How much value has digital transformation created so far in the Oil & Gas industry?
Which technology has contributed the most and what are the most compelling use cases? What are the digital value drivers and how to accelerate the transformation?
These questions were debated at the O&G IIoT and Digital Conference which took place in Amsterdam in June. In this series of two blogs, I will take a closer look at the key points raised during the conference’s presentations and debates to shed light on the three key trends in today´s digital landscape that represent the opportunities and challenges for Oil & Gas players:
- IT players and platform focus on the IT/OT convergence market with disruptive value propositions for operations management, and push back traditional market boundaries
- The almost infinite power of Big Data, algorithms, networks, and cloud empowers OT providers to increase the value of their solutions in the fields of industrial software and robotics
- Domain expertise is the cornerstone of digital transformation success. Smart change knowledge management initiatives must be taken to extract expertise and accelerate digital transformation
The challenge for Oil & Gas companies is how their organizations can leverage these three trends to create more value along their transformation journey. This digital transformation relies on trustworthiness between the IT and OT worlds. This trust would be earned the day operators reap significant business benefits out from their digital journey.
Digital technology acceleration is blurring the IT and OT market boundaries
IT, software, and GAFAs (Google Appel Facebook Amazon) have, by nature, high R&D ratios and marketing costs. Their technologies promise great business value in the operational landscape. At the conference, these major players presented a number of business cases and initiatives which illustrate the IT/OT convergence in progress:
- Intel presented an IoT box which they developed in partnership with an OEM provider and a field operator to bring connectivity to the field and increase production while reducing operations and maintenance costs. Their strategy is not only to become a solution provider but also to develop an open source control system that will enable higher operational flexibility by addressing the issue of heterogeneous hardware installed bases in the field
- Microsoft builds the value proposition of its IoT suite for machine learning applied to predictive maintenance. This capability is available as a service and does not require IT infrastructure investment, while algorithms provide business insights and notifications through the analysis of historical data series
- Google entered Oil & Gas exploration with Total to jointly develop artificial intelligence solutions applied to subsurface data analytics 
Intel’s move down the automation system pyramid looks like a common solutions strategy, but the investments made by the IT, software, and platform companies that can afford to leverage vast resources to facilitate the future adoption of digital deserve a lot of attention.
OT providers must place data and system openness at the heart if their digital strategy. We’ve seen how some IoT suppliers have failed when, among other things, they didn’t open their analytics platforms for predictive maintenance.
Data is the blood of the digital system and it needs free, open pathways. Not surprisingly, technology players strongly advocate for this openness but the pathway destination is less clear. Microsoft, TNO, and others companies acknowledge that some data-driven artificial intelligence models still faces ‘explainability’ gaps that act as barriers to the OT field, but for how long?
 Condition required to exchange data across ecosystems – reported by the IIC consortium
 Intel is engaging in a Universal Well Controllers programs to facilitate future adoption of IoT.
 Google is also partnering with Repsol to provide cloud and AI capabilities for refining efficiency.
 Operators are not willing to let OEMs or IT players take the benefits of their operational data.