MODELLING AND SIMULATION
Geomechanics director for mechanical earth modelling g
Comsol recently modelled corrosion of a deep-water pipeline system, which is a highly non-linear case at large scale. The project involved electrochemistry modelling with external currents, pipe coatings and galvanic cathodic protection. Cai said: ‘The major challenge comes
from the geometric dimension/scales of the physics system. Pipelines are hundreds of kilometres long in a 3D water environment. One has to capture as much detail as possible in the variation of the applied current and electrochemistry over-potential, in order to capture/ simulate possible failure in the system, as well as proposing preventive solutions. This poses great challenges to the
computational cost and convergence of the numerical simulation,’ he added. The Pipeline Flow Module and
infinite element domain were used to simplify the most expensive part of the numerical problem, which includes the pipeline systems and the far-field water environment, into a lower-dimensional calculation without sacrificing the accuracy of the result.
Human first While machine-learning techniques start to gain a foothold in the market, there are human challenges to address too. Cai explained: ‘We have observed a great number of scientists and engineers are
“It is one thing for the products to have the ability to solve these massive problems, and another to be able to solve them quickly”
The porosity for a specific well by depth is shown in blue, the middle plot in red is the prediction by computational model. On the right is a histogram of the relative difference between the truth and prediction
28 Scientific Computing World August/September 2019
The saturation of water by depth for the same well, again on the left is the manually interpreted truth value and on the right is the prediction from our model, sight unseen for this well
leaving their jobs due to budget issues in traditional exploration and production. Talents are also attracted to trending sectors, such as internet companies, which slows down the utilisation of latest technologies in traditional operations.’ This, in turn, adversely affects the oil and gas industry’s drive for cost efficiency. Cai added: ‘Regarding numerical simulation, the frequent movement of modelling experts and engineers affects the usage of established models and simulations negatively, and the high turn-over rate leads to higher cost regarding training and development of newer models.’ However, somewhat counterintuitively, machine learning could help address this human issue. Automation frees experts from time-consuming and mundane tasks to find new ways of working, as we have seen with the EPCC project. As a result, the oil and gas industry could benefit not only from increased efficiencies but also from the development of exciting, new frontiers, in the real world and in the simulation and modelling domain.
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EPCC
EPCC
Altair
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