The energy industry followed a placid approach towards digital transformation and has been characterized by its complex workflows. The benefits of cloud adoption in other industries have set the energy leaders seeking innovation in their quintessential challenges of ensuring energy security, providing affordable energy with cleaner solutions. AWS stands out as a disruptive player in the energy industry leveraging the power of cutting edge Cloud and AI solutions.
The Role of AWS Cloud in Energy Industry
The energy industry has become more data driven in recent years to keep a track of output, surge in demand, and solve other operational challenges.
AWS with the largest share of cloud infrastructure is helping energy enterprises streamline their operations, enhance efficiency and drive innovation. As the energy companies became more data driven this led to the increase of on-premise data centers in the initial days which incidentally gives rise to its own carbon footprint. With rising demand for data, companies soon started to realize the importance of data democratization for a wider scope of analysis. AWS cloud fosters an environment where data is synchronized and can be accessed globally by authorized parties.
Reduced Carbon Footprint
It is interesting to note that a study by 451 Research found AWS reduces the carbon footprint by 88% compared to on-premise data centers and is far more efficient in handling the workloads. The above figure is soon to improve upto 95% when AWS becomes powered by 100% renewable energy in 2025. The cloud also stands beneficial for energy companies used to spending huge amounts towards maintenance of on-premise data centers and infrastructure and can now pay for only the amount of time computing resources were consumed with zero worries on maintenance.
Enterprise IT Modernization
Power and utility firms aspire to transition to the cloud seamlessly, integrating critical enterprise IT applications. These applications encompass business process management, customer utility billing, geospatial information systems, enterprise asset management systems, and enterprise data historians. Rather than opting for singular solutions addressing specific needs, tech leaders aim to establish a comprehensive cloud-based architecture that manages data holistically throughout the organization. This architecture not only paves the way for solutions like data ingestion, data lakes, and analytics frameworks but also ensures a cohesive and integrated IT landscape.
The Potential of Generative AI in Transforming Energy Industry Operations
Generative AI, or GenAI, is an integral part of artificial intelligence that feeds from existing data to create original content. AWS has been pioneering the integration of GenAI into the energy sector aiming to elevate operational efficiencies, mitigate health and safety risks, improve the customer experience, reduce emissions linked to energy production, and expedite the global transition to sustainable energy practices.
Optimizing Smart Grids
AWS’s GenAI capabilities are pivotal in optimizing smart grids, ensuring efficient energy distribution and minimizing wastage. Through machine learning algorithms, GenAI analyzes real-time data to predict and prevent grid failures, enhancing reliability and reducing downtime.
Predictive Maintenance
GenAI transforms predictive maintenance in the energy industry.
AWS employs GenAI to analyze equipment data, identifying patterns and predicting potential failures, extending asset lifespan and minimizing operational costs.
Energy Demand Forecasting
AWS’s GenAI excels in predicting energy demand patterns, optimizing production and distribution. By analyzing historical data and relevant factors, GenAI helps energy providers make informed decisions, reducing waste and environmental impact.
Carbon Emission Reduction
GenAI plays a crucial role in reducing carbon emissions, aligning with sustainability goals. AWS leverages GenAI to optimize energy consumption, minimize waste, and integrate renewable energy sources into existing infrastructure.
Exploration and Production Optimization
In the oil and gas sector, GenAI is utilized for reservoir modeling, predicting drilling outcomes, and optimizing exploration and production. Utilizing generative AI techniques like General Adversarial Networks (GANs), it’s possible to create subsurface models. The GAN’s generator network can be trained to generate synthetic models resembling real-world subsurface reservoirs, while the discriminator network distinguishes between real and synthetic models. Once trained, the generative model can produce numerous synthetic reservoir models for applications like reservoir simulation, optimization, and enhanced hydrocarbon production forecasting. Additionally, these models find utility in other energy domains like geothermal and carbon capture and storage, enhancing subsurface comprehension in critical areas.
Real Applications of AWS Solutions in Energy Sector
Grid Modernization
Duke energy has a grid of 314,000 miles of transmission and distribution lines. It is important for Duke energy to effectively anticipate the electricity demand and modernize the grid accordingly. Leveraging AWS, Duke Energy’s Intelligent Grid Services enhance electricity forecasting accuracy, enabling informed decisions on equipment replacement and non-wire alternatives. These smart-grid solutions optimize upgrading electric distribution circuits for new developments or adding protective equipment to prevent failures, offering a cost-efficient approach. The transition to AWS accelerates the simulation process, enabling Duke Energy to run hundreds of millions of grid simulations in 15 minutes, instead of the estimated six weeks required using on-premises technology. This cloud strategy also streamlines IT infrastructure, minimizing procurement and maintenance efforts.
Simulations for Designing Gas Turbines
Baker Hughes, a global leader in industrial turbomachinery and innovation, encountered scalability challenges with its on-premises high-performance computing (HPC) solution for simulating gas turbine designs. The solution faced resource capacity bottlenecks, leading to prolonged simulation wait times and increased reliance on physical prototypes. In collaboration with AWS, Baker Hughes developed the Leucipa automated field production solution, harnessing AWS’s advanced analytics and Baker Hughes’s domain expertise. This solution empowers operators to autonomously and proactively manage field production and energy efficiency, maximizing value from producing wells, minimizing operational downtime, and optimizing overall well performance. Simulations were run using Amazon Elastic Compute Cloud (Amazon EC2) and the resultant data was securely stored using Amazon Simple Storage Service (Amazon S3). The engineers uses a fully managed desktop virtualization service called Amazon WorkSpaces that can be accessed from any location across the globe.
Conclusion
The energy industry finds itself at a pivotal juncture, embracing the transformative power of digitalization. As this evolution unfolds, a vast array of innovative solutions beckons from the combined offerings of AWS and its extensive partner ecosystem. These solutions address pressing challenges and unlock exciting possibilities, ranging from drastically reducing carbon footprints to streamlining complex workflows into agile, data-driven processes. By harnessing the combined expertise of AWS and its partners, the energy sector can embark on a transformative journey towards a more sustainable, efficient, and resilient future. The vast array of innovative solutions available ensures that the possibilities are not merely endless, but within reach, empowering the industry to navigate the digital transformation landscape with confidence and purpose.
