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How Woodside is using Watson machine learning to predict itself out of catastrophes

Nadia Cameron | April 22, 2016
Woodside is running the largest commercial instance of the Watson advisor engine.

“This would give engineers time to go and investigate potential issues,” Jordan said. “More than just predictions, the model also actually captured vast knowledge across 20 years of running these systems, then allowed us to transfer this knowledge to other facilities so we could prevent these events in those location, too.”

This not only requires the ability to churn through data, but also to keep analysing fresh streaming data to keep on predicting on a 10-minute or hourly basis the likelihood of an event. Streaming data adds about 10GB of data to Woodside’s data lake per day, while calculations go into the millions.

Woodside is using AWS Big Data platform to do this, which Jordan said can scale quickly and cost effectively.

“We need to be at the cutting edge of technology for scale and to increase our performance,” she said.

“We’re an early adopter of Apache Spark technology, and have open source analytical toolkits running including R. We needed to be able to quickly test out and adopt data science technologies so we can be at the front end of the curve.”

Bringing in Watson

Woodside has also brought on Watson technology to help better tap the wealth of expertise, insight and knowledge across the organisation and in its data stores. The machine learning engine is being used to analyse about 200 million pages of technical documents and reports.

Jordan said engineers can ask Watson questions, and it comes back with answer based on searching these documents. Most importantly, an experienced engineer takes that answer, looks at whether it’s correct or relevant, and feeds back intelligence into the system, improving the accuracy of results for future reference.

“Over time, and as you feed back in responses and feedback, it becomes more self-reliant and accurate,” Jordan said. “That’s what makes Watson fundamentally different to other types of search engines.”

In one example, Watson worked with a Woodside engineer designing a new offshore platform and concerned with how to manage seabeds loosening. Rather than having to scroll through thousands of potentially relevant documents, or having to find the one individual in the business with historical knowledge on these issues, the engineer used Watson and within seconds had relevant information to act on.

“Watson wasn’t trying to answer the question, and the main focus of the document wasn’t necessarily about the specific problem we were trying to solve, it was just one of the subsections,” Jordan stressed. “But that timesaving for our engineer in finding the answer he needed was huge.”

Jordan also noted Watson doesn’t give an opinion, it gives factual answers back based on information found within the reports it can find.


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