MODELLING AND SIMULATION
“There is greater focus and investment on more accurate methods of simulation for improved reservoir recovery rates, and in ways to reduce carbon emissions”
and in ways to reduce carbon emissions. The industry is investing in renewables and our initiatives in AI are relevant to all energy production sources and their value chains, from upstream oil and gas, power generation, production, distribution and even retail use cases.’ Nvidia recently worked with IBM to jointly develop the Pangea III 28-Pflop supercomputer, which will be used by energy company Total to ‘more accurately locate new resources and better assess
www.scientific-computing.com | @scwmagazine
potential revenue opportunities,’ according to a statement. The nodes are based on the IBM
Power AC922 node architecture with six Nvidia Tesla V100 Tensor Core GPUs per node and two IBM Power9 processors each. With more than 3,000 Tesla V100s providing more than 90 per cent of the compute power, Total will be able to process the vast amount of data required in seismic modelling to get more accurate insights, faster than ever before.
Machine learning This need for computational speed is widespread and revolutionising the way many experts in the oil and gas sector now work, with automation taking a more dominant role in the market. Haidari said: ‘Oil and gas companies are
increasingly relying on digital technology to improve their operational efficiency, reduce manpower through automation
and developing autonomous systems for drilling, offshore platforms and increasing use of remotely operated systems and equipment for inspection.’ EPCC, the supercomputing and
data-services centre based at the University of Edinburgh, is collaborating with geoscience consulting firm Rock Solid Images (RSI) on one such machine learning program, to reduce exploration drilling risk for oil and gas companies. Nick Brown, a research fellow at EPCC,
said: ‘There is a wealth of subsurface data collected by borehole drilling, but as this is real-world data for each specific well, the data itself is noisy and bits can be missing. Also, the petrophysicists aren’t necessarily interested in the raw data from the tool, but instead higher-level information, such as mineralogy composition, porosity of the rock and fluid saturation.’ It currently takes more than seven days for one expert petrophysicist to manually g
August/September 2019 Scientific Computing World 25
S.Wongpetsakun/
Shutterstock.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32
