While we were sleeping – Oil 1.4 and Solar

It’s been very busy since the Network Dinner in September. I will post an update on the discussion later this month.

In the mean time I’ve been busy working on innovation – more of that later – but I recently came across two interesting items that I think might be worth sharing.

Firstly the FT ran a special issue talking about Oil and Gas 4.0 [Link]. It’s good to see that this term is being widely applied – and a big change from when I started to talk about it a few years ago.

I wrote an article in March 2016 when I claimed that Oil and Gas were really at 2.5 while industry was going 4.0 [Link] I was concerned about the lack of urgency and technology progress. I also called out the contribution of Collette Cohen as being one of the few that seemed to get technology. She is now director of the Oil and Gas Technology Centre.

The OGTC were referred to in this article [Link]

In October, the non-profit Oil & Gas Technology Centre (OGTC) in Aberdeen in Scotland, announced the next phase in its autonomous robots project with Total of France, which is developing what it calls the world’s first offshore work-class robot. The first phase of the work saw Austrian firm Taurob create a robot to conduct visual inspections at Total’s Shetland gas plant and the Alwyn gas platform in the North Sea. A second-generation version will have a stronger chassis and a heavy-duty arm that will lift objects and turn valves. It will be tested by Total and Equinor of Norway, the research initiative’s new partner.

“A lot of our work on hazardous environments focuses on whether we can avoid sending people into those areas in the first place,” says Stephen Ashley, head of OGTC’s digital transformation solution centre.

Another article coined the phrase Oil and Gas 1.4 which is a clever take on the combination of an old-age industrial organisation embracing new digital technologies within its core business. I think I like this term better than my 2.5 one.

This article [link] makes the point that the new technology is prevalent in some areas of the business, but that the new frontier for production might be the application of technology to find economic ways of enabling enhanced oil recovery. 

Unmanned rigs are now commonplace, complex operations are monitored from a single control room, leaks and emissions of greenhouse gases can be identified by drones and satellites, removing much of the need for direct human inspection. Numerous technologies are being applied in ways that can reduce cost and improve productivity.

The key question, however, is whether the digital revolution can answer the sector’s biggest challenge: how to secure future production. Oil demand is not falling. There may be 7m electric vehicles on the world’s roads but there are also 1.2bn vehicles with internal combustion engines.

[…]

One answer must be for companies to make the most of assets they already hold. Across the world the typical recovery rate from a conventional oil or gasfield is only 35 per cent.Even after decades of production giant fields such as Prudhoe Bay in Alaska or Ghawar in Saudi Arabia still contain billions of barrels of oil. Recovery rates have slowly risen and provinces such as the North Sea, originally expected to close at the end of the last century, continue to produce oil and gas. In Norway recovery rates are typically 50 per cent — well above the world average but still leaving half the resource base undeveloped.

The point at which recovery becomes uneconomic, ie when the cost of enhanced recovery is greater than the value of the oil, is a serious constraint.

What I’ve found really interesting this year is how irrelevant the oil and gas industry seems to have become down here in London. What I mean by that is that Oil and Gas seemed to be at the crux of things in a way that, say, copper mining and concrete production wasn’t. It used to be a cool place to play with technology, travel the world and to make a bunch of money. I think those days may be over (though some predict a spike in prices around 2025). Now no-one here cares about Oil and Gas at all.

Where I am seeing a lot of action and excitement is around Solar and Wind. I thought for a while it was just me becoming aware, but now I’m onvinced that it was a sea change and it really is picking up. And the cost-curve of Solar is particularly striking.

I urge you to have a look at Tim Harford’s article on Solar [link]. As always he has an ability to grasp the implications of what he sees in ways that other’s don’t. He looks a PV cells – how in 1980 Solar was about $100 per watt ($10.000 to light a light bulb). It is now already below $0.25 / watt and falling. Utility scale production is now looking to provide generation at below $0.015 per KW/h. [link]

The thing about Solar Panels is that they are a pure manufacturing play. Once created they just sit there and make energy. No moving parts, no plat to really operated as such. We have been, and continue to be, very good at manufacturing standard products in standard factories.

Sometimes the learning curve is shallow and sometimes it is steep, but it always seems to be there.

In the case of PV cells, it’s quite steep: for every doubling of output, cost falls by over 20%.

And this matters because output is increasing so fast: between 2010 and 2016 the world produced 100 times more solar cells than it had before 2010.

Batteries – an important parallel technology for solar PV – are also marching along a steep learning curve.

The learning curve creates a feedback loop that makes it harder to predict technological change. Popular products become cheap and cheaper products become popular.

And any new product needs somehow to get through the expensive early stages. Solar PV cells needed to be heavily subsidised at first – as they were in Germany for environmental reasons.

More recently China seems to have been willing to manufacture large quantities in order to master the technology.

Watch this space, it’s just getting cheaper, better and faster. This is where the action is – I just don’t know how to play the opportunity yet.

 

Machine Learning – more learning….

As an addendum to last week’s post about machine learning – here is an article by the BBC : https://www.bbc.co.uk/news/technology-48825761. This describes a story about an amazon employee who built a cat-flap. Fed up with receiving “presents”, he made this clever device to recognise whether his cat has come home with prey in its jaws. If it has, it refuses to open and the cat must stay outside.

I thought it neatly encapsulated some problems of machine learning I am finding, and also pointed to some possible features where this technology could be applied to generate more value.

The training problem and need for clean data

It took 23,000 photos to train the algorithm. Each had to be hand sorted to determine whether there was a cat, a cat with prey etc. etc.

This is like the oil and gas industry in that it needs a lot of clean training data that may not be available without a lot of manual input.

The Bayesian stats can work against you

The frequency of event-occurrence in this case is once every 10 days. The maker ran a trial for 5 weeks (so should have seen 3-4 instances in that trial, though in this case it’s stated as seven). There was one false positive and one false negative – giving an error rate which may be around 20% (though there are not enough samples to have a lot of confidence). If the algorithm continues to learn and say that the cat uses the flap 5 times a day on average. This means, in a year it will have 1,825 additional samples from which 35 will be positives of which 7 will be false and 1790 negatives of which 358 will be false-positives. Manual correction will be required 365 times (i.e. per day on average) and the learning rate will take about 12 years to duplicate the original training set. I don’t know, but I suspect there are diminishing returns on adding new data so how much smarter it will be in 12 years I’ve no idea.

Disclaimer – a good statistician will know my maths above is not quite right, but the principle is.

Do you trust the alarm? Do you take notice?

So in this example, quite like oil and gas operations, the issue was getting hold of clean training data. The event being detected was comparatively rare meaning that a lot of false positives are likely. In the example above an “alarm” would have sounded 365 times and 28 occasions it would have been real. With the sparsity of events the this means that the algorithm will not learn very fast and I think the alarms will be ignored.

So where can the applications be better?

Distributed learning

Parallel learning helps build better predictive models. if we had the same cat-flap for every cat, and every owner corrected the false signals and right algorithm could learn quickly and disseminate the results to all, this would speed the learning process. Self-driving cars are a good example of where this is possible, and google-search is great example of the power of parallel experience.

Products and services impossible for a human

Situations where there is so much data that manual processing is impossible. Here I don’t mean that you can collect so much data on a manual operation that is ongoing that you cannot analyse all the extra information. What I do mean is that there is intrinsically so much information it would be impossible to analyse by hand and never has been. So an ML approach is the only one possible. For instance looking at real-time changes patterns in data networks.

Simple situations where it’s expensive and boring for a human

Automated first-line help systems, call screening, password resetting etc. These are all tasks where humans can do them, but they are simple tasks which are too boring for smart people to do, where automated help can often provide a better experience. And where “sorry I didn’t get that, did you say yes?” is only mildly irritating not the cause of major corporate loss.

Conclusion

There are places that machine learning will be revolutionary, but I suspect that much of ML will be either embedded to make normal tasks a bit easier – such as auto-spell checking, voice recognition etc. Or they will tackle classes of problem such as IT security, or on-line shopping behaviour where there is inherently a lot of fast-moving data and manual monitoring is simply not feasible nor fast enough to work.

Machine Learning — Data First

With all the attention on Machine Learning (ML) that I encountered at London Tech Week, I thought I better find out a bit more about it. I wanted to verify my view that it won’t have a dramatic impact on the Oil and Gas industry and see if this was actually true.

My findings, so far, is that ML might:

  • Speed up some analysis
  • Change the spread-sheet paradigm (for better or for worse)
  • Enable people with the right level of expertise to create predictive analysis (whether this will be at all valuable is a different matter)
  • In a myriad of small ways, change minor tasks (removing annoyance, reduce low-value add data & reporting work, change interfaces)

None of these applications are dramatic in themselves, but over time they may add incremental benefits that provide a moving improvement-front. A bit like a six-sigma or Kanban.

What will you need in order to drive broad-value from ML

You’ll only be able to take advantage of that if four things are true:

  1. You have clean, historical data available
  2. You can access and combine quality-controlled, time-dependant data in near real-time (ideally from multiple sources)
  3. You have wide-spread knowledge of how to apply the new analysis tools – like those based on “R”. (think how many people can use Excel today for collecting, analysing, querying and reporting on data – varying degrees of proficiency, but who do you know that can use “R”)
  4. You are prepared to reorganise the way work is performed to take advantage of the new possibilities created by: data analysis leading to demonstrated-fact-based / probability-assessed management decisions & employee actions.

Low cost hardware is the trigger

The interest in machine learning is spawned from the dramatic drop in the cost of hardware and software required to perform the number crunching required. Because of this, not only has the complexity of the addressable problem increased but also the inefficiency of code that can be supported increases the usability of tools and techniques leading to their application by practitioners outside pure decision sciences.

If you attend any of the IoT conferences – or speak to the large vendors of real-time industrial data, you’ll hear a lot about how edge-computing and Machine-Learning will change things for industry. “Edge” means placing computing power in the field with low-power and small costs.

Putting this alongside the sensor enables pre-processing information to send back only the results. This helps to reduce data bandwidths and increase responses.

For a view on how cheap this type of technology now is – and how undramatic the applications of ML really are I invite you to have a look at this video (from the hobbyist market) showing what can be done for less than $100. Listen out for the references to “TensorFlow” one day that will be important, there are also some passing references to cloud-based resources that may be of interest.

What I learned at London Tech Week

Big Data, Artificial intelligence, Machine Learning and Computer Vision

Turns out that Computer Vision requires Machine Learning, and Machine Learning requires Artificial intelligence. Artificial intelligence is of most use when there is large amounts of data to process. Hence in a way, AI relies on big-data (though not always). The cognoscenti use the abbreviations CV, ML, AI and err…. “big data” to refer to these technologies.

Some simple definitions

These are what are called HORIZONTAL TECHNOLOGIES, because they are general and can be applied to a range of problems across industries. They are already having an impact in some areas but they are not a panacea.

Big Data relates to the collection and storage of large quantities of data and being able to access and manipulate this quickly – using high speed networks, special search algorithms, parallel processing etc. It gives rise to a whole world of shared resources, cloud computing and specialised storage schemes. When real-time information is included (such as sensor data) then this is the world of IoT, time-series data and edge processing.

Ai is a way to analyse the information contained the big-data very quickly. Creating inferences between data signals and looking for patterns, sometimes used to predict outcomes and reduce uncertainty. AI is very narrow in its applicability, even Bill Gates says you wouldn’t trust it to order your inbox for you, so it’s ability to make judgements is limited. A lot of what we talk about being AI is a form of linear regression, mass computational power enabling the quick processing of data and crunching of large amounts of data. AI can give the illusion of being smart, when in fact it can be easily fooled.

Machine Learning is the ability for an algorithm/analysis to change over time by examining a changing stream of input information and comparing computed outcome with desired outcome and tuning. Neural networks. Machine learning is an application of AI.

Computer vision is an application of both AI and ML which is used to process images (still and moving), one of the most controversial applications of this is with facial recognition and the automatic tracking of people.

Some of the take-aways from London Tech-week

Big Data

Everyone that spoke (and I mean everyone) – said that their biggest issue of applying any form of advanced analysis fell down on the quality of the data. The meaning of information collected and the way it is labelled is so inconsistent. There were some semantics companies working with different ways of expressing ideas in language which may hold a key to explaining the differences between the labelling of data items. Until this takes off then 75%-90% of your AI budget is going to be spent cleaning up data and sorting out the meaning of feeds. Trouble is you will spend this 90% of your budget with no tangible change in outcome as you can’t get started until it’s sorted out.

AI

I saw what I thought was a brilliant example of this from a Swedish company called Spacemaker (https://spacemaker.ai/ ). This company works alongside architects to help them choose between the complex trade-offs required when selecting the layout of buildings. Trade-offs between natural light, housing density, noise exposure, energy efficiency etc. By providing optimisation inputs the computer very quickly generates possible layouts which works alongside the architect freeing them from the mundane, but complex, calculations and predictions of weather patterns, seasons etc. The result is much better buildings but not taking away the artistic judgements of the architects.

In Oil and Gas I can see a similar “advisor” system working alongside production engineers, economic planners, maintenance engineers, planners and schedulers helping to provide scenarios based on optimisation parameters enabling them to choose the best configuration to implement.

Machine Learning

I saw an example from a company called Dark Trace (https://www.darktrace.com/en/ ). As well as being a simply brilliant commercial success with enough financing to direct sufficient money dedicated to PR, marketing, sales and distribution (as well as well researched and implemented tech). Said quickly their system sits at the network hubs in your organisation and reads each packet of information (sometimes understanding the content, but often only it’s source and destination). It uses ML to work out what normal looks like for you (and evolves), and if it sees something abnormal start it can raise questions. It can also take action by IP spoofing to intercept traffic and block comms. One of their success stories is the NHS trusts that installed them and contained the WannaCry attack (https://www.bbc.co.uk/news/health-43795001 )

This might be applicable to Oil and Gas by monitoring all the signals in the real-time stack and learning what normal operation looks like and then being able to spot abnormalities as they start to occur. It would be more complex because the relationships are more complex than network traffic but still, got to be worth a shot.

Computer vision

I found this very interesting. I spoke to a company Winnow which has applied computer vision to the problem of cutting food waste from restaurants. (https://venturebeat.com/2019/03/21/winnow-uses-computer-vision-to-help-commercial-kitchens-cut-food-waste/ ). By using a camera it captures images of what the chef is throwing away and through a series of algorithms works out the amount and value of the waste. For instance, perhaps a chef orders the same amount of broccoli every day, but really only uses most of it on Fridays, Wednesdays and Saturdays. Or maybe the ordering changes with the weather – but either way, if you can reduce the over ordering then everyone wins.

People are already applying technology like this in industrial settings to check that people use their safety equipment (like glasses, harnesses etc.), you could also start to put cameras covering manual controls to create records of change and current settings – it would be a cheap way to retro-fit instrumentation.

Conclusion

That’s all for now but some of the other things I found out about included “The Ostrich Problem”, the real-world applications of AR/VR, more on cyber security and what 5G will really mean. Other things that are hot right now are Commercial adoption, future of work, small-scale adaptive robotics, AI-Ethics and Decarbonisation. More of this later.

London Tech Week

Last week was London tech-week. I guess a bit like London fashion week, only much larger.

There were events all over London – this has grown from a week of borrowed conference rooms and underground gatherings into a large series of happenings. Monday-Wednesday saw the COG-X show near the google campus north of Kings Cross. At this event there were 10 stages giving parallel presentation sessions over the full three days, an Expo, start-up section and various corporate networking events.

Wednesday and Thursday (yes overlapping) saw the TechXLR8 exhibition at the Excel Centre, this was a large expo event with presentation 6 stages running all day.

Alongside these two there was also 5G Europe and Identity Management conferences both of substantial size.

Have a look at the website here https://londontechweek.com/events – there are 18 pages of events with 7 events per page. Tech-XLR8 above is only one of these.

This blog previously covered the launch of the UK’s industrial strategy (at Jodrell Bank) and the lack of coverage of this in the main stream media [Link]. Well, despite there being still no interest from the media. The UK industrial strategy was evident everywhere – with announcements from the various bodies, challenges, and funding opportunities. Have a look at this if you haven’t already : https://www.gov.uk/government/publications/industrial-strategy-the-grand-challenges

And did you know there is an “Office for Artificial Intelligence” ? https://www.gov.uk/government/organisations/office-for-artificial-intelligence

I’ll write more about some of the events in due course but here are the highlights:

  • There were 1000’s of under 40, very intelligent, eager advocates of tech everywhere. Very diverse in terms of sex, ethnicity, country of origin you name it, very much in contrast to this [Link]
  •  AI, IoT, ML, CV, AR, VR were the flavours of the moment (and I learnt some really interesting new insights here, more later)
  •  AI Ethics is a huge deal, and lots of people are thinking about this.
  •  Energy tracks focused on decarbonisation, distributed grid and combining sensor technology with predictive algorithms to reduce consumption. Oil and Gas didn’t feature once.
  •  Interesting to see the traditional tech players (with notable exceptions) were looking dated and pushing out platitudes about the new tech and the business impact it should have (but with no concrete examples). Meanwhile there were (really) hundreds of well-funded small companies that had real-world use-cases for niche solutions that had demonstrated value (though most had not had to pass a business-case hurdle to get going).

What struck me most was the vibrancy of the arenas, the buzz of conversation and the high-energy engagement between participants – problem solving and exchanging ideas. It was very refreshing to see. There was also a willingness by all sorts of industries to try new solutions and approaches – knowing that not everything will work but understanding the need to learn and push the envelope forward. The pace of change is amazing.

I was lucky enough to have the chance to try a VR simulation made by Linconshire Fire Brigade to train their officers in fire investigation. On with a VR head-set and into a virtual world. It was very, very realistic.

Oh and everyone was talking about “Digital Disruption”

Next year London Tech-Week should be one for your diary.

Looking for inspiration

I like to look across sources for analogy and stimulating ideas. A couple of things have recently caught my eye.

I find it amazing how hard it is for people (including me) to see the implications of new technologies and ways of working. In retrospect, once a change has happened, it’s obvious what the outcome would have to be. But when the change is happening it’s not so clear.

Going up

Ground floor
Perfumery, stationary, and leather goods, wigs and haberdashery, kitchenware and food. Going up…

Can you remember the theme tune to Are You Being Served?

I’m old enough to remember the lift operators in Aberdeen’s E&M and Watt & Grant department stores. They were replaced by automated lifts in about 1980. The stores have both succumbed – one to the shopping mall, the other a victim to digital retail.

Being a lift operator was a skilled profession, making sure that you stopped the elevator car level with the floor and opening the concertina iron-work doors with the brass handles.  Apparently New York’s last lift operator was only made redundant in 2009 Link

The Economist 1843 magazine just ran a story making the connection between the elevator operators strike and the adoption of self-driving cars. We could probably do the same with roles in the oil field.

The elevator strikes in 1945-47 crippled the city, and led to calls to redesign the city so that only low-rise development was permitted – to reduce the power of unions.

Of course, the answer was – as we know – automated elevators. But a lot of change management was required before people started to use them. Innovations such as emergency stop buttons, telephones for help and recorded announcements all came about in this time.

I’ll wager that we will look back at some of the manual ways of operating an oilfield we use today in the same way was we look back at the anachronism of the elevator operator.

Electricity – who’d want that?

Another story that I picked up on and found illustrated a point was this one [Link]. It’s written by the BBC’s Tim Harford. He asked and answered the question why did it take so long for electricity to displace steam in the factories in the North of England. It was decades after the invention that it was fully adopted.

He explained that it required a redesign of factories before the economics made enough sense for people to abandon centrally powered manufacturing and move to individually powered machines. We’ll see the same adoption economics in oil field operations and technologies such as 3D printing.

Digital Marketing – a lesson for oil and gas?

Today I found another article that resonated. This one is from Marketing Week [Link]

Mark Ritson makes the case that the separation between Digital Marketing teams and Traditional Marketing is ridiculous. What I think he’s saying echoes my point that there should be no separation between “IT” and “The Business”, because IT needs to be just how things are done around here. It’s true in Marketing, it’s true in Oil and Gas too.

“… On the one hand you need to avoid being precious about your digital creds. Signal early you are entirely comfortable losing the D prefix from your title and, for good measure, add something re-assuring like ‘I do not even know what digital means anymore’ or ‘isn’t everything digital now?’.

The merger process means that anyone who is a member of the extreme digerati will be the victim of the new regime. You know the type: obsessed with AI, convinced in the long-term value of VR, boastful that they don’t own a TV. They will be the first to go when the revolution comes.

Digital experience is a prerequisite

But make no mistake, it’s no good proclaiming that digital is wank and it’s time to get back to basics, pull all the money from Facebook and get it back into ‘proper’ media. The post-digital era cuts both ways.

While idiot digerati will be exposed, so too will those who aren’t open to the potential of all the new research and media options that have appeared over the past decade. When Alastair Pegg, the leading marketer at Co-op Bank, noted that that there was “no such thing as digital marketing” he followed up with the corollary that “all marketing is digital marketing”.

I think I can see the parallels between what he’s saying is happening in Marketing now, and what will overtake the world of Oil and Gas operations in the next 3-5 years. What do you think?

2019 – The Year Ahead

Happy New Year

As we enter 2019 I’ve managed to already break my first resolution – to get this blog post out before everyone gets back to work. As an excuse I’ve had a very busy start to the new year. As a warning, I think we will all have a busy year this year.

When looking forward, I often find it useful to reflect first on the past and see how thoughts are changing.  After you’ve read this post, please revisit this one [link]. It was written in March 2016 – Trump had not been elected, the Brexit Referendum had not occurred, Cambridge Analytics had not been exposed and Russian interference in the US election was not known about. An extract from the post reads as follows:

With modern communications and the ability to mobilise quickly we’ve already seen massive changes in the way the people (or, in Greek, demos) interact with conventional democratic systems and capitalism. [….] Whether that’s the Arab spring, so-called ISIS, Brexit, the mass-migration of populations or the astonishing rise of Donald Trump, things are getting decidedly odd in traditional politics. […..]

Cyber-politics is a whole new dimension. Whether cyber aggression is aimed at accessing private information, denying or altering the dissemination of information or compromising the physical integrity of machine-based systems the ability of people to alter the course of events through “hacking” has never been so great.

As the 4th Industrial revolution unfolds there will be more disruption ahead.

On the positive front, last year we saw the unveiling of the first industrial strategy for Britain for a generation link. I’m seeing the ripples of this throughout the industrial landscape of Britain, including a member of the Bestem network  who told me about some very innovative work he’s doing with the railways – all funded from central government. The funding he has access to is much larger than the whole OGTC [link] annual budget and he just needs to fill in a form to get it. It’s very light weight, no committees, websites, offices, equipment, industry sponsors – just get on with it. And he has. Big time. Oil and Gas is still not innovating, but we are good at committees and wasting each-other’s time.

My top predictions for 2019

  1. Attention will continue to swing away from economics & finance and towards science, inventiveness and engineering (genetic, information, computing, transport).

  2. Competition between nations will intensify with value-capture swinging towards creators and away from traders and rent-seekers.

  3. Politics will continue its rise – no more will debates be settled on the economic benefits of an argument. Politicians will start to use emotive language more. Manuals on speech-writing for rhetoric, bathos and pathos will be dusted off along with words and phrases including: trade, craft, pride, sacrifice, service, future, humanity, community, future-generations and “for-our-nations-children”.

  4. Language will continue its progression-regression. Old words take on new meanings. In my field the fourth industrial revolution became digitalisation, I am sensing that this is now becoming “innovation”. Again.

  5. Productivity will increase and the british economy will grow. Not, you understand, because it will objectively do so – but because the way we decide to measure it will change. We are already moving to double-deflation accounting in April [link] . You can expect more of this type of thing. It may be good for us.

  6. The Oil business will still be ruled by old-world economics and yesteryear-practices. I remember the dot-com boom in the late 1990s when there was genuine fear in my part of the Schlumberger world that we may be acquired by  Yahoo. Now Google (which was only formed in 1998 and not floated until 2004) could swallow Schlumberger many times over – but frankly, my dear, doesn’t give a damn. It’ll be the same this cycle, the Oil business will still work, be profitable and vital – but paradoxically become increasingly (and proportionally) less relevant to measured world economic activity.

  7. The Big-Oil innovation committee will, after a multi-year tender programme, finally hold a committee meeting to issue the PO for the automated remote light switch. After their youngest member retires on full final-salary and is the last to leave the building this will be used to turn off the lights. By SMS. Sent by his secretary. From the last electrons of his dying Blackberry.

  8. Elon Musk will either be killed in a freak mid-air collision between him and Richard Branson, or will buy a small nation (to be called Matrix) and will be joined John McAfee [link] and Larry Ellison. They will declare independence.

2019 looks like it will be a fascinating, scary, depressing, joyful and amazing ride. Strap-on, tune-in and don’t drop-off. All the best my friends, it’s going to be a wild-one.