AI will shape the energy transition
Artificial intelligence will have a wide-reaching impact on the entire energy sector and determine the speed and direction of the transition, according to Hypergiant founder and CEO Ben Lamm
Ben Lamm is the CEO and founder of US-based advanced technology solutions provider Hypergiant. The Texan serial entrepreneur—it is his fifth company—embarked on his most ambitious enterprise to-date when he co-founded Hypergiant in 2018.
Hypergiant is focused on advanced artificial intelligence (AI) for clients in a wide range of range of sectors from oil drilling and fluid dynamics to entertainment and healthcare. It has an impressive roster of industry partners: consultancies Booz Allen Hamilton and EY; applied science company Dynetics; software companies Adobe, Microsoft, AWS and SAP; and computer hardware company Nvidia.
Likewise, its clients include leaders in diverse areas of the oil and gas sector including Shell, US E&P independent Marathon Oil, oilfield services company Schlumberger, conglomerate GE and marketing and trading firm Pacific Summit Energy.
How can AI aid the decarbonisation of oil and gas production?
Lamm: For decades, oil and gas producers have faced pressure to improve their carbon footprint. Technology in general has proven to be one of the most effective levers for them to pull, with AI in particular now surfacing as the newest and hottest lever of them all. Given that the production phase of the industry is responsible for less than 15pc of the emissions released, the bulk of the problem lies downstream of the producer functions which include exploration, drilling, and processing/refining. However, for these four discrete categories, the following examples demonstrate how AI is becoming one of the more widely accepted approaches for addressing decarbonisation.
“The result [of AI] is the more efficient distribution of power at lower cost and risk to both providers and consumers. Literally everyone wins”
In exploration, improvements in finding the oil or gas can be made through the application of AI methods so that the geothermal intelligence and other exploration data can be used to much more effectively predict where additional reserves are located.
In the case of production, methane leakage can be detected and reduced and/or eliminated with both the use of optical gas imaging cameras at the valves/fittings and drone-mounted leak detection sensors throughout the sites. Additionally, routine methane flaring can be dramatically reduced via more effective production forecasting due to more accurate machine-learning infused data. Lastly, the analysis and application of predictive weather models help achieve greatly enhanced disaster response.
The processing/refining phase is definitely one of most carbon-intensive parts of the process and the area in which detection devices continue to get smarter and smarter. Carbon leaks can be detected, predicted, and ultimately reduced/eliminated via drone-mounted leak detection sensors and fence-line scanners.
Once detected, the data can be analysed automatically with AI resulting in predictive maintenance plans so that leaks are less likely to happen. The faster these planned and unplanned leaks can be identified and then acted upon is reliant upon smarter and more real-time data—and that is where AI is highly impactful.
Energy systems are becoming more distributed and including more sources of power. How can AI improve the performance of smart grids?
Lamm: This is an important question because it affects us all, from our homes and into our businesses, which in many cases right now are one and the same. Due to the number of variables to consider and challenges associated with upgrading and evolving an entrenched, legacy system of power distribution, AI must be leveraged in order to do so most efficiently, effectively and responsibly.
“There is simply no way to achieve these types of results with traditional technologies or humans alone”
The truth is, AI can help improve the performance of smart grids in the same way it can improve the conditions within any other use case where it can be applied. By consuming and processing vast amounts of data and accounting for fluctuating variables and scenarios, intelligent technologies identify patterns and produce insights that help humans make better decisions towards achieving a desired goal.
In the case of power distribution, AI will help providers safely deliver clean and reliable power to consumers from dynamic sources while giving those consumers tools to more effectively manage their own power consumption. What was once a fragmented patchwork of disjointed systems fraught with uncertainty and risk will become a more efficient and robust ecosystem infinitely more capable of managing and responding to the challenges of our modern world.
Moreover, the technologies that enable these results are designed to learn and improve over time. The result is the more efficient distribution of power at lower cost and risk to both providers and consumers. Literally everyone wins. There is simply no way to achieve these types of results with traditional technologies or humans alone.
Many see hydrogen as the fuel of the future. What part can AI play in its adoption?
Lamm: Blue hydrogen—the capturing and storing of the emitted methane—and green hydrogen—the utilisation of renewable power generation to achieve the electrolysis of water—are definitely receiving lots of attention from policymakers in the US and beyond.
The transition, however, is neither an easy nor a quick one. While it is the ultimate endgame, the infrastructure overhaul required dictates a long runway of at least 30-40 years. Therefore, every aspect of the business from wellhead to end-user will continue to leverage AI to optimise profitability while continuing to rotate their portfolio away from fossil fuels.
The suggested first step is to start at the most corporately strategic level and establish a three-to-five year AI roadmap that will accomplish the ultimate corporate mission of a drastically reduced—very close to zero—carbon footprint. This roadmap ideally lays out a rigorous plan that begins, and never ends, with the necessary pre-AI step of aggregating, cleansing/scrubbing and organising the data, via clustering and other AI methods, so that machine-learning can be applied and predictive analytics can be much more highly utilised.
Oil and gas prices are at low levels and are likely to remain so. What can AI do to alleviate pressure on profits?
Lamm: Most producers and integrated oil and gas companies have already adjusted their budgets for 2020 and 2021 to reflect $30-40 oil and made the subsequent capital and operating budget reductions. Therefore, now is the perfect time for companies to focus internally on streamlining and applying AI across their operational data in order to reduce process inefficiencies, maximise talent, predict maintenance anomalies, comply with current and planned regulatory reporting requirements and implement systems that will support the reduction of the corporation’s carbon footprint.
Energy demand is also subdued and many fields are running under capacity. Maximising output is therefore not a priority. How can AI help in the current market situation?
Lamm: AI has the ability to maximise business returns for energy providers. By dynamically adjusting generation, transmission and delivery, AI is able to utilise real-time telemetry that is matched against prevailing and forecast consumption patterns to establish optimal power delivery across existing expensive assets.
“Over the next decade, we will have a number of advancements that will lead to technology-first energy sector companies”
The portfolio of machine-learning models is employed to assess generation from load criteria, regulatory profiles such as renewable usage, and the current state of (condition-based monitoring) asset availability and health. This ensures maximum return on investment (ROI) even when consumption is muted.
With respect to transmission, similar AI-enabled capabilities can be utilised with the inclusion of real-time control of asset protection platforms to ensure asset efficiency, health and maintainability.
In terms of delivery, AI is employed to evaluate changing consumption patterns, loads, geographies and profiles to evaluate and forecast the ever-changing energy delivery portfolio to reinsure a reliable and consistent supply that is revenue optimised for ever-changing economic influences.
The oil and gas industry has lagged behind some others in its adoption of technology. Why do you think this is the case?
Lamm: There are certainly a few exceptions, but it is true that the industry as a whole has lagged in the adoption of technology. I believe this is due to a number of contributing factors, some at the organisational level and others at the individual employee level.
Firstly, an ‘it has always been done this way’ and ‘it is good enough’ mentality results in a failure or resistance to evolve.
Another factor is education. Many leaders do not fully understand the many ways technology can help their organisation. This is particularly true of AI. Without a clear understanding of what, why and ROI, they may simply decide to invest their time and money in other areas.
Fear and uncertainty also has a role. Hollywood has taught many people to fear AI, conditioning them to believe that AI is coming to take their jobs and lives away. Fear causes people to resist change, and this resistance can make it difficult for organisations to advance digital initiatives. Employees’ fears can prevent them from seeing the real purpose of AI, which is to help them do their jobs and live their lives better.
There can be self-perpetuating cycles. Humans get so caught up and busy with tasks that are better suited for technology that they do not have the time or energy to explore, invest and adopt the very technologies that can set them free to be uniquely human.
Finally, it is hard to adapt and can be really hard. Humans and organisations have a habit of finding ways to procrastinate and deprioritise challenging initiatives in favour of those requiring less effort, and those which promise nearer-term ROI.
Fortunately, many in the industry are acknowledging these issues and working hard to address them as part of their digital transformations, with Hypergiant being uniquely positioned to help them accelerate and advance their initiatives.
What areas of the energy system remain untouched by AI but could perhaps benefit from it?
Lamm: When looking at the energy system across the spectrum, there are many gaps where AI can add value where it is not being deployed. In many of those cases, however, solutions are being researched and/or developed either by internal teams or external vendours, such as Hypergiant, which are working towards providing customised solutions and universal answers to industrywide challenges.
But where we want to be careful is looking at the opportunity or value of AI as being limited to individual projects, regardless of how valuable they are. The real value, and what largely remains untouched within and beyond energy, is the enabling power of AI as an overarching horizontal thread, woven through the entire fabric of an organisation and tying it all together.
Think of it as the antithesis of, or remedy to, the tendency of departments/divisions within organisations to sequester themselves into their own silos, largely cut off from the visibility, resources and input from others. In order for an energy company to reach its full potential, its leaders must work to break down these walls and use intelligent technologies to see and understand their business and customers in ways their competitors cannot. Individual projects are important, but they must be tied to a larger strategy. Organisations that understand this will dominate the energy landscape through the 21st century.
Looking a decade ahead, what applications of AI could you imagine in the energy sector?
Lamm: To be honest, a decade is probably the amount of time that the industry needs to really begin to hit its groove, so to speak, with this technology. Over the next decade, we will have a number of advancements that will lead to technology-first energy sector companies. These companies will lean even further into clean energy and efficient energy practices to match the changing needs and desires of the world.