O&G –Development, Projects and Industry 4.0

In an earlier post [link] I introduced four areas of the Oil and Gas value chain that would change because of Industry 4.0 (I4.0). In this post I am going to present some ideas about what might change the activities occurring between finding a deposit of oil and gas and going into production.

There are four widely recognised phases during this period I’ll label these: Concept, Design, Construction and Commissioning.

There are many interacting functions contributing to this process besides engineering. These include: Project Controls, Stakeholder Management, Finance, Legal, Risk, Marketing, Contracting & Procurement. Because oil projects tend to range from 100’s of millions to 10’s of billions of dollars these activities can have more impact on a project’s viability than the engineering.

In this post I am not going to consider the changes that I4.0 will bring to the underlying technology deployed in a field (such as intelligent completions, sub-sea completion systems etc.) but rather will concentrate on the operator process of bringing a field from discovery to on-line production and how this may change.

CONCEPT SELECTION

Concept selection involves generating options for ways to develop a field – such as what type of platform, how many wells, where to sell the product, how to transport it etc. This involves assessing various trade-off’s between cost, production profile, technical risk and time to market.

This area is one where experience and networks count. Advisors add value here. Unless an operator is a super major, the number of concept selections processes undertaken during their development teams’ career can be low.

Concepts rely on relationships and contracts with external parties such as governments, partners and financiers. The goal of selection is to examine possibilities and choose the most promising to work up in more detail. Detailed work-up of ideas costs a lot and takes time, so it’s best to avoid re-doing it. Selecting the best concept is important.

Selection is through an assessment of feasibility and creation of valuable approaches for external parties aligned to their appetite to participate. Better modelling of risk/reward profiles and the ability of AI and machine learning to help with option generation and assessment will create change here.

To generate viable options and to make introductions to partners requires wide experience, a diverse set of skills and set of external relationships. Companies like io oil and gas consulting, Genesis and Xodus and others are go-to companies for serious operators.

I4.0 will likely impact the work of these companies in three ways:

  1. Technologies will be adopted that enable better collaboration between parties and facilitate the exchange of information. This, by itself, will result in faster turnaround, reduced waste and lower cost.
  2. The value of networks and relationships cultivated by consultants to both source work and to introduce in related parties may reduce. Part of the value captured is because of “knowledge gaps” where players do not know all the parties who may be interested or how to contact them. It is likely that some of this role will be augmented with the introduction of automated agents. Reputation and relationships will still matter and will become a differentiator but it is likely that the ability to command premium pricing will be reduced.
  3. The value of experience will almost be wiped out. As an anology, consider how an app on a smart phone has given Uber drivers the ability to use the backroads of London like a black cab driver with 30 years personal experience. In concept generation, a system that constantly learns all the options that have been considered before around the world, combines them and add twists will provide novel approaches. This will mean the creative aspects of concept design will become increasingly automated. There is the potential for a google-like winner-takes all first mover advantage. Captured experience will lead to more opportunities for work and hence generate more experience. Perhaps we will see one consultancy decide that the value of knowledge will be higher than the cost of acquiring it and will start to work under cost price for the chance to become the first mover?

For concept selection, the differentiation will not only come from the depth of the accumulated knowledge-base but also from the ability to deploy emotional intelligence and maintain the confidence of stakeholders. Like many I4.0 opportunities value will be generated from blending human soft-skills with an ability to harness the power of the new machines and ways of working.

DESIGN

There are various iterations of design and “stage-gates” on the way from Front-End Engineering Design (FEED) to Detailed Design. For the sake of this piece I’m going to be broad brush in my approach, so apologies in advance to my engineering friends for the lack of detail.

There are four main areas within the Design phase that need to be addressed

  1. The engineering of components and systems
  2. The co-ordination of the design activity
  3. Developing the commerciality of design
  4. Maintaining the consistency to the original concept

Engineering of Components and systems

The engineering process has already been revolutionised by computational power and the transportability of information. No longer are drawings done on dye-line blue prints and red-lined in pen. The computation revolution included things like reservoir simulations, process simulation, CAD drawings and automated parts lists.

I4.0 will enhance the process by enabling more simulation – such as simulation of construction, operations and maintenance. The type of output that will be required is likely to change. As operations move towards more “digital twin” enabled processes the as-designed, as-built and as-operated models will require more detail and will contain more information. Therefore, I4.0 approaches will need to enable this by, for instance during design, automatically populating tags for equipment, and providing normal operating ranges for instrumentation alarms – all pulled directly from manufacturers information.

AI systems in the future may even make suggestions for more efficient ways to plan pipe-runs, or help by making suggestions concerning process engineering or instrumentation. For an example of this type of guidance look at MS Word. As I type I get underlining in blue when I misspell a word and in red when my grammar is off. This is accompanied with helpful suggestions should I want them. Engineering design systems in the future may do similar tasks alongside engineers.

We will see an ever-expanding repository of re-usable design components which will continue the progression towards modularisation and re-use of designs. Standardised design and construction (where appropriate) will reduce costs. Couple this with additive manufacture for specialist parts and design work may move away from run-of-the mill towards assembly of modules and the design of very specialised be-spoke components.

Co-ordination of design activity

Anyone who has worked in a large-scale engineering design office will say that much of the work (and most of the errors, and most of the wasted cost) is incurred in the co-ordination of work between engineering teams, subcontractors and clients.

Firstly, there is the problem of controlling the internal integrity of the design. For instance engineers need to know things like flow-rates, reservoir pressures numbers of wells etc. I have seen examples where the reservoir group and the engineering team will progress for quite some time when their assumptions have diverged. Same goes for all upstream-downstream processing, topside weights, fire-systems and instrumentation. This is an information co-ordination problem. I4.0 will help because there will be fewer engineers required to work on design due to automation (therefore less co-ordination will be required) and where it is by “compiling” the individual designs into an evolving a digital-twin, techniques from software engineering used to co-ordinate source code, apply updates. automate error checking and run-test can be used.

Sign-off between clients and contractors means document trails can be an immense source of frustration. The control of versions and reviews often needs a whole department of people dedicated to it. Contracts are tied to sign-offs and all have time-limits that must be observed. I4.0 has the potential to apply block-chain audit trails, digital twins, version control & roll-back. Block chains can even be used to automatically transfer value between parties to settle contracts.

Managing Interfaces is a whole discipline. Different teams – sometimes different companies – design parts of the plant and it’s important that pipes and wires all match up when the modules are constructed and delivered. Again, design component submission into a digital twin from the start can detect problems and eliminate this type of error from propagating.

There is schedule and cost management activity required. Normally this is either a P6 or MS-Project based process with email reporting of progress required from all parties and manual co-ordination to provide an approximation of current progress. This tries to highlight any dependency busts that have blocked the project and provide forecasts to management. Designing within a digital twin, where the design process can be simulated, will enable a risk-based approach to schedule and cost management to occur automatically providing visibility and risk-control.

Developing the commerciality of the design

I4.0 will require changes to decision process here so it may take a while for things to change. Good design provides both construction-dividends and operation-dividends. Designing a plant in a way that eases construction currently increases the cost of the design work. I4.0 automation may reduce or eliminate this extra cost, but the types of material or module may be more expensive to buy but cheaper to construct – balancing those trade-offs are difficult both because the information and the consequence of decisions are not visible to everyone, and procurement rules may favour a least-cost approach. I4.0 may help to present more transparent business cases and enable more informed decisions to be made.

Operation-Dividend is a hard one to address. Even today there are information, models and design files that can be created and could be of great value to operations. They may never be created or delivered to clients because of cost pressure. Many Oil and Gas project procurement processes are based on lowest cost supply – this drives out added extras which are not strictly necessary during design, but would have produced dividends later in the life-cyle of the project.

It is my belief that as I4.0 techniques become established and drive efficiency in the design process there will be a natural tendency for these dividends to arrive, however by forcing the issue now – operators could accelerate the adoption of I4.0 techniques among their suppliers. This they should do.

Maintaining Integrity of the Concept

It is a phenomenon that design decisions are taken for good reasons in the depths of the project and these can, unintentionally, add up to a fundamental change in the economics of the concept. These decisions are taken for good reason, but without the impact being fully understood.

A forward-thinking operator may employ representatives from the concept team to assure that, during design, no decisions are taken that alter the concept economics without due care and attention. Even then, however, it may be very difficult to detect all the small changes that may add up to a problem later on.

I4.0 means not only will this process be built into the “compiler” checks for the design but also it will have knowledge of the assumptions underlying the concept. The project owner will be able to track real-time fit between as-designed facility and the expected concept value. This means also that the validity of the concept economics can be monitored as external events challenge the assumptions made. By setting appropriate risk-tolerances actions can be taken to steer the design of a project faster the normal stage-gate system.

CONSTRUCTION

This is a huge topic with lots of interaction between subcontractors, yards, welders, steel producers, component suppliers, machinery vendors, site logistics etc. etc.

The order in which construction happens can be crucial. it’s an involved problem to solve because sometimes something can’t be built until a large piece of equipment has been manoeuvred into place. Sometimes there are contingencies such as road access or camp construction or constraints on access to lifting equipment time. These types of scheduling problems, full of constraints, dependencies, lead-times and uncertainties interact with physical layout. This type of problem is one that AI based simulation and machine learning has demonstrated value. I expect that the planning of construction work packages and sequencing will become much more efficient with I4.0.

I4.0 is likely to deliver technology in terms of sensors, autonomous survey vehicles and 3D model population that provide accurate tracking of progress against schedule. There are likely to be wireless condition sensors provided with equipment (such as rotating machinery) to ensure that it is properly maintained in the time before delivery and installation. This will save pre-installation maintenance and avoid delays.

We are likely to see technologies such as automated reporting of equipment positon on the way to site meaning that schedules and daily work-plans can be optimised to the prevailing circumstances.  Block chain style transactions are likely to reduce the formidable overhead associated with contractors and their execution of work. The same technology that can provide better than ISO-9001 trail of supply, fitting etc.

I expect to see more automated construction, on-site manufacture of parts (3D printing) and more just-in-time manufacture and delivery of equipment. All of this is going to combine with better visibility of progress to lead to faster construction, with less waste (and hence less cost) and deliver much higher quality and predictability.

COMMISSIONING

Commissioning of new plant as it starts up and before it is presented to operations is likely to change too. Simulation and machine learning will mean understand that different parts of the plant need to be brought on-line and in sequence. Experience and learning will shine a light on areas most likely to fail and their failure modes. Some potential failures will be detected and ironed out prior to attempted start-up.

As plant is brought on-line equipment settings will be noted in their “tags” and can be automatically compared to the as-designed expectation and the manufacturer’s recommendations. Equipment performance can be tracked through sensors from day-one, this will help with predicting failures in the future. Intelligent software can be used to highlight unexpected situations.

The automated learning and recording of the start-up procedure and equipment settings can be used by operations for plant re-start and for comparing as-operated settings to as-commissioned to track where, when and why parts of the plant are being changed.

CONCLUSION

Industry 4.0 has the potential to provide substantial benefits to the development of oil fields. Many of the processes described above have been technology back-waters relying on manual systems, ad-hoc applications and a lot of tacit know-how. I believe that this will change. It will take new technology and also take new ways of working. The supply chain will need to respond. FEED, EPC and PMC contractors who get on the bandwagon first will create the opportunity to assemble unassailable leads and take a dominant market-share in the way that Google did for internet search.

(Image credit : https://www.gie.com/)

 

 

 

 

 

 

 

 

 

O&G – Exploration and industry 4.0

In an earlier post [LINK] I briefly introduced the four areas of upstream value chain that could benefit from the 4th Industrial Revolution. Here I put forward some potentially controversial points about how this may (or may not) affect Exploration.

First of all my definition: Exploration is concerned with finding and appraising new deposits of Hydrocarbons trapped under the surface of the earth. It’s the identification of these that I am addressing here, not how (or if they can be) exploited.

There have been many advances made in technology in the previous 25 years that have transformed the process of finding deposits. The two most notable have been around the use of remote sensing through Seismic Data, and the accuracy with which deviated wells can be drilled. Seismic acts like an x-ray into the composition of the rocks, while new wells use precision direction control and combine it with analysis of real-time feedback from rock measurements surrounding the drill bit to let operators steer the trajectory in real-time.

Many of the advances that have been harnessed could legitimately be described as pioneering in the technology of sensing, big-data, simulation and automation. These are the key technologies underpinning the 4th industrial revolution. Exploration got there first.

In my work with small companies seeking investment I continue to see a slew of new start-ups with fancy seismic algorithms claiming to be able to spot even more obscure sources of previously unidentified hydrocarbons. Maybe they work. Who cares?

In my view the major gains from the 4th Industrial Revolution have already been captured in exploration. Perhaps we are close to entering an era of more stable oil prices – driven by: elasticity of supply from shale; abundant reserves released from both tight reservoirs and hydrates; and managed demand through smart technology, electric drive-trains, renewable generation and batteries. So the commercial pressure to find obscure resource pools may have gone.

In the North sea there are over 300 pools of hydrocarbons already discovered but not yet developed [LINK]. So the question is: even if the new technologies are successful will they have a significant impact for operators? I suspect the answer is no.

New algorithms and systems may provide marginal gains around the edges of existing fields and provide additional in-fill development opportunities. They may reduce the number of people in G&G dept 10%. Commodification of techniques (as happened for 3D animation) may see the demise of some companies and job-roles. But I don’t think it’s going to provide a revolutionary impact. Of course, I may be wrong.

If I am right, this suggests that there will be two main opportunities for companies providing technology here – either to provide an “add-on” to the main interpretation platforms (Petrel, OpenWorks) and then sell small numbers of seats to operators in special circumstances, or attempt a wholescale assault to replace the platforms already in place. Neither of these are revolutionary for operators and result in minor cost reduction by pitting service company against service company.

I think the 4th industrial revolution is likely to provide only a small impact on the dynamics of this part of the value-chain. There may be a displacement of revenue from one software vendor to another, there may be some marginal in-fill development opportunities that will add more elasticity to oil supply (and help to further stabalise prices) but neither of those are going to be massive nor revolutionary. I think that the 4th Industrial Revolution gains have been captured already – AI, auto-pickers, attribute statistics, simulations, integration, cloud, geolocation, computing power in the hands of individuals – the main technologies are already in place. Gains from here-on-in will be marginal.

There is one thing that may change my view, however. If this happens it will have a profound impact and swing power towards the national resource owners. If these innovations are adopted at the level of the nation state things may change.

National Data Banks were established in the 1990’s (example LINK) to hold archives of seismic and well data and make them publicly available. These may get a boost.  Cloud technology and on-line AI-based mining-algorithms may change the way that license economics work by de-risking exploration and encouraging competition. If this is combined with a stable oil price there is a potential recipe for reduction in the incentives needed for exploration companies. That could change the economics and the structure of the discover, farm-down, refinance, develop and keep carried-interest process that is used today.

Automation Risk

We have recently seen some very high-profile failures of IT systems – whether by Cyber Attack (WannaCry and Varients [LINK]) or by poor operation risk control [LINK]. Something is wrong in the way things are managed

Insurance companies are starting wake up to their potentially unexpected liabilities around this risk [LINK]. They will certainly start to price this risk; this will mean that risk-reduction investment will have an immediate identifiable financial benefit. Perhaps it’s time to take a different approach

For years, I have railed against the distinction between “IT and the Business” [LINK]. I think the time has come for this gap to be finally put to rest. They are now truly inseparable.

Statoil has recently announced its response to the 4th Industrial Revolution (or Digitalisation). [LINK]. The most significant part of this is that the initiative is led by the COO. Of course it is!

If we were to take any other industrial process technology in Oil and Gas (say compressors) the management line looking after these runs up through the COO. There is not a Chief Compressor Officer who reports through the CFO and imposes compressor decisions (or delays) onto operations.

In that case, why would computerised operations technology report through the CFO via the CIO as is the case in many organisations. This should be part of the COO role. This recognises that IT is now central to operations. This is how it should be, because it is now at the heart of the current automation of operations [LINK].

In line with this I propose that the COO needs to own a new risk – Automation Risk. One of the advisors to the COO would be an Automation function – responsible for assessing and managing benefits and risks as well as defining policy across the organisation. Operational assets should comply with policy and work with Automation function around standards, risks and controls. Assurance functions can then audit compliance via the Chief Risk Officer – the right checks and balances would have saved a few hundred million dollars across the airlines in the last 12 months, perhaps a billion dollars across the industries hit by the WannaCry virus.

Automation risk planning means that the COO knows what risk sources there are that cause automation systems failure and what is the consequences of that failure would be. Importantly, he also knows what actions could be taken to reduce the likelihood and impact of a failure.

Then he can approve a business decision to choose which reduction measures are implemented. The COO must then know the real-time status of the mitigation measures – are they ready to work in case they are called upon and what is the current risk status of the organisation, is it acceptable and – if not – what is being done about it?

Contact me if you want to know about planning using Bow-Tie models and how you might be able to monitor current compliance in real-time, set-up alerts and manage your automation risk.

[Image credit: https://smlrgroup.com/universal-cyber-risk-model-part-1/ ]

Levels of Automation in Oil and Gas

One of the aspects of the 4th Industrial Revolution [LINK] is automation.

I am often asked if automation could ever add value to oil and gas? I contend that there is a good reason to think it will because benefits could come from:

  • accurate control and adjustment leading to higher throughput;
  • self-repairing (or self-scheduled repair) leading to higher uptime;
  • more accurate execution of plans by reducing manual error; and
  • less distracted management leading to higher-value decisions;

Many people are sceptical that we can ever “automate” an upstream oil and gas production facility.  I agree that “full” automation is unlikely, but I would argue that partial automation is already present. What we need then is a way to describe “levels of automation” so we can define what we are talking about and reach agreement about what to do.

Autonomous cars are in the news for good reason right now, and are making great progress. Perhaps we can create a common language for “level of automation”. The SAE introduces itself as:

SAE International is a global association of more than 128,000 engineers and related technical experts in the aerospace, automotive and commercial-vehicle industries

It has defined 5 levels of automation that has now been adopted by the US Department of Transport. Here’s an explanation from wired magazine [LINK].

What dimensions could be used to classify degrees of oil and gas production automation? My definition of the process of automating is: “Reducing the friction between sensing what is going on and taking the right action”. This can be used to underpin a maturity matrix.

If we can sense what’s happening, reduce the friction before acting to zero and be exactly right in the actions we take – then this achieves perfect automation. Of course this then leads to the need to define in more detail: sense, friction and right.

Some of the steps that can lead to more automation are:

  • Improved sensing of the environment;
  • Distribution of sensing data;
  • Transformation of data into information (e.g. production vs. target);
  • Exploring related information, history and context (e.g. maintenance records)
  • Understanding the consequences of the information presented (prediction);
  • Checking possible actions and their impact (simulation);
  • Delegating decisions to reduce management delay; and
  • Feeding back observed results to scientifically tune future predictions (learn)

Contact me for more information about how it is possible to incorporate the above into an assessment of your organisation’s automation maturity and readiness for change.

 

 

 

 

 

BA IT and my Eggs Florentine

So it happened again yesterday. This time BA cannot handle luggage. A bit of an issue for an airline. No word yet as to the cause [Link] but it did mean that checked baggage did not travel with BA passengers.

I took a risk and travelled BA this last weekend (I was using air-miles for a trip to Romania where I was helping out a friend with a project in the hinterlands there) and the experience was pretty reasonable but even I had problems. This time my probelms came from catering.

I flew in the business class cabin  (or as BA call it Club Class – hopefully not a reference to how they will quell any dissent).  In any case there was all this pomp and ceremony involving hot towels and a menu for breakfast as we took off. I was given the choice of Full English or Eggs Florentine. Printed on some stout card in a very grand font. Sounded lovely. There were only 8 passengers in the cabin. But they packed only 4 of each menu item. Zero redundancy. So as I was the last to be served, I didn’t have a choice. The menu should have read – you’ll get what’s left over out the two possibilities or go hungry, good luck. People only got a choice until 4 people had chosen one option, and then there is no longer a choice. So, as my Texan friends would say, that fancy menu was all “Hat and No Cattle”

It seems that catering, like IT, didn’t use risk management and uncertainty when designing their systems. This highlights the danger of over-assuming and the consequences of running at minimum cost with no redundancy. Redundancy, like insurance, can be seen in hind-sight as wasteful. Sometimes it’s called “over engineering” – but only if it wasn’t needed. If the laws of probability catch up with you, it is really a prudent investment.

Risk management has three elements:

  1. Identification of the Risk, what can cause them and what the consequences will be
  2. Taking prudent steps to reduce the probability that the risk will materialise
  3. Making preparations so that if the worst does happen the effects are mitigated

Giving BA the benefit of the doubt and assuming that they knew that their IT systems may fail, the baggage system may halt, and that they may run out of eggs florentine, then I have to assume that BA management now hate their passengers.

The consequence failure seems to be  inconveniencing passengers.

Probability of Failure: At no time did they take steps to reduce the likelihood of tripping their whole IT system by pulling out a plug [link] (perhaps they might have double ported UPS, or Duct Tape over the plug!) or running out of my choice of breakfast (by perhaps packing say 6 of each choice, rather than 4).

Consequence of Failure: Let’s just inconvenience everyone – was not mitigated either, it was just accepted with a shrug. Though to be fair baggage was re-routed and couriered and people were re-booked on flights. I got an extra Bloody Mary. But still feel that it would have been better to reduce the likelihood of failure, as the mitigation seemed to be the minimum anyone could expect – and may be a legal requirement (except the bloody Mary of course, which probably the EU would not mandate)

Good luck with preserving the premium pricing for your Brand BA.

In a future post, I’m going to argue that as operations now rely on IT to deliver (and even more so in the 4th Industrial Revolution) it’s quite wrong that much of IT comes under the purview of the CIO rather it should now be part of the remit of the COO. Risk management of operational IT should focus on putting barriers in place to interrupt the cause and consequence routes, using an operational function lens, not an IT one. I Bet that is going to cause some organisational “team work” issues when it comes to budget time.

 

 

BA – Blinking Awful?

I haven’t posted for a while as I’ve been very busy working with clients on industry 4 projects, but the recent IT outage for British Airways (BA) requires a response. (for more information on the BA IT outage follow this [link])

In August 2016, I examined the cost of the IT outage to DELTA airlines [link]. I calculated that this must have cost DELTA at least $60m [correction:  with related costs the post says it would be $100m]. In the wake of the BA story the FT published an article over the weekend [link] looking at the top 5 IT outages. They tell us that DELTA believed that it cost them at least $100m.

We’ll wait and see what effect that BA outage has on their revenues – but IAG (the owner for BA) declared a profit of about £2Bln for 2016, so there is a chance that this will have the possibility to knock 10% off the earnings for 2017.

Now – in an eerily similar set of circumstances to Delta – the company had recently outsourced their IT and they experienced a “power surge” and the back-up system didn’t work.

The following seem likely to me:

  1. The digitisation of business has happened and is accelerating, IT systems are not peripheral to operations they are now crucial.
  2. The creation, management and care of these systems are critical, but it appears that there is no-one on the senior leadership team who is on the case.
  3. The focus on “business cases” for IT investment don’t consider the transformation of current business operations, nor the risk of “not investing”

This posts talks about the need to prepare non-linear business cases [link].

The Oil and Gas industry (and others) are becoming rapidly digitised and will require different investment decisions around IT. It is no longer appropriate to concentrate on cutting costs, driving standardisation and outsourcing the activities. In operations “IT” is now critical to business success. This means good investment decisions drive competitive advantage and loss of IT capability can cripple the business.

It’s just an analogy

I’ve recently been working on analogies designed to let me talk about Industry 4.0 concepts. In short I’ve been trying to find ways to explain what’s almost unexplainable, and often to a sceptical audience. This is my current favourite:

Here’s what happened lasttime

In 1993 the Internet was explained in terms of bits, bytes, modems and tunnels. Most people had no idea why this geeky stuff would be important or what it could possibly be used for in everyday life. By 2003 it was explained in terms of Amazon and Facebook. Now my mum can order shopping on-line but has no idea how the Internet works. That’s how it should be, invisible to the application. My niece uses Facebook, WhatsApp and ASOS and can’t really imagine not using them – it’s woven into the fabric of how she does things, she’s never done it any other way. Why would she? In the mean-time those that had no idea what the geeky stuff could do ignored Amazon and are now closing their retail space [link]

Here’s what’s happening now

Industry 4.0 is now explained in terms like sensors, internet-of-things, and security. There is little understanding of how to retrofit this into existing ways of working, or why all this geeky stuff is relevant. In short people think this is a nice to have but really changes nothing. In ten years I will be explaining this in terms of its application and not how it is implemented. Industry 4.0 will be a forgotten concept and we’ll be talking about its various applications – like operating and maintaining according to equipment condition. In 20 years time a maintenance engineer (like my niece does with Facebook) will have no concept of why you would (or even could) operate equipment without on-line condition monitoring, system level surveillance, and connected “helper applications” that learn from global failure modes. Why would she?

But surely we’ve already been here?

I normally get an objection at this point along the lines of this:

“We’ve had digital oilfield for years, and it’s promised a lot, cost a lot and not delivered much – why will this be different, why should I think there will be a change.”

In my view, things no longer change incrementally when platforms become ubiquitous and costs tumble 1,000 times. They “take off”. That’s what’s occurring now. Add to these exponential technologies such as machine learning (which self-improve with time and experience) and t the stage is set for big breakthroughs.

Four companies: Facebook, Google (Maps +Waze), Uber, Amazon would be impossible without the widespread adoption of horizontal general technologies. They’re interdependent and co-ordinated rollouts enabled cross-platform co-innovation at the application level.

By the way – If you think these companies are just fluff : Google is worth 356Bln, Facebook 350Bln, Uber 62Bln and Amazon 250Bln. In total over a trillion dollars. For comparison Exxon is valued at 360 Bln.

Adoption Curve is reversed

Here’s another thought – In the 1960’s Military and Space applications were modified for business use before finding their way into the hands of rich consumers a couple of decades later. Facebook-like platforms and messaging applications such as Skype emerged first in the consumer space before being adapted for corporate deployment.

I think this mode of adoption is now true for application level innovation generally. If this is so for our next wave application innovation for industry 4.0, I expect to see it emerge first in the consumer space, deploy rapidly at scale and be ready to find ways to adapt and deploy in industry. It will be people like my niece that will know how to leverage these applications with no need to have any knowledge of how the underlying infrastructure works.

Keep your millennials close at hand; you’ll need their insights.

Image Credit http://parterre.com/2011/12/01/interrrupted-analogy/