The energy sector is at the center of the climate challenge. Efforts to cut down emissions are important, particularly, by asset-heavy industries like oil and gas and electricity with larg-scale operations. The Paris Agreement calls for these industries to achieve net-zero emissions by 2050 to limit the impact of climate change. These industries must ensure critical equipments perform optimally and report scope 1 and 2 emissions.
However, energy security has become primary for nations and the energy sector. Countries in Europe are prioritising their immediate energy needs by leveraging traditional sources like oil and gas to ensure a stable supply. At the same time, energy transition must continue.
The use of AI can be game-changing in stabilizing energy supply and for better energy management from production to distribution to consumption.
Roadblocks to decarbonization
Investments in renewable energy, a key enabler of decarbonization, have come down in some regions in recent years, due to a shift in business priorities. Instead of long-term investments in renewables, which often require significant upfront capital, there is more focus on low-carbon molecule technologies such as carbon capture and storage (CCS) and biofuels. Since 2022, investments in renewable fuels, CCS, hydrogen, advanced materials, and digital technologies have significantly surpassed renewable energy uptake.
Energy efficiency, also vital for decarbonization, requires more attention than ever as the energy sector’s investment focus shifts from traditional renewables to alternative technologies.
AI’s role in decarbonization
AI-based solutions can be pivotal across the energy value chain, streamlining processes to support less energy wastage and more sustainable operations where efficiency and profitability are maximized. It helps decarbonize buildings and operations by improving occupational safety and quality control standards, and proactive asset monitoring and management.
AI helps monitor equipment and processes in real-time, identifying potential issues and solutions which means improved efficiency and lesser downtime. It sharpens decisions by using data and advanced analysis to choose the best course of action in every activity – from where to drill for oil to better ways to manage energy supplies. This helps minimise non-productive time in an expensive activity like drilling. AI’s ability to analyze large amounts of data enables speed and depth of insights that humans might miss. It can analyze geological data to help decide the best places to drill for oil and reduce the chances of drilling in unproductive areas.
For instance, AI can reduce large cycles of exploration involved before identifying the production potential by predicting quantities of oil or gas in a reservoir and the most efficient ways to extract it.
Companies are also exploring AI-led automated well control and drilling optimization. Drilling teams will benefit from comprehensive operational insights, anticipating potential challenges, and optimizing the drilling engineers’ preparedness and efficiency. Morning drilling reports are used with well completion reports to optimize workflow planning, minimize operational delays and drilling errors through the day.
Emissions reporting is a non-negotiable aspect of sustainability that AI can simplify. Tracking greenhouse gas emissions is essential for meeting regulations and initiating steps to reduce environmental impact. AI and automation improve this by making data collection and analysis faster, more accurate, and less labour-intensive. By continuously monitoring emissions from a source, delivering real-time data and instant alerts if safe limits are breached, it allows organizations to respond swiftly. For example, during oil extraction, AI can be used to monitor flaring and minimize spills.
Deep predictive intelligence capabilities in analyzing past trends and current conditions can be used to anticipate, model and plan for energy supply, demand, and potential disruptions. Energy management platforms with AI capabilities can be established to match variable supply with fluctuating demand, thereby, maximizing the financial value of renewable sources. These can adjust energy production and distribution by dynamically switching between renewable and traditional sources, enhancing reliability of energy supply.
Leading the energy transition being AI-first
Using AI has many upsides. However, enterprise-wide AI adoption for critical decision-making has been restrained because of data security and privacy concerns – as these operations involve highly sensitive information. While these concerns must be alleviated through responsible AI practices, the technology’s potential in enabling energy efficiency must not be ignored.
As AI adoption increases, green data centres will be necessary to limit growth in energy demand.
Industries, in general, will benefit from platforms that harness AI and advanced analytics to support sustainability efforts. Data from multiple sources is consolidated, and demand and supply facets of the sector integrated into a unified platform. This enables seamless monitoring, management, and decision-making to lower energy use, costs, and carbon emissions.
Achieving net zero is a necessity to mitigate the severe impact of climate change. Towards decarbonization in the energy sector, technology such as AI can make a significant, positive difference in maintaining their progress.
