insideBIGDATA Guide to Energy – Part 2

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13 ways big data can improve efficiency and drive down costs in the energy industry

While these disruptors represent significant challenges, companies are finding ways to overcome those challenges by investing in big data analytics.

It’s worth noting that big data isn’t a simple or easy solution to any of the problems energy companies face. In order to do analytics well, you need the right mix of talent, hardware, and software. You need clearly defined problems, targeted objectives, and executive support to shepherd the project.

Putting all those elements into place is very difficult. According to some estimates, between 60% and 85% of all big data projects fail.

But the potential upside is so significant that most energy companies are investing significantly in big data technology.

What are those potential benefits? Here are 13 ways that big data analytics is helping energy companies manage the current level of disruption.

1. Better weather prediction

Today, energy companies have access to much more weather data than has ever been available before. Many have their own weather sensors installed on key equipment, and they also subscribe to both paid and free sources of weather data.

Many have also invested in advanced servers or built supercomputers that can enable them to apply artificial intelligence (AI) or machine learning (ML) techniques to that data. This allows them to create highly accurate, localized weather forecasts. Armed with this information, they can make adjustments that allow them to better prepare for and respond to extreme weather events. While better weather prediction can’t stop or slow climate change, it can make it easier for companies to deal with natural disasters caused by climate change.

2. Faster research

Big data analytics can also speed the process of conducting research. Whether companies are searching for hidden oil reserves, inventing new types of photovoltaic panels, testing batteries for energy storage, choosing locations for wind turbines, or doing other scientific work, big data analytics can help the process go faster.

Today’s servers are capable of volumes of calculations that would have been nearly impossible just a few years ago. That means companies can analyze more data much more quickly than ever before. That can help them make breakthroughs that might slow or mitigate global warming.

3. Preventive maintenance

Every energy company relies on equipment of some kind to help them produce, transmit, and/or deliver energy to consumers. Today, a growing number are installing IoT sensors that can detect minute changes in the way the equipment is operating. By performing advanced analytics on this data, they can predict in advance when a particular piece of equipment or part is going to need repair.

This information allows firms to schedule maintenance at a time when it is least disruptive to their operations. For example, if a utility knows that a transformer is likely to fail in the next two weeks, they can take it offline for repairs overnight when demand is low, making it easier for the rest of the grid to compensate.

4. Pipeline integrity

Companies that transmit oil and gas through pipelines can use a very similar process to detect and prevent future leaks. Spilling a large volume of oil and gas can be disastrous for both the environment and a company’s reputation.

By installing sensors at key locations on the pipeline, companies can detect small changes in pressure, temperature, flow, density, or other factors that might indicate problems. In some cases, they can also use computer vision techniques or ultrasound to detect cracks or dents in the pipe that could eventually result in a leak.

And if the worst happens and a leak occurs, these sensors provide the information to the companies right away, as well as allowing them to use analytics to determine the best response.

5. Security analytics

Most energy companies deal with near-constant cyberattacks—sometimes from state actors as part of ongoing cyberwar and sometimes from ordinary cybercriminals hoping to make a buck or sow chaos.

Security professionals often feel like they are falling behind. As quickly as they can come up with defenses, bad actors are coming up with brand new kinds of attacks that companies have to figure out how to detect and prevent.

One of the most successful strategies for dealing with these evolving threats has been to rely on big data analytics. Many of today’s best cybersecurity tools use machine learning models to define a baseline “normal” level of activity on corporate networks and then immediately spot anything out of the ordinary. These tools aren’t foolproof, but they can make energy companies safer.

6. Seismic surveys

For several decades, oil and gas companies have been relying on seismic surveys to help them locate deposits within the earth. After setting off small explosions, they use a seismic array to measure the waves as they flow through the earth’s crust, allowing them to create a visualization of what lies beneath the surface.

Today, geologists must look much deeper to find the oil and gas they are looking for. That requires larger arrays that generate much more data—generally terabytes or petabytes. To handle that much data, companies need hardware with scalable storage, fast processors, and advanced graphics processing units (GPUs) that will allow them to conduct analytics on their survey data to find the resources they are looking for.

7. Geophysics simulations

Scientists combine seismic survey data with other data to help them build geophysical models. These models are incredibly valuable because they allow oil and gas companies to predict with a high degree of accuracy where they will find underground reserves, as well as the likely volume and quality of those reserves.

Today’s models are far more complex than those created in the past, relying on much larger volumes of data and frequently incorporating advance ML techniques. Again, this requires powerful servers similar to what is required for processing seismic surveys.

8. Talent management

Today, attracting and retaining high-quality workers is a make-or-break proposition for many energy companies. Because competition is so fierce, many companies are investing in talent management software to help them accomplish these goals. The best of these systems rely on big data analytics to identify the best candidates. Some companies are also turning to predictive systems that attempt to identify staff members who are likely to leave the company so that managers can take action to try to get them to stay. But in order to make these predictions accurately, the systems need a high volume of data.

9. Supply chain management

While no amount of data can make computer chips or other equipment magically appear when none are available, big data can enable better visibility into the supply chain, and big data analytics can improve forecasts about which supplies are likely to be necessary. Energy companies have long used supply chain management tools to keep tabs on the flow of equipment and goods. By combining these resources with big data from other parts of the organization, firms can improve the quality of the insights they are gaining, speed up operations, and reduce risk.

10. Predictive consumption models

Using advanced predictive analytics and ML algorithms, data scientists can create more accurate models of consumer energy use under various scenarios. Using these tools to analyze historical energy data can’t tell you when international conflict or extreme weather is going to occur, but it can tell you what is likely to happen when events like these take place. That can help firms plan ahead so that they can better meet demand and keep the world supplied with the energy it needs to function. It can also help them reduce the risk that they will miss out on potential revenue because they are unable to keep up with demand.

11. Predictive price modeling

Data scientists can also apply similar modeling techniques to pricing, allowing them to forecast with some certainty what is likely to happen to energy prices in different situations. This information can help oil and gas companies decide when, where, and whether to drill. It can help refineries decide whether to increase capacity or close plants. It can help utilities more accurately set prices for the energy they deliver to businesses and consumers. And it can help energy companies of all kinds become more competitive.

12. Speed

The process of delivering energy to end users is long and complex. Big data analytics doesn’t make any one piece of this process dramatically faster. However, it can make nearly every step a little more efficient. Taken as a whole, these improvements can have a cumulative effect of making companies able to execute on their plans significantly more quickly. That speed can be tremendously important as companies seek to keep up with the competition and respond to the current disruption in the marketplace.

13. Agility

Speed is closely related to agility. By their nature, most energy companies are not naturally agile. You can’t drill an oil well or build a new power plant in a day. And once projects like this are underway, changing your mind carries a huge amount of risk. But the speed afforded by big data analytics can help organizations make good decisions more quickly. In an industry not known for quickly reacting and adapting to change, any improvements in this area can make a significant impact on the bottom line.

Looking ahead

Most analysts believe that this period of intense disruption in the energy industry is likely to continue at least through the end of this decade. And the effects of climate change will probably only intensify for many decades to come.

Fortunately, organizations have growing amounts of big data from a wide variety of sources to help them deal with this disruption. The firms that navigate this period of time most successfully could very well be those that do the best job of converting their big data into actionable insights that can guide their decision making.

How to build an environmentally friendly data center

Energy companies face a dilemma: In order to deal with the challenges created by global warming, they need very powerful computing infrastructure that can perform the big data analytics that can give them the insights they need. But those powerful computers themselves can add to global warming, making it difficult for companies to reach their sustainability goals.

Fortunately, it is possible to create a very powerful data center that is also environmentally friendly.

For example, the University of Cambridge Research Computing Services built one of the world’s greenest supercomputers, the Wilkes 3, with Dell PowerEdge XE8545 servers. In fact, the Wilkes 3 is currently fourth on the Green500 list of the world’s most energy efficient supercomputers.

The Wilkes 3 system includes 80 nodes with 26,880 cores in its AMD EPYC 7763 processors. The EPYC chips are the world’s highest performing x86 server CPUs, which is ideal for workloads like big data analytics. Augmenting those CPUs are 320 NVIDIA A100 GPUs, which help the system achieve 4.5 to 5 petaFLOPS of computational power while driving down the overall energy consumption.

Another organization that used Dell PowerEdge servers featuring NVIDIA GPUs to create an environmentally friendly supercomputer was the Italian firm Eni. In this case, the company chose to use solar power for its data center, making the installation even more sustainable.

Dell Technologies is committed to advancing sustainability through its processes and products, including its advanced server hardware for big data analytics.

Over the next couple of weeks we’ll explore these topics:

Download the complete insideBIGDATA Guide to Energy technology guide courtesy of Dell Technologies and AMD. 

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