Oil and Gas

Accelerating Technology and Domain Expertise are the Recipe for IT/OT Convergence in Oil and Gas

As I discussed in my previous blog,  How much value digital transformation has created so far in the Oil & Gas Industry, a debate is currently happening around which IIoT technology is contributing most, what are the most compelling use cases, and what are the digital value drivers accelerating the transformation.

IT/OT convergence is one of the three trends shaping the digital landscape. The almost infinite power of Big Data, algorithms, networks, and cloud – together with domain expertise as the central pillar – complete the picture.

The disruptive effect of the digital acceleration is yet to come, but the continuous improvement of cloud capabilities enhances the value of industrial software solutions

Digital technologies have been around for decades, creating tremendous value since the beginning of automation in the 1970s and the software industry in the 1980s. Today, digital is now at a tipping point through the combined effects of a logarithmic reduction in IT costs and the convergence of core digital technologies becoming mainstream. This should have a disruptive impact on the industry[1].

The promise of the digital twin to optimize the total cost of ownership over an asset’s entire life cycle (from design, construction, and operations, based on a continuous data flow and consistent decision making) is a disruptive value proposition for the industry. Kongsberg and Equinor presented their digital twin initiatives which rely on open platform capabilities and an open ecosystem environment. It is still too early to measure the benefits on such an investment, though, and while the question was raised during the conference, it got no direct answer.

However, for industrial software business applications, the benefits of advances in network connectivity, cloud computing, and machine learning are already proven. They are even amplified when solutions are delivered through platforms and an ‘as-a-service’ business model.

For instance, Schneider Electric-AVEVA reported use cases where predictive maintenance solutions, as well as trading, planning, and scheduling software, delivered millions of dollars in savings to Oil & Gas customers. The cloud is instrumental in those successes: its almost infinite computing power allows real-time simulation that EPCs and operators can leverage to de-risk capital projects and capture value creation opportunities along the full operations life cycle.

This is an example of how industrial software technology can create real benefits by combining unprecedent visualization and simulation capabilities and make them available to engineers and operators. It enables an effective step wise approach to real digital transformation.

Beyond technology, domain expertise is vital to the success of digital transformation strategies in Oil & Gas

First and foremost, in the digital transformation landscape – and in business, in general – technology on its own can’t deliver progress without deep domain knowledge:

  • Only experts can ask the right questions and identify the right problems to solve. As Shell and many other representatives reported, this could explain why POCs often fail to demonstrate value creation. Experimenting with POC is good but not sufficient: framing the problems to solve while focusing on user experience can be the key to success in experimentation.
  • In robotics, artificial intelligence is used to develop a concept of supervised autonomy of remote assets. Houston Mechatronics embeds machine intelligence into its subsea robot[2] and experts provide the supervision required for safe, reliable, and efficient autonomous operations of the assets.

Secondly, the aging workforce of Oil & Gas operators face the challenge of transferring their wealth of knowledge and experience to ensure sustainable operations and growth:

  • The industry can’t operate safely and efficiently without domain expertise in its operational technologies and processes. Artificial intelligence solutions like Maana’s Natural Language Technology extract concepts from technical reports in a simple user interface; Schneider Electric’s wise data management approach transforms data into knowledge while freeing up time for experts to focus on their jobs and resolve problems. Shell called this the ‘secret sauce’ for digital transformation success: ‘changing the way of working.’
  • Experts are needed to make sense of and, even more importantly, to provide trustworthiness to data analytics. Unsupervised machine learning faces the ‘explainability’ gap that still keeps this technology confined more to the realm of experimentation, i.e. seeking problems to solve. However, machine learning applied to asset predictive maintenance, supported by experts, brings tangible benefits, i.e. creating value by making the right decisions to avoid added costs following efficient business processes.

Digital transformation is certainly a multi-faceted challenge for the Oil & Gas industry. Technology acceleration and IT/OT convergence create new opportunities for value. So far, IT and software players have entered the OT space while OT players are leveraging connectivity, big data, cloud, and machine learning capabilities to increase the added value of their industrial software offers.

Trustworthiness in data life cycle management, from quality to business insight, combined with Oil & Gas domain expertise are mandatory. This calls for deeper collaboration between the two worlds to the advantage of operators. At the O&G IIoT and Digital Conference in Amsterdam, technology providers clearly called on the industry to open data so that the digital transformation promise could be fully realized.

[1] ACCENTURE illustrated a ‘disruption’ by presenting two photos of Fifth Avenue in NYC, one taken in 1890 and the other in 1905. A magnifying glass was needed to see a single car in the first picture and a single horse in the second picture!

[2] The Aquanaut is the next ROV generation; it embeds analytics to cope with bandwidth constraints and operational risks related to the environment.


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