This paper addresses two kinds of sustainability: first, how data is used in conjunction with organizational sustainability goals, and second, how data itself is made sustainable, through data standards, and more.
Do you know what value lies beneath your (sub)surface data lake? Join this talk to hear the reasons to liberate your data from silos, disparate systems, proprietary formats -- and into a modern energy data platform that can unlock millions in savings, efficiencies, and new insights.
The global demand for Artificial Intelligence (AI) based solutions is projected to grow from about $50B in 2020 to $350B+ in 2028. And yet, at numerous technical meetings of AAPG, SEG, EAGE, and related societies, the companies with AI ready products are few, and the operators buying off-the-shelf AI-related products are even fewer. So, are we stuck in a hype cycle, or is something else going on? We see some major factors: 1) Many operating (and service) companies have spun up groups of data scientists who are delivering bespoke AI solutions internally, but data labeling still requires scientific-domain experts, making these efforts not scalable. 2) AI solutions are not yet available as ready, off-the-shelf products complete with the domain-specific user-interfaces in order for companies to be able to easily deploy the solutions, thus complicating implementation and penetration, and 3) company leadership have not yet figured out how to effectively leverage AI/ML in a value-adding sense. These factors all point to a market in a nascent state. Both the operators and the product providers have not yet figured out how to deliver and deploy optimal solutions. We will discuss these observations, and what they mean for the evolution of AI in the Energy industry over the next few years.
There were significant improvements to the Geothermal map from 1992 to 2004, with an exponential step change from 2004 to 2011, leading to the “discovery” of the West Virginia thermal anomaly. This discovery was made possible by recording existing bottom hole temperature data in a different way, one more meaningful for geothermal exploration. Similar work was performed to compile the National Geothermal Data System (NGDS) for geothermal exploration, completed in 2014. More recent work shows this same reexamination of old data suggests greater geothermal resource potential in Texas and in many parts of the United States, although the full strength of the NGDS has yet to be used because the data are not in a single common format, and therefore, the NGDS has not been analyzed at a national scale for new insight. This example shows:
1) Companies can utilize existing data (theirs and public data) to search for green energy opportunities and 2) consistent data standards could facilitate and expedite new geothermal discoveries.
2) Consistent data standards could facilitate and expedite new geothermal discoveries.
Edwards Energy Innovation Consulting