Category: Technology and Methods

New technology and ways of working: What it may do – what’s commercial and what’s not

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.

Self driving and the digital avalanche

Justin Rowlatt just published an interesting article (he admits it is provocatively one sided) about the inevitability of self driving cars and the disruption it will cause. The article can be found here: https://www.bbc.co.uk/news/business-45786690.

I urge you to read the article, because it describes accurately the confluence of forces that causes avalanches and a split between the new and the old. When technologies hit a certain point the economies of network, scale and of learning kick-in to reduce the cost and increase the convenience of switching to the new, while the exact opposite happens to the old – making the switch happen in a non linear avalanche of change.

Justin’s article includes a photo of a New York street in 1900 and then in 1913 – in the first, the street is full of horse buggies and one car, in the latter the situation is reversed. The Model T Ford motor car was introduced in 1908.

For electric cars – just like in parts of the world where you still find many horse (and Ox) drawn carriages – motor cars as we know them will not disappear; the rate of manufacturing switch will be slower and cars bought today will still work in 20 years time.

A few years ago I made a calculation that, because of these and other factors, the internal combustion engine would take 50 years to be replaced even if the rate of uptake of electric vehicles accelerated. Justin makes a great point that, because of the effects of self-driving, we need, perhaps, only 10% of the current fleet to change and we’re done. Economics will kill the current car and nothing else matters.

This reminds me of why Amazon can (and has) destroyed the high-street. It doesn’t need to take 100% of the business, but – because bricks-and-motar retail has high fixed costs and low margins – they only need to take 10% of the revenue and Mrs. Smith’s Bookshop is toast.

The Fourth Industrial Revolution will be made on lots of changes like this. The facilities that the self driving car will enable (and the infrastructure needed to support them, and spin offs around that) will mean new industries emerge and old ones die. And it will happen quicker than we imagine.

Elon musk, for all his bluster about electric cars, is really re-inventing manufacturing [Link]. Not only will his disruption hit the auto industry, but any form of manufactured assembly of mass-produced product. And that’s just about everything consumers buy.

Get ready now!.

Digital overload?

I’ve just had the time to scan the Sept-Oct ’18 issue of HBR and there is one article that all digitalisation professionals should read. Titled “Too many projects” by Hollister and Watkins [https://hbr.org/2018/09/too-many-projects].

Not only does it provide an example of why cutting IT budgets across the board is a bad idea for business, and an explanation of “logrolling” where executives support each-other’s pet projects, but also it provides a neat framework for assessing initiatives prior to launch.

I touched on initiative selection as part of a prioritisation framework Introduction to Prioritisation V 1.0,  I also wrote an article in 2017 which was recently published in Oil and Gas Technology that touches on some of the concepts [link]

I hope HBR don’t mind me paraphrasing one of their templates but it’s really quite excellent:

Questions to Ask Before You Launch an Initiative:

Analyse the project

  • What problem is this initiative meant to fix?
  • What data or other evidence tells us that this initiative will have the desired impact?

Assessing resources

  • What is the true human capital demand?
  1. What resources (time, budget, and head count) are needed to design and launch the initiative?
  2. In addition to the department that owns the initiative what departments or functions will be tasked with supporting it?
  3. What time Commitments will be asked of leaders and staff members to attend meetings or develop the skills needed to understand or implement the initiative?
  4. What resources will be needed to sustain it?
  • How does the human capital demand compare with the potential business impact? Does the cost outweigh the benefit?
  • How will the organization determine whether it has the capacity to take on the initiative?

Sizing up stakeholder support

  • Who are the key stakeholders?
  • What actions will be required to support the initiative?
  • How fully is that support in place?

Selecting limits

  • What trade-offs are we willing to make? In other words, if we do this, what won’t get done?
  • What’s the sunset schedule and process?
In summary then: next time you're asked to add yet another digital initiative make sure that it takes into account the impact and value to operations!

image credit: http://www.chinadaily.com.cn/photo/2013-12/02/content_17143892_2.htm

 

 

Report from the future

The trouble with our industry at the moment is the plethora of conferences and events that go on. The FT reported on Thursday that our industry is 40% less productive than the rest of the economy, is there a connection?

Last week there were, at least, four separate events happening in Europe. I didn’t manage to attend the Future Oil and Gas conference in Aberdeen, but it seems that I may have missed a rather good one. I’ve been asking around and receiving reports on the discussions and topics.

My informal word-count revealed some key themes: Open platforms, leverage of diverse data sets, generating insight (whatever that really means), data silos, collaboration, machine learning and AI.

Where are all the young people?

First the bad news. This conference seemed to have a definite bias towards the fourth industrial revolution and the future of innovative technology – but no-one arrived by skateboard. In fact, my sources indicate there were more suits and ties on display than at a moss-bros Christmas party and Grecian 2000 narrowly avoided being the main sponsor.

Where are all the young people?

When I go to a tech conference in the South East or in Silicon Valley I’m positively jumping out of the way of hover boards, unicycles and tattoo artists. I may appear flippant but I’m not – the great creative and innovative minds of the future seem to be missing from our conferences. If we are going to succeed we need to be able to form teams that embrace diversity and create energy. We need people like this and we need to provide an appealing set of challenges to keep them motivated.

Equinor supports entrepreneurs

Now onto the good stuff. Einar Landre from Equinor (the artist formally known as Statoil) told how they supported small vendors – while being careful to explain that they were not offering blank cheques, he recognised that procurement processes could be slow and risked pushing suppliers to the wall. I heard they claim to be actively promoting ways to engage with innovation and to create disruptive business models where they pay for outcomes rather than for inputs. Separately,  I  picked up on an announcement that Equinor plan to release all the operational data that was gathered on the Volve field to be used to test algorithms and find new ways of working. Well done chaps, I think that’s a very collaborative and welcome move.

Chrysaor integrates a new asset

I also hear that David Edem from Chrysaor gave a lively presentation where he told the gathering about the recent experience of taking over an oil field from another operator. How explained that first problem is to get hold of the data to understand what it is that you’ve actually bought. In the middle of all this their organisation head count grew 20x in a year and, for them, it is clear just how much time and effort had to be invested searching for data. David told us he was keen to address this early in the company’s life and highlighted one case where a simple change in data-handling practice is already producing savings of $1M pa. He said that we should consider carefully the value that is embedded in the data that comes with a platform and treat this as a capital asset.

Ithaca understands the tension between IT and OT

I also heard that Malcolm Brown from Ithaca was keen to share his experience regarding the tension between IT and OT. He brought a key insight that the perception of risk is different – IT believe that the more you leave a system alone the more vulnerable it becomes (because of the evolving security threats and the lack of patching), whereas OT believe the opposite – each time you touch a system it is more likely to break than get better (i.e. don’t fix what ain’t broke).

Of course, both viewpoints are valid and have merit. Reconciling these is going to be important for us all, so it sounds like formal risk-management processes with OT are going to be required to enable safe innovation.

Fail Fast and Learn

Another theme that emerged from the conference was Agile development of systems and processes. This is important, because Silicon valley has proven that Agile methods can increase the rate of value creation. They also establish competitive advantage and lead to unimagined breakthroughs. How can we integrate the “fail – fast & cheap – and learn” methodologies with our industry and still keep everything safe.

Keith Wildridge from Eigen brought this topic into his talk and was keen to share experience engaging in collaborative development with ENI making safety systems and using methods such as SCRUM and SPRINTS.

Event Format

The format for the event – that of discussions and panel sessions – was warmly received by everyone I talked to. They all said they were fed-up of boring people with boring powerpoints standing up and lecturing at an equally bored audience. This was much better.  They were also happy that the representations were not all from Vendors trying to find a way to dress-up a blatent sales pitch as some form of case study. Exploring broad themes in an open environment went down really well – so this conference seemed like a welcome boost and I think it will stand the test of time and become a feature in my diary for 2019.

Conclusion

I’ll leave it to the words of Esa Jokionen from Rolls Royce who apparently summed up the industry approach to AI and Big Data. I’m told he said it was like teenage sex. Everyone thinks everyone else is doing it, everyone wants to say they are doing it – but, truth be told, there is not much of it actually going on, no one knows how to do it properly but everyone’s keen to try.

Image credit: http://www.futureoilgas.com

 

 

The Fourth Musk(eteer)

Introduction

Amazon reinvented how we bought books. In the process they re-invented the way we enable people to find and order almost any type of goods. Once ordered, the company arranges to have to have our orders despatched and delivered. Amazon seems to have become an unstoppable force in the world of retail – laying waste to high-streets, department stores and shopping malls along the way.

I see something similar beginning to happen at Tesla. Elon musk has moved on from innovative products such as the electric car and is now on the cusp of reinventing manufacturing. Few people seems to have noticed how general his approach can be and how it can be applied to making just about anything, and making it anywhere.

Innovator’s Dilemma

If you haven’t read “The innovators’ Dilemma” by Clay Christiansen [LINK]  it may be time to do so, or if you have brush up on the contents again. This book was first published 1997, as the world was going internet and computer crazy. It has stood the test of time.

The basic premise of the book is that industry incumbents tend to innovate by making their products better. All their customer focussed research and development is structured to avoid making products that are demonstrably worse than what they have in almost every way. But upstarts can and often do launch products like that to serve market segments uninteresting to the incumbent.

But innovation, it turns out, is dynamic and pretty soon the upstart is learning to get better to the point that their offering becomes “good enough” for a large slice of the market served by main suppliers.  The most demanding customers will still be pushing for extra features from the incumbent but this becomes increasingly difficult to achieve and scale economies fade (as the mass-market defects). This leads to the demise of the once dominant generation and the rise of the innovator.

The examples that Clay based most of his early published research on where the manufacturers of disk drives in Silicon Valley. But he drew the parallels in other industries. The book considered end-product (the disk drive), whereas now I am seeing the same market dynamics emerge in processes and services. Where the first steps of the new methods are not quite as good as the traditional, but the direction of travel means that the inevitable result will be an unstoppable revolution in the way things are done and the way things are made.

Amazon warehouse success and Tesla’s manufacturing innovation

Earlier I wrote about the innovation that was happening in the Ocado warehouse. [LINK] Amazon has quite a lot to say about efficient warehousing but (I don’t think) are licensing their technology to others. The innovation that has happened here has digitalised the warehouses and made them more efficient.

Elon Musk is doing this for manufacturing. What I find interesting about the approach to manufacturing in the Giga Factory [LINK] [LINK] is that it’s fundamentally different approach than updating a car manufacturing plant to become digital. It’s the reverse. Let me explain.

Amazon didn’t apply digitalisation of retail to book buying, they applied book buying to a digital retail and supply chain – once perfected it was instantly ready to serve across categories. Tesla is doing the same in manufacturing. Once you’ve learned to manufacture in an automated way – it’s a small step from cars to any other type of product.

A good place to start

Books were a good place for Amazon as it was a very inefficient process and bad for the customer. It turns out that car manufacturing is also a great product to choose to apply to digital manufacturing because there is demonstrable market for the finished goods. They are poorly served by the current process and the incumbents are being held back by two big forces – the internal combustion value-chain, and the clogged thinking born of mass employment and model for command-and-control distribution of labour and “industrial man”.  See “My Years at General Motors” Alfred P. Sloan [Link]  for insights on what the world of manufacturing has been striving to emulate since the 2nd industrial revolution started.

How does this apply to oil and gas?

There are two reasons why this is relevant to Oil and Gas.

  • firstly we are organised very much along the lines of division of labour and command and control described by Sloan. If this model is now under threat from people like Musk then we can assume that world of work as we know it in our industry will also change;
  • secondly as Patrick Von Pattay said in my interview with him [Link], perhaps the threat is not going to come from an incumbent applying digitalisation to make their existing oil and gas operations better, but perhaps it will come from someone who has learned to be an efficient operator of facilities who is now going to include oil and gas to their process. Like Amazon starting to sell electronics as well as books.

Conclusion

For the next 25 years, I suggest closely following the advances in automated manufacturing which is happening in Nevada right now, and imagine how such changes in working practice can affect our industry. Because they will.