Shell Is Using MATLAB to Improve Geomatics and Automatic Tag Detection of Panoramic Images - MATLAB
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      Shell Is Using MATLAB to Improve Geomatics and Automatic Tag Detection of Panoramic Images

      James Martin, Shell International

      With minimal setup, MATLAB Parallel Server™ allows the team to train networks on multiple remote GPUs in the cloud. MATLAB Production Server™ lets the team create thin web clients that operators in the field can use—with minimal physical hardware, such as a smartphone.

      Published: 22 Dec 2020

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      Advanced analytics is playing an increasingly important role, specifically within MATLAB. We're leveraging upon some of the deep learning tools to improve our innovation pipeline. So these are active areas where analytics is playing a leading role within our organization.

      In upstream, in exploration, seismic data is one of the most important technologies that we have in order to look underneath the ground in the subsurface. And the cost of acquiring data-- so putting in energy into the ground and receiving it-- is very high. So we're talking tens of millions per year, per survey. So they pay for a highly specialized, well-paid individual to look at satellite images and manually draw polygons around rough terrain, what they think is rough terrain.

      So in our case, because we now have lots of training data, we thought perhaps we can replace the whole workflow with something a bit more computer intensive. So we decided to try this semantic segmentation approach.

      So these are the results. I've gone from the color and decomposed it back into the original images. So on the top, you can see, on the left, the aerial photography and then the radar and the DSM. And then, on the bottom on the left, you can see the human-- or the ground truth, in our case-- and then what the algorithm predicted.

      And we showed this to the end customers, and, already, they essentially think that the performance is superior to the existing workflow.

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