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Where the digital oilfield succeeded, by Peter Black

Monday, October 10, 2016

A review of recent technical papers on the 'digital oilfield' shows that some of the biggest successes has been through facilitating better collaboration, not, as previously thought, from connecting up an oilfield digitally. By Peter Black, managing director of EnergySys

Back in the early 2000s, my company was working for one of the large oil and gas service providers.

They were interested in developing technology that would facilitate collaboration among disciplinary teams, and reduce the friction and errors caused by the overly-formalised interchange of data between the groups.

We all believed that technology was the answer, and we set about investigating ways to link data together, as a 'digital oilfield'.

The facilities engineers, for example, were given a defined set of information from the reservoir engineers, but this often omitted information that later proved to be vital.

It wasn't sufficient to simply extend the dataset that was exchanged, because where would that stop? Also, what was important for one asset might not be significant for another, and one group didn't understand clearly what the other might need.

My view was, and still is, that trying to create a single database to store all data was impractical and unnecessary, and what we needed to do was create an information bus that would allow us to aggregate information from heterogeneous data stores. With this, together with a knowledge repository and discipline-specific tools, the information could be accessed as required, and updates would be shared seamlessly with the whole team.

But as it turned out, the impact of these technology advances was limited, and the change that had the greatest impact was the accidental creation of a physical meeting space which could be used by people from different disciplines to discuss and share their ideas and challenges.

In short, the greatest benefit accrued from people and communication rather than technology and process.

Looking back, it was probably not important that the meeting space was physical, and web conferencing would have been equally valuable; the key point was the discussion that ensued.

This was a salutary lesson, and it is evident that it has been learned and re-learned by others before and since.

A study of six major oil companies (Quaadgras & Edwards, 2013) highlights that organisational change, team working and coaching are as important, if not more, than technology.

The 'Digital Oilfield' offered improvements in automation and integration that would deliver real value in cost savings and increased production.

Unfortunately, it does not appear that much of this early promise has been realised across the industry, and the focus has sometimes been on technical innovation that is often disconnected from business benefit.

Unclear goals

Digital Oilfield has not been associated with a clear and unequivocal set of goals that could produce real and dramatic performance improvement. And despite substantial investment, the positive results that have been achieved have not been translated into a set of accessible best practices.

Looking at the literature, Crompton provides an overview of business process and technology transformation, and suggests that progress has stalled (Crompton, 2015). Meanwhile Cramer says that the digital oilfield is 'a somewhat ill-defined, misunderstood and abstract concept.' (Cramer, et al., 2012).

Feineman says that 'understanding the actual state of digital oil field implementation across a portfolio of projects and assets in a single operator, or across operators today, is almost impossible due to the lack of consistent definitions of what constitutes a digital oilfield.' (Feineman, 2014).

What are the drivers?

Many digital oilfield publications highlighted the increased cost of production, the complexity of field development, and the so-called 'great crew change' as drivers for digital oilfield.

However it might be argued that the current environment, with significant reductions in new development efforts, has mitigated those concerns.

There is evidence (Control Engineering, 2015) that companies are actually dispensing with the services of their most expensive, and most experienced, staff, though operators in the Middle East might benefit (Dutto, et al., 2015).

Change management

Culture and resistance to change are most frequently cited as key barriers to adoption (Saputelli , et al., 2013).

It has been suggested that 'the pace of change in the oil and gas industry is on the order of 15 to 20 years or more' (Davidson & Lochmann, 2012).

Change management is key (Berger & Crompton, 2015; Ruvalcaba Velarde, 2015), but there is limited evidence that the industry is investing in the soft skills that would be required for any significant shift.

Several references argued that 'the formation of a small, central empowered team is the most effective means of achieving the adoption of iE [intelligent energy] practices, principles and associated technologies in a multi-asset organisation.' (Edwards, et al., 2013)

Return on investment

Many companies find it difficult to be certain of the expected benefits of digital oilfield. This is compounded by uncertainties over ROI and aggregate value at the business portfolio or corporate strategy levels (Al-Mulhim, et al., 2013). The suggestion that digital oilfield programmes progress in five-year phases (Dickens, et al., 2012) implies a longer term view than many might feel they can afford right now.

Others point out that digital oilfield projects 'move too slowly for the type of organisation that is delivering them, or alternatively, that there is too rapid a rotation cycle in the organisation for the type of change delivered by a typical [digital oilfield] project' (Gilman & Nordtvedt, 2014).

In addition, while a pilot might be considered a success, it is demonstrably difficult to scale this to a whole organisation (Davidson & Lochmann, 2012).

Claims of value delivery of over 70 mboed net production (Dickens, et al., 2012) are obviously appealing, but it is simply one data point.

Chevron has attempted to address this challenge by leveraging a central development approach that looks for common solutions across business units (Bourgeois, et al., 2015). If programmes are to find general applicability, the techniques and tools must be standardised and, to some degree, commoditised.

Automation and data

While the automated processing and analysis of data to support decision making is perceived to be increasing within oil and gas, it is still immature compared with other industries.

There are some extremely impressive reports of success with analytics (Turk, et al., 2013). This paper shows how Devon Energy established a dedicated analytics team and, working with external consultants and software vendor, created a proof of concept and several pilot projects.

Devon believes that it was able to deliver decisions faster and more cost-effectively over traditional domain analysis. In order to ensure clarity internally, the team developed their own definition of analytics, 'the discovery and communication of meaningful patterns in data.'

One of the most vital conclusions concerned the importance of identifying good questions, the resolution of which would drive an evidence-based decision. As might be expected, dataset quality issues were a significant problem, spanning accessibility, consistency and relevance.

The importance of subject matter experts (SMEs) is emphasised, as they are critical in ensuring the success of analytics projects. This underlines the fact that these techniques might improve efficiency, and potentially uncover interesting new results, but they are not a complete replacement for discipline experts.

Linn Energy

Linn Energy is one of the few examples of small companies investing in digital oilfield, specifically around reservoir surveillance. (Eldred, et al., 2015). The company built a platform for data integration, surveillance and optimisation, primarily to support inter-disciplinary teams with access to a common set of data.

While it may have been inspired by the digital oilfield initiative, it is hard not to see this as an individual company seeking to use automation and data integration to improve its current processes.

Integrated asset modelling

There is substantial debate over the nature of integrated asset modelling, without considering what benefits it can bring.

Wadsley argues that there is an optimum level of detail that balances accuracy of forecasts versus speed of execution (Wadsley, 2015). His goal is a 'factory' model, in which the models for each stage in the hydrocarbon lifecycle are coupled together.

On the other hand, some operators have particular problems, like hydrate formation and chemical inhibitor tracking, that impose a specific set of constraints (Al-Jasmi, et al., 2015). In this context, PVT calculations are key to calibrating the well models. For Qatargas, what is important is a 'robust flow modelling system, supported by physical laws that can offer all information relevant to well and reservoir performance, accurate to the highest degree' (Bian & Abuagela, 2015).

Overall, it is not obvious that there is a single approach that is appropriate for all assets, and at best we might be looking for a small set of solutions, at worst for a unique implementation for each asset.


There is very little explicit reference to data standards in the literature on digital oilfield, beyond passing references to PPDM and OPC (Akoum & Mahjoub, 2013).

This is possibly because the bulk of the reported implementations are specific to, and developed by, individual companies.

There are industry bodies, like Energistics and others, attempting to build standards, create awareness and drive adoption (Hollingsworth, 2015). This includes PRODML, developed with the goal of establishing an industry standard for data exchange to support production processes (Ormerod, et al., 2013).

The authors suggest that the lack of data exchange standards causes increased complexity and reduces the ability to generate automated workflows.

However, as the paper identifies, the standard is still underused. While there might be significant benefits to the use of standards, they seem peripheral to current and historic digital oilfield initiatives. It will be key in future to ensure adoption as a priority.

In contrast, there is the suggestion that it might be necessary to eschew the idea of a standard deployed everywhere, and for speed and adoption purposes, allow local innovation and diversity. (Gilman & Nordtvedt, 2014)


In a discussion of approaches for ontological frameworks applied to the information interchange between production applications, one paper (Saputelli , et al., 2013) highlights the Integrated Operation for the High North (IOHN) project, developed by the Norway Scientific Council and Statoil in association with several services companies.

Other projects identified as interesting in this context include Shell's Smart Fields initiative, the Field of the Future from BP, the i-Field from Chevron, and Saudi Aramco's Intelligent Field Program, among others. It is striking that the list does not include any smaller operators, and that many of the actual implementations are regarded as company proprietary.

Cloud computing

Cloud computing is attractive as a way to reduce costs, and increase deployment flexibility and speed of implementation. Very broadly, the oil and gas industry makes two arguments against cloud: security and data set sizes.

Both of these concerns can be mitigated, not least by envisaging a near future in which a hybrid model is enacted with end-to-end data encryption and secure storage of keys.

Unfortunately, as Perrons & Hems (2013) point out, software vendors in the industry have been slow to move to the cloud computing model of 'apps' and interoperability. Rather, the trend has been to move existing monolithic applications to shared data centres with access via remote terminals, thereby eliminating many of the benefits of cloud and adding very little value for the customer.

Big data

It is still relatively early days for big data in oil and gas.

IBM have made a case for the use of big data techniques in areas such as closed-loop reservoir management and production optimisation, and integrated operations (Brulé, 2013). They argue that it is more effective to consider data-in-motion combined with data-at-rest.

Thus, they recommend combining stream computing for analysing high-frequency data, such as sensor data, with large volumes of structured or unstructured data in Hadoop or massively parallel processing relational data warehouses (MPP DW).

While the technology described is undoubtedly interesting, it is necessary to see where it can add value. The work at Devon Energy (Turk, et al., 2013) seems entirely relevant and illuminating, as without good questions we cannot have good answers.

Internet of things

One of the most interesting ideas has been to use very low cost, disposable devices that can record (or capture) data and transmit it to the cloud. No control function would be included, so the security issues are minimal, but it might allow enhanced monitoring of a facility without expensive infrastructure. As with so many other technologies, this has the feel of a solution looking for a problem.

Innovation networks

A recent paper on innovation networks explains some of the challenges that digital oilfield has encountered (Lyytinen, et al., 2015), identifying four types of innovation networks.

1) A Project innovation network which takes place in the context of a centralised and hierarchical control.
2) A clan innovation network, which involves relatively homogenous participants who share a single discipline or closely related disciplines.
3) A federated innovation network, having members who operate under centralized control, often within a single organizational hierarchy (or within an alliance).
4) An anarchic innovation network, in which control and decision-making is distributed through the network, and the knowledge resources are highly heterogeneous.

The last of these is the most complex, yet it best represents the way in which the cross-industry development of digital oilfield has been carried out. There are a wide range of participants who often have different and possibly conflicting interests with highly distinct knowledge bases. No one has control over the final product architecture, the digital infrastructure that supports the innovation, or the rules of engagement.

It is unsurprising that the most success has been in a single company, such as BP, which generally resembles the project innovation network.

We argue that the recognition of these network models, and the existence of tools and mechanisms to address them, might enlighten future work in the area.

A personal perspective

Some personal opinions:

If digital oilfield is not to simply be an expensive toy for the few, it must be accessible and relatively inexpensive to acquire.

It is perhaps an oxymoron to advocate digital oilfield as a solution to the skills shortage if it requires a phalanx of PhDs to support its implementation.

A dramatic increase in adoption of cloud computing seems inevitable. The mistake made by many companies is to ask what they can do internally, not what they should do.

A significant part of the IT budget is regarded as commodity purchase, not an investment in competitive differentiation, and as such the move to cloud is in perfect alignment with the industry direction.

The cloud will also be the place where innovation happens. The current major players in oil and gas software think of innovation as the opportunity for third-parties to write add-ins for their platforms: the reality is that innovation will come from a wide range of completely new apps delivered in the cloud.

Big data will remain, like object databases before it, a niche interest without widespread adoption. For many, the definition of big data remains opaque, and the only one of the three Vs (velocity, variety, and volume) that is dominant in peoples' minds is volume. Concerns about industry adoption have already been raised (Perrons & Jensen, 2014).

The Internet of Things (IoT) will also likely remain a niche application, at least for the foreseeable future. While many are hard at work on the development of standards (OMG, 2016), past experience suggests that it will be challenging to get vendor buy-in in the face of likely customer apathy. A relatively simple problem, like home automation, is seeing slow progress despite the dominance of Apple and its efforts in this space.

Digital oilfield will fade as a distinct concept. While a number of companies have benefitted from its use as a driver for change, and vendors have created new solutions to address perceived needs in this space, it is questionable whether it continues to have value as an independent discipline.

Just as banks have been forced to recognise that, at their core, they are digital businesses, so will oil and gas.

However, unlike the efforts of the last ten years, which have seen major companies developing their own solutions, the broad industry will look to the market to supply the tools they need, just as other industries do. Innovation within oil and gas companies will be focussed on those areas that can drive competitive advantage.

Finally, the future really should be one in which individual users can choose a range of applications, and bind them easily into workflows to meet their needs, without requiring programming skills and without interaction with any of the individual vendors.

If you want to see what that looks like, then have a look at Zapier (

Note: this article is an edited version of a longer paper. The full paper is online here

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