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.

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?

To innovation and beyond – 2019+

My first post of the year – a look ahead for 2019 – was a bit tongue-in-cheek. Now The World Economic Forum (WEF) is meeting in Davos, Switzerland, I thought I would provide a more insightful analysis.

The WEF will be considering the implications of the 4th Industrial Revolution as the headline theme for their annual conference. If you’re new to all this here is a I4.0 primer from CNBC [Link]. 2019 is going to be a year where industrial innovation takes centre stage. 

The thinking from WEF is always good, detailed thorough. I think that some of the crucial themes for unlocking innovative value will be focussed around opportunities and risks. Here are some of my current favourites.

The Opportunities

  1. Using information and reconfigurable platforms to provide new solutions to stakeholder experience. This will establish new ways to create, deliver and consume the core outputs from industrial processes.
  2. Removing the idea of separation between “IT and the Business”. The two are now conjoined. Being good at tech will be a prerequisite of being good in business. Technology will be embedded in every way that work is done, products are created, consumed and delivered.
  3. Empowering the front-line will be crucial. The winners will be faster organisations where workers make autonomous decisions and are rewarded for outcomes. As an analogy think of Deliveroo drivers. For many reasons, more refined models of work-coordination are required but the core autonomous nature of the work is being previewed here. Decentralised decision-making and autonomous action guided by technology removes many of the tasks performed by middle management. I hope we will start to see teachers, dentists, doctors and nurses no longer filling in spreadsheets and working as relecutant automatons directed by ill-informed command-and-control resource-allocation systems.
  4. With power comes responsibility. Without middle management, new forms of controls (and motivation) will be needed to spot problems and reward behaviour. Surprisingly for some, I don’t believe it is the front-line worker, but middle management, that is most under threat from AI, visual computing and big-data. I hope the CFO won’t push progress only on AIQ but that marketing and talent managers will push the AEQ agenda. It’s important we understand not only economics but also pride, satisfaction and feelings of accomplishment.
  5. Innovation may not be in new forms of technology. The tech available to us now is far ahead of our application of it. Deployment options are already available but not used. Innovation will come from the application of existing technology to new areas of business. Those stuck with old infrastructure will not be able to reconfigure fast enough to keep up. Value will arise from designing new ways of working. Capturing the value will rest on finding ways to get the rest of us to work that way too.

And now the risks

  1. Innovation will come from networks. Big companies will look to small companies for ideas, small companies will be formed from collaborative networks of individuals. Ideas will be mashed-up to cross-fertilise creativity. Guards must be in place to avoid exploitative situations – if they arise unchecked it will mean that the small-guys can’t and won’t play for long. Without them, brilliant ideas will never be used. Rights management is crucial for the distribution of the value created. In the way that song-writing credits generate performance fees for artists. Licenses for ways-of-working are needed to stimulate innovation, and society needs to enable easy access to legal enforcement to uphold claims against copying without permission.
  2. Massive generalisation follows: Young people are frustrated by old-people’s inability to embrace new ways of working. Technology savvy folks are orders of magnitude more productive than their peers. They are quicker to make decisions and to multi-task. This leads to not only high-productivity but also to high-error rates. Iterative short-cycle experimentation and learning-by-doing is the hall-mark of agile strategy. This is not an approach that has been adapted to high-risk industrial work-settings. This leads to a clash of culture and an inability to attract and retain talent.
  3. Innovative individuals will continue to pursue independent careers in increasing numbers. Old industries will die, vested interests will be disenfranchised. The world of work, taxation, social contracts, pensions and access to finance will have to evolve to cope with this. To create a consensus and establish a sense of fairness new-politicians will need not only wisdom but also to deploy the old-tools of oratory and persuasion. There will be big disagreements across society and between nations. It will be necessary to create hope for those who fear being disenfranchised. They will not go quietly into that good night.
  4. Politics of property will come to the fore – the control of assets will be important. Whether that is physical real-estate where low-paid important workers are unable to afford to live where the people who need them reside; property from an accumulation of historical data that provides an unassailable lead and monopoly positions; or the “IDEA” that one person has spent 10 years creating that is exploited by a large corporation without reward. Society will need to find ways to address the control and distribution of property in a world where labour and working-time may not function as a distribution & motivation method.

I will spend time exploring these themes during the year – I have a number of initiatives already kicking off for the year and I hope that you’ll be able to help.

Industry 4.0 – Are we there yet?

Something’s up. I was reviewing my web-stats for this site and I found that since Jan 2018 one post has been read 1000’s of more times than any other one. This one: Innovation and Productivity with the Fourth Industrial Revolution.

About 3 years ago,  I started to talk about how relevant to the Oil and gas industry were things I had been studying about the 4th Industrial Revolution. At that time no one had a clue what I was talking about. Some thought I may have gone mad. I’m glad to say that things have (mostly) changed.

Who’s talking about it?

Since 2018 I’ve seen the term being used by a number of my peers in Oil and Gas, and when you search on this and combine with the term “Digitalisation” It becomes obvious that there is a major movement underway.

Steve Ashley uses the term in his article: Digital, data and taking control of our own destiny and its also used in this press release from Petrotechnics.

Today this announcement from CapGemini helping Statoil with a 3 year project to create a digital roadmap.

How to assess a digital project

There are still a few fundamental questions that every digitalisation project should be able to answer. Below is your starter for ten, and – yes – you may confer:

Examine the project value:

  • What is the primary business driver;
  • Does this project fulfil the agenda of the CIO or the COO;
  • What is the net cost of this project;
  • When will I see value (hint: the choice is either this year, or 3-5+ years out); and
  • Is this a platform investment or can it ride a set of network effects?

Examine the project risks:

  • What things will prevent me from realising the planned benefits;
  • How much change will be required in the way we work;
  • If I do this, does it prevent me from doing other things;
  • How likely is it to require more money than I’ve budgeted for; and
  • What happens if I wait and do it later?

These criteria (and others) should be considered for every project.

Enterprise level

At the enterprise level there are a couple more  things that need to be considered including:

  • How does this project fit into my portfolio of digitiallisation initiatives;
  • As my business gets more digitalised, how am I addressing digital integrity; and
  • Should my HSSE function be renamed HSSED?

Further reading

I wrote an article for ITProportal for some of this and here is a note on project portfolio prioritisation that I wrote a few years back – Introduction to Prioritisation V 1.0.

Digital disruption landscape for upstream oil and gas

I was recently asked by a client for assistance in examining how their business strategy might be affected by Digitalisation. This company is a mid-tier upstream operator with a mix of assets mostly non-operated but does have some where it is the duty-holder.

So I’ve propose the following five point map to classify where the disruption could occur in the upstream business. This helps to define not how, or when,  but where disruption is possible. This framework helped us examine what threats and opportunities are likely to emerge in each area and I thought I’d share.

Please feel free to comment and I will keep this updated  for the network.

Demand for Oil and Gas

  1. Digitalisation in the wider economy may affect the demand for energy through different transport usage, renewable control, demand management and micro-grids.

Access to Resources

  1. Access to operate resources may change as national owners find different partners to help them monetize geological wealth
  2. Opportunities to become a non-op partner may change as operators get more certain about their outcomes and require less diversification in their portfolios
  3. Competition for resources increases as development and production services become purchasable/tradeable activities
  4. Transparency of operation and methods to extract changes what is required to retain a license to operate
  5. Better techniques for collecting and interpreting data leads to more resources being found and better development pre-planning (westwood puts the commercial success rate at between 30% and 50% https://www.fircroft.com/blogs/less-drilling-more-success-the-state-of-exploration-drilling-so-far-in-71921114313 )

Development and Operation of Resources

  1. Digital planning and modelling combined with better logistics and manufacturing/construction techniques reduces the capital requirements for fields meaning lower barriers to entry
  2. High frequency low-cost drilling reduces the sunk-cost nature of investment, reduces cyclical volatility of supply/demand imbalances and hence expected return on capital
  3. Better information leads to increased recovery factors and ultimate value of assets. (Currently this is estimated to be below 40% http://www.spe.org/industry/increasing-hydrocarbon-recovery-factors.php )
  4. Better information, co-ordination and reduced waste leads to lower operating costs per hour of activity (for some bench marks check here https://knoema.com/rqaebad/cost-of-producing-a-barrel-of-crude-oil-by-country )
  5. Better prediction of failure and real-time optimization of fields leads to higher efficiency and hence accelerated cash-flow (currently 73% in North Sea https://www.ogauthority.co.uk/news-publications/news/2017/uk-oil-and-gas-production-efficiency-rises-to-73/)

Sale and transport of product

  1. Better information about the crude quality and refinery / other consumer plant configuration leads to higher yield / lower cost processing
  2. Information about the location of supply and demand enables better optimized transportation and reduced costs
  3. Better prediction of both future production and future demand enables balancing of both. This leads to changes in the premium available from trading and who captures it

Human elements

  1. Automation leads to different models for distribution of wealth among the middle classes (no longer based on work)
  2. Automation leads to people choosing to add creativity and seek challenges in different environments and under different conditions
  3. Changes to the working motivation scheme means modernization is required for operating model for Oil and Gas industry to attract talent

 

 

Image credit: https://www.pmfias.com/natural-gas-distribution-world-india-petroleum-gas-value-chain-upstream-midstream-downstream-sector

 

 

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, Granherne, 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/)