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Data-driven automated earth model building (FWI) from raw exploration seismic data

Data-driven automated earth model building (FWI) from raw exploration seismic data

Interview with Dr Nikil Shah, S-Cube


Technology innovators S-Cube launched in 2015 to advance data-driven automated earth model building (FWI) from raw exploration seismic data. Their XWI™ algorithm is unlocking the potential of untapped deep-sea deposits whilst reducing risk for the industry. Founder Dr Nikil Shah won Young Entrepreneur of the Year at the Asian Business Awards and discusses S-Cube’s AI technology.

Can you tell us about S-Cube’s AI technology and how it works?

The S-Cube Cloud is home of the XWI™ algorithm – a new sensor predictive analytics solution for seismic field data.  Using advanced search and adaptive machine learning optimisation technology, it transforms seismic data and provides the best possible 3D image of the interior of the earth to uncover new resources and define drilling paths with greater unprecedented precision.

Further, it has the predictive power to modify existing rock property predictions and shift them in the direction of known values at previously drilled locations, improving alignment by up to 90%, using nothing but surface seismic data.

A by-product of running on the cloud is the launch of the first ever ‘Pay as You Go’ cloud user model for the energy sector available on demand via the Amazon Web Services cloud and piloted with Tullow Oil.

What are the key benefits of this technology for the energy industry?

The XWI™ provides the ability to view the interior of the earth with superior definition, focus, accuracy, and certainty.

Our models have been tested against the wellbore measurements and demonstrated the capability to provide accurate predictions that align with the downhole measurements up to 4 km deep. Below 2 km is generally where standard techniques go wayward whilst with XWI™ continues to maintain accuracy.

This means we are able to significantly reduce risk and uncertainty in identifying untapped deep-sea deposits and help exploration and production operators achieve greater efficiencies. With a robust 3D model, they can reduce drilling risk, avoid costly dry holes and maximise recovery. All of this can also be done at a faster rate as we have reduced model building time to 3 weeks from the standard 9-12 months.

How does this technology help to reduce costs?

Essentially, the XWI™ gives energy firms the ability to make drilling decisions quicker and with more certainty. We all know that time is money, and in this case, our solution helps ensure that no time is lost – whether that would be on drilling in the wrong locations or by going through lengthy decision processes.

The XWI™ framework can scale to thousands of square kilometres whilst retaining iteration cycle times to minutes, rather than hours or days. Further, it can focus in on key areas where higher definition is required.

Tell us about your recent achievement, being named Young Entrepreneur of the Year at the Asian Business Awards?

This was a very proud moment. I have long held a vision to spark change by embedding and unlocking the potential of artificial intelligence in the context of a capital-intensive industry and it fits directly with the country’s digitalisation strategy for the industry as iterated on the night by Phillip Hammond.  

The award came in recognition of our success in theorising, patenting and implementing the Adaptive Waveform Inversion (AWI™) oilfield sensor technology.  The smart optimisation cost function is integrated within the XWI™ framework and gives an adaptive way of scoring the predictions using a mathematical mapping between field data and model predicted data. AWI thus generates more accurate model updates when starting further from the truth. It was the breakthrough on which we founded S-Cube.

What are S-Cube’s plans for the future? Will the company be using artificial intelligence to develop further technology?

Yes, we are not stopping at this. The ultimate goal for us is to deliver a solution that will give global energy companies the ability to make real-time decisions while a seismic survey is being conducted. We strive to use the technology to make sure that our customers can optimise production, improve decision making, enhance safety, reduce costs and – ultimately – deliver a superior return on investment. The applications of AI in this sector are endless and we want to lead the charge towards greater automation and accuracy from upstream to downstream to carbon storage.

Published: 26-07-2019
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