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Oil majors are getting wise to the possibilities of machine learning, helping them manage risk more efficiently and with less subsea inspection. That translates to the potential for substantial efficiency gains and cost savings across the industry, Kieran Kavanagh, Innovation and Data Analytics Director at Wood Group, said.

In April, French oil major Total announced a tie-up with Google Cloud to jointly develop AI solutions for subsurface data analysis in oil and gas E&P efforts. Under the partnership, Total geoscientists will work side-by-side with Google Cloud’s machine learning experts within the same project team based in Google Cloud’s Advanced Solutions Lab in California.

The move reveals heavyweight backing for machine learning in the oil and gas industry, with Total teams set to explore multiple machine learning applications, including production profile forecasting, automated analysis of satellite images, and analysis of rock sample images.

Total has already used machine learning algorithms to implement predictive maintenance for turbines, pumps and compressors at its industrial facilities in 2013.

The new joint initiative with Google takes the partnership further, and alludes to the growing interpenetration of AI businesses and big oil; Google Cloud’s CEO Diane Greene has an oil and gas background, having worked with oilfield services company Schlumberger on its DELFI software environment, which leverages digital technologies including machine learning to improve operational efficiency and deliver optimized production at the lowest cost per barrel.

Machine learning’s growth reflects the industry’s migration towards techniques that make use of modern computing power’s ability to store and process sizeable data sets. Best understood as a type of AI, machine learning uses algorithms to draw conclusions through the study of these data sets.

Well-placed industry figures are point out the benefits of such technologies. “The thing about it is that typically it allows you to have the ability to learn from experience (data); you look at what’s happened in the past, and you learn from it,” Kieran Kavanagh, Innovation and Data Analytics Director at Wood Group, an energy services company headquartered in Aberdeen, told Upstream Intelligence.

Supervized and unsupervized

There are two main types of machine learning – supervized and unsupervized. Supervized is when you know what “bad” looks like; that is there is a sufficient “training set” to tell a computer how to recognize patterns in data.

“Say, for example, you have experienced some degradation or failure of a piece of equipment or component in the past. You can then look at the history of monitored data relating to that component (for example temperature or vibration information), to see if the measured data can be related to the onset of degradation,” says Kavanagh.

Put simply, if you know what poor behaviour looks like, you can go and identify that and teach the machine – introducing an algorithm to recognize data patterns that you have seen before.

Kavanagh cited an example: if there's a time trace of measured temperature or pressure at a well, and a certain behaviour patterns in this data is observed, this  could be indicative of the start of degradation or failure. “You can now say that each time you see that behaviour again, it may be indicative of degradation or failure. With a good history of measured data, linked to known (tagged) events, we can use this ‘training’ data from the past to predict the future.”

Caption: Valve signature machine learning – identifying characteristics of healthy or degrading vales.

Source: Wood Group

Unsupervized machine learning is when it may not be known what degradation or failure look like, but there is a view as to what non-normal looks like.

Technology providers are now actively marketing machine learning products tailored for the oil and gas industry. Switzerland-based Akselos, a digital twin developer, it is attempting empower predictive maintenance with machine learning technologies.

Thomas Leurent, Akselos’ Chief Executive told Upstream Intelligence that Akselos an active R&D programme to enhance machine learning, and make it part of the technology ecosystem.

“We use a highly accurate and operationalized physics-based model which is basically about augmenting predictive power; that’s why we are doing it.  Machine learning is mostly about predictive power -- sometimes it is about learning from failure, but mostly it is about predictive power,” he said.

Predictive power

Much of the work that goes into building predictive power at the design phase, but that has tended to be lost the moment we go into operations. “That has to stop,” said Leurent. “We need that predictive power in operations now, as part of the digital transformation. “

Akselos have taken the technology and made it operations-ready, speeding it up by factor of 1,000 and sometimes a lot more. And it has coupled it with sensors that learn from sensors that are deployed in operations – and that has made the workflow much easier, so it can be used in operations.

Caption: Akselos created the world’s largest predictive digital twin of a 90,000 ton FPSO.

Source: Akselos

“Essentially, we’ve changed predictive technology to make it operations ready,” said Leurent. “We try to couple machine learning and a subset of machine learning that is uncertainty quantification. In the upstream industry, there is plenty of uncertainty of the inputs. It is all very good to be using model, but if you don’t know what the input is, then we have a problem.”

Machine learning has enormous potential in the integrity management side of the oil and gas business.  Wood Group’s Kavanagh highlighted the implementation of a machine learning solution is in what it calls “Valve Signature Analytics”.

This addresses the challenge the subsea industry faces in identifying when valves might be degrading. Wood has applied machine learning analytics to the time trace of key operating parameters that are monitored for these valves.  In this case, said Kavanagh, it managed to implement a solution that has allowed it to anticipate degrading and failure typically 1 to 2 years before the failure occurs.

Machine learning approaches can be used to reduce the frequency and costs of subsea inspection. “For inspection costs that could run to $100,000 a day for lengthy programmes that may be carried out each year, the potential cost reductions in this area are clear,” said Kavanagh. “If machine learning helps you manage risk more efficiently and with less subsea inspection, that translates to the potential for substantial efficiency gains and cost savings across the industry.”

Upstream Intelligence

Machine learning gains renewed momentum as oil companies tap into AI

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May 14, 2018