WorldWide Drilling Resource

! " # ! Exascale Computing in the Energy Sector Adapted from Information by HPC4E Exascale computing refers to computing systems capable of a billion billion calculations per second. The HPC4E project aims to apply exascale high-performance computing techniques to energy industry simulations. The project customizes and innovates the techniques in the required exascale simulations with wind energy production and design, efficient combustion systems for biomass-derived fuels (biogas), and exploration geophysics for hydrocarbon reservoirs. For the wind industry, high-performance computing is a must. The competitiveness of wind farms can only be guaranteed with an accurate wind resource assessment, farm design, and short-term wind simulations to forecast the daily power produc- tion. Models capturing turbine wakes, and array effects used to analyze atmospheric flow also require exascale systems. Biogas is attractive because of its wide availability, renewability, and reduction of carbon dioxide emissions. However, its use in prac- tical systems is limited because the complex fuel composition might lead to unpredictable combustion performance and instabilities in in- dustrial combustors. The next generation of exascale systems will run combustion simulations in parameter regimes relevant to indus- trial applications using alternative fuels, which is required to design efficient furnaces, engines, and power plants. One of the main high-performance computing consumers is the gas and oil industry. The computational requirements arising from full wave-form modelling, as well as seismic and electro- magnetic data is causing the industry to adopt exascale computing technologies. By taking into account the complete physics of waves in the subsurface, imaging tools are able to reveal information about the earth’s interior with great quality. Geophysical exploration for finding and monitoring hydrocarbon reservoirs relies heavily on processing large amounts of data. The computational intensity associated with current data processing tools makes geophysical imaging a challenge. The huge cost related to data acquisition and drilling in challenging locations is being counterbalanced by using cheaper computing infrastructures. This is expected to cause companies to buy their own computing systems for geophysical imaging and reservoir modelling. Accurate imaging of the subsurface helps reduce geological uncertainty, which reduces drilling failure rates, making the cost of a drilling project a far less risky investment. Furthermore, the environmental consequences of drilling are reduced since each bore is more likely to hit the reservoir. 50 APRIL 2018 WorldWide Drilling Resource ®