of Parallel and Distributed Computing, vol. 68, no. 1, pp. 93-111, 2008. [11] M. Nikolaou, "Computer-Aided Process Engineering in Oil and Gas Production," Computers & Chemical Engineering, vol. 51, pp. 96-101, 2013. [12] F. A. Anifowose, J. Labadin, and A. Abdulraheem, "Hybrid Intelligent Systems in Petroleum Reservoir Characterization and Modeling: The Journey So Far and The Challenges Ahead," Journal of Petroleum Exploration and Production Technology, vol. 7, no. 1, pp. 251-263, 2017. [13] F. c. J. L. Ribeiro, A. de Castro Pinto Pedroza, and L. s. H. M. K. Costa, "Underwater Monitoring System for Oil Exploration using Acoustic Sensor Networks," Telecommunication Systems : Modelling, Analysis, Design and Management, vol. 58, no. 1, pp. 91-106, 2015. [14] A. Jernelöv, "The threats from oil spills: Now, then, and in the future," AMBIO: A Journal of the Human Environment, vol. 39, no. 6, pp. 353-366, 2010. [15] L. A. Clevenger, H. Eng, K. Khan, J. Maghsoudi, and M. Reid, "Parallel Computing Hardware and Software Architectures for High Performance Computing," May 1, 2015 2015. [16] O. Lindtjorn, R. Clapp, O. Pell, H. Fu, M. Flynn, and O. Mencer, "Beyond traditional microprocessors for geoscience high-performance computing applications," Ieee Micro, vol. 31, no. 2, pp. 41-49, 2011.