Energy Transition and the Exascale Era
HPC and AI solutions from HPE
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Business white paper
A fundamental transformation is underway in the energy industry—a transition from fossil fuels to clean and renewable sources of energy.
Responding to stakeholders and the public interest in environmentally sustainable practices, oil and gas companies are decarbonizing and exploring new economically viable renewable energy options; and utility companies and grid operators are adapting for a future with high amounts of variable but renewable energy, such as energy from wind, waves, and the sun.
Across the energy industry, organizations are investing heavily in physical and digital infrastructure to better generate, transform, store, and distribute energy. High-performance computing (HPC) and artificial intelligence (AI) are becoming ever more crucial for this digital transformation.
HPC and AI are also transforming. First, as HPC, AI, and Big Data converge, exascale-class systems enable faster insights to solve some of the critical problems in this new era of energy. Second, energy companies are increasingly augmenting their on-premises digital infrastructure with cloud and edge computing.
As the market leader in HPC with a large energy customer base and a pioneer in exascale computing, Hewlett Packard Enterprise is a reliable partner for energy companies throughout the value chain. 1 HPE offers an advanced portfolio of HPC and AI solutions for energy exploration transition, delivered on the edge, on-premises, hybrid, or as a service.
What is driving energy transition?
The energy industry is under severe pressure to balance stringent climate change goals with the rising demand for fossil fuels.
Population density and energy consumption are causing an unsustainable level of carbon emission and greenhouse gas (GHG) (Figure 1), which drive climate change.
Fortunately, there are several decarbonization trends (Figure 1)—peak oil, electrification, plummeting costs of renewables—driving their increasing use in power generation along with hydrogen.
Despite many existing decarbonization trends, CO2 emissions are projected to remain far above the 1.5°C pathway dictated by the Paris Climate Agreement. 2 So, all energy companies, especially oil and gas companies, are transforming themselves into carbon-neutral energy companies (Figure 2).
The growing use of HPC and AI in the energy exploration
For decades, oil and gas companies have relied on HPC to enhance decision-making in exploration and reduce investment risks.
As new hydrocarbon resources became harder to locate, exploration moved to difficult terrains such as in deep water, which needed higher dimensional and more accurate (hence more data intensive) surveys.
More recently, as oil fields are being highly instrumented (generating even more data), advanced analytics and AI techniques are being used to improve development/production operations, predictive maintenance of equipment, and more (Figure 3).
As they transition, these companies are increasing their current HPC and AI investments to handle even larger compute/storage requirements. They are also deploying new workloads in geosciences, computer-aided engineering (CAE), life sciences, computational chemistry, materials science, and more—the areas where HPE is already a leader.
As HPC, AI, and Big Data converge, HPE is also pioneering exascale computing so that energy companies can gain faster insight from the growing volumes of data from the new workloads.
Next-generation energy HPC and AI workloads
As a market leader in HPC for geosciences and many other industry verticals, HPE is working with the open-source community and commercial customers/partners on many initiatives to help oil and gas companies implement a diverse set of innovative energy transition workloads (Figure 4).
Geophysics: Oil and gas companies are increasing the use of HPC and AI for seismic processing, seismic interpretation, reservoir modeling, and visualization particularly as the size and complexity of 3D subsurface models grow. Additionally, HPC and AI are used to model the pumping and storage of CO2 into underground geological stable formations such as basalt, depleted oil/gas reservoirs, and saline aquifers.
CAE: Engineers can design and test ideas for new products, wind turbines, production plants, and more without having to physically build many expensive prototypes. CAE reduces corporate risk by finding issues early in the design cycle before they go to manufacturing. Other benefits include lower warranty costs and less potential litigation if product failure causes injury.
Life sciences: HPC- and AI-enabled next-generation genome sequencing (NGS) accelerates the identification of key traits and development of oilseeds, algae, and other crops to create low-cost, next-generation biofuels. HPC and AI are also used to model the development of new biodegradable bioplastics, which could replace environmentally harmful petroleum‑based plastics in the future.
Combustion/turbulence models: HPC is used to model the chemical reactions that boost hydrogen production, develop the hydrogen energy supply chain (including the shipping of liquefied hydrogen), design new containers for hydrogen shipping, and develop thermodynamic models that can help improve the designs of compressors. 6
Weather modeling: Weather, especially severe weather, affects both the demand for power and the ability of companies to generate and deliver it to consumers. The meteorologist is to renewable energy as the geophysicist is to the oil and gas industry. As we shift to renewable energy sources, such as wind and solar, high-resolution weather models are critical to allow producers to predict how much power will be generated and supplied to the grid. HPC systems are used to run numerical weather prediction models at a variety of scales—from very high-resolution day-ahead forecasts for energy production to multiday forecasts of severe weather that can impact operations.
Materials science: The convergence of automation, HPC, and AI can accelerate the rate of materials discovery. These advanced technologies can be used for materials discovery, development, and characterization as well as manufacturing and reliability science for renewable energy technologies, including photovoltaics, solar fuels, hydrogen production and storage, fuel cells, windows, batteries, thermoelectric, and optoelectronics/lighting.
Computational chemistry: Green chemistry HPC simulations help design chemical products and processes that minimize hazardous substances, foster the use of natural gas and biomass as fuel, and encourage the development of circular (sustainable) products. It also offers tools to efficiently screen and design cost-effective materials for CO2 separation and storage.
Nuclear engineering: HPC is used to understand the thermo-mechanical properties of structural and nuclear materials to ensure the safe operation of a nuclear facility. It is also used to model complex structures and thermal hydraulics and guarantee the structural integrity of components.
Advanced optimization: Predictive analytics and AI solutions bring significant stability, optimization, and efficiency to the energy grid. Analyzing massive amounts of historical and real-time data enables companies to effectively balance energy supply and demand, automate the synchronization of grid assets for maximum reliability, minimize energy consumption, and develop agile energy trading / pricing strategies. These HPC and AI solutions are even more crucial with the widespread deployment of renewable generation, smart-grid controls, energy storage, intelligent infrastructure, plug-in hybrids, and other emerging technologies such as the Internet of Things (IoT).
Even with the dramatic acceleration in investment and deployment, we are still a decade or two away from shifting to most of the energy coming from renewables. As a society, we need to decarbonize our energy systems way before that, and the underground CO2 storage will be one of the pillars enabling this decarbonization and the hydrogen economy. It requires the integration of geophysics, fluid dynamics, materials science, and complex multiphysics simulations. This complex simulation is one of the strategic areas of the Exascale Computing Project and will need significant advanced computational resources to be attacked.
The diversity of HPC and AI applications for energy transition workloads also requires a new approach to traditional HPC. Modern, next-generation systems will be required to handle the exascale performance demands and massive data throughput requirements. These new systems will be more heterogeneous with multiple processors, accelerators such as GPUs, a variety of interconnects, and other elements. In addition, cloud-like approaches will be required.
Growth of cloud and data-intensive computing
The delivery of HPC and AI is also changing. The energy industry is increasingly augmenting on-premises data centers with cloud computing to improve end-user experience, agility, and economics.
Consequently, the use of public cloud in geosciences is forecasted to grow at 22.4% CAGR until 2024. 7
Public clouds have provided a dramatic shift in flexibility and elasticity for the consumption of compute cycles. Methodologies such as containerized workloads are now also being deployed on on-prem systems facilitating the software portability between both public clouds and traditional on-prem data centers. While this flexibility is great, once workloads mature and move from development to production, the cost of running in a public cloud can skyrocket.
Another big problem for running data-intensive workflows is the repatriation of data. It is usually easy and inexpensive to upload data to the cloud provider and this is attractive when the data value is perceived to be low. However, as customers realize the value of their data and want to implement AI and analytics, they are often limited in their ability to repatriate their data because of egress charges.
HPE GreenLake offers customers economics of the public cloud with the security and performance benefits of on-premises IT. As such, it provides a good middle ground for customers who want a cloud-like infrastructure while maintaining control of their data and managing and scaling workloads in an agile and cost-effective way (pay only for use) but with the benefits of dedicated systems.
In addition, as energy workflows become more complex and data-intensive, the HPE HPC storage portfolio addresses the unique storage demands of AI as well as traditional all-flash enterprise file storage, and it is also scalable and cost-effective.
As they transition, energy companies will increasingly use a wider variety of HPC and AI workloads from different industry verticals. With about 40 years of experience in the industry, HPE is a proven leader across HPC verticals and offers customers expertise and solutions to advance their business using these workloads. To stay competitive, HPE customers can cost-effectively process complex data faster, lower risks, and improve decision-making by leveraging cloud and exascale computing.
Exascale solutions from HPE for energy transition
In the next couple of years, several leading national laboratories (Argonne, Oak Ridge, and Lawrence Livermore) that pioneer energy transition research will be deploying HPE Cray exascale supercomputers.
HPE is proactively partnering with energy companies in their transformation journey and is making these new exascale technologies available and affordable with a comprehensive HPC and AI solutions portfolio.
HPC and AI solutions portfolio from HPE: Unlike other vendors, HPE can offer a complete and integrated HPC solution (Figure 5) including compute, networking, storage, and software with a single point of contact for all support requirements through HPE Pointnext Services. These services are provided by HPE experts who can work closely with customers to help them on their energy transition journey and tailor the entire solution to their specific needs.
Energy companies can run HPC and AI workloads with HPE solutions (Figure 5) at the edge (where a lot of data is generated), in data centers, and in cloud environments (for better flexibility and economics).
HPE solutions range from single, small systems all the way to exascale-class supercomputers with tailored software, interconnect, and storage capabilities. Figure 6 details the key systems in the compute and storage portfolio starting with purpose-built supercomputers.
The HPE Cray supercomputing systems and the HPE Apollo family offer purpose-built HPC and AI platforms that can support a wide range of size, complexity, processor, and accelerator choices. HPC options include top-bin CPUs, fast memory, integrated accelerators (GPUs or coprocessors), and fast cluster fabrics and I/O interconnections, making it easy to achieve the right performance and price/performance for HPC workloads. For harsh edge environments such as oil rigs or smart meters / drills, HPE Edgeline systems provide enterprise-class compute, storage, networking, security, and systems management at the edge.
In addition to compute platforms, HPE also offers two outstanding HPC and AI parallel storage solutions. The Cray ClusterStor E1000 is purpose engineered to meet the demanding input/output requirements of supercomputers, and HPE Parallel File System Storage delivers a high-performance, cost-effective solution for HPC clusters. This portfolio also includes object storage; data management framework software that enables customers to manage, migrate, protect, and archive their data; and the best practices to make everything work together efficiently.
Finally, HPE offers a comprehensive portfolio of software solutions designed to help you get the best performance out of their systems.
HPE GreenLake for HPC is a market-leading IT-as-a-service offering for HPC and AI that offers easy and affordable access to dedicated, powerful supercomputing and analytics capabilities, so you can make faster decisions and reduce time to discovery. With HPE GreenLake for HPC, customers can choose an on-premises HPC solution with the flexibility, scalability, and utility-like consumption of the cloud. Customers can increase their agility with pay-per-use pricing and preinstalled buffer capacity that is ready to provision when needs grow. Whether demand for HPC resources spikes suddenly or grows steadily, customers are always ready to meet new needs.
Customers can design their own HPC infrastructure solution within HPE GreenLake using industry-leading HPE technologies. Alternatively, they can standardize their service with presized configurations that offer self-service and are managed for them.
HPE Pointnext Services offers a spectrum of services to meet HPC and AI requirements—from services such as application tuning to more integrated advisory service offerings such as project management, on-site consulting, technical account management, and solution architecture consulting.
HPE financial support helps customers purchase HPC systems and upgrade them frequently. HPE offers a wide range of flexible sourcing options for the full HPC and AI infrastructure stack ranging from classic purchase to financing and/or subscription.
In addition, the HPE Technology Refresh program allows clients to benefit from the latest technology available in the market, so they can stay competitive. HPE can also buy out and recycle existing infrastructure to help customers meet sustainability targets.
Last but not the least, HPE has a relentless focus on the critical challenges of our times such as climate change, security, inequality, and more. The 2020 Living Progress Report provides an overview of HPE’s approach, programs, and progress on the environmental, social, and governance issues most significant to its business.
HPE: Your HPC and AI partner for the new era
The pressure on oil and gas companies to respond to the threat of climate change is growing.
Yet, these companies must also satisfy the rising energy demand. So, they must adroitly balance their current energy exploration business, institute decarbonization initiatives, and transition to an energy company.
HPC and AI have always been critical for energy exploration for decades. Now, with the energy transition and the convergence of HPC and AI, energy companies are finding these technologies even more indispensable. They can reduce time to insight and lower costs of exploration and investment risks using a variety of workloads, such as carbon capture/storage, CAE, life/materials sciences, advanced optimization, and more.
Oil and gas companies need a reliable and experienced IT partner for their transformation journey. As a market leader in HPC/AI with decades of experience serving not just the oil and gas industry, HPE is uniquely positioned to help companies along their journey.
HPE offers a comprehensive portfolio of end-to-end solutions for energy transition using one of the most advanced HPC technologies, high-value services, and the best ecosystem of partners, delivered on the edge, the data center, and the cloud.
Worldwide, many energy companies already rely on these HPE solutions. Now, HPE is front and center driving collaborations and innovations to help energy customers improve oil and gas exploration and production (E&P), reduce energy-related emissions, and transition to alternative renewable energy sources to stay competitive. HPE is once again playing an indispensable role in the new era of energy and for the sustainable future of our planet.
- 1 “2019 Market Results, New Forecasts and HPC Trends,” Hyperion Research, April 2020
- 2 “Paris Climate Agreement: Everything You Need to Know,” NRDC, 2021
- 3 “The future is now: How oil and gas companies can decarbonize,” McKinsey and Company, Chantal Beck, Sahar Rashidbeigi, Occo Roelofsen, and Eveline Speelman, January 2020
- 4 “Shell Ordered by Dutch Court to Cut Carbon Emissions,” Wall Street Journal, Sarah McFarlane, May 26, 2021
- 5 “Activist Wins Exxon Board Seats After Questioning Oil Giant’s Climate Strategy,” Wall Street Journal, Christopher M. Matthews, May 26, 2021
- 6 “High Performance Computing for a sustainable hydrogen economy,” Shell, 2020
- 7 “ISC 2021 HPC Market Update,” Hyperion Research, June 2021