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Applying blockchain in the geoenergy domain: The road to interoperability and standards

Geoenergy sources will continue to be mainstays of the world’s energy mix for many years to come, but the technological and business realities behind these energy sources are changing in two fundamental ways. First, with much of the world’s “easy oil” already consumed, the companies behind geoenergy will have to use increasingly sophisticated technologies to find and deliver these energy sources to the market. Second, the expectations placed upon the geoenergy sector by many of its stakeholders have grown considerably with regards to environmental stewardship, safety, and human welfare. In the face of these kinds of challenges, the industry will require an increasing degree of technological and commercial sophistication to continue to be a part of the world’s sustainable energy mix. Blockchain has emerged as a promising innovation that could potentially play an important role in delivering the kinds of technological and commercial capabilities that the geoenergy sector will need to achieve these ends. In spite of the myriad ways that blockchain could potentially improve the efficiency and sustainability of the geoenergy industry, however, the technology is still evolving, and a few barriers stand in the way of its widespread deployment. This paper puts forward case study evidence from the Intel Corporation and the Energistics Consortium showing what the geoenergy sector can learn about blockchain from other industries, and highlights that the absence of data standards and interoperability has contributed to blockchain’s failure to deliver significant value in the geoenergy domain thus far.
- Intel (United States) United States
- University of Cambridge United Kingdom
- Intel (United States) United States
- Queensland University of Technology Australia
Energy, Case study, Intel, Blockchain, Energistics, Geoenergy
Energy, Case study, Intel, Blockchain, Energistics, Geoenergy
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).37 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
