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in the energy sector
Every day, energy leaders make key decisions that determine the future of their companies. As the energy sector evolves, leaders face many challenges steering their companies toward success.
Because the demand for energy continually changes, often even hourly, forecasting production is a constant challenge, even with sophisticated models. Renewable energy sources, such as wind and solar, make it even more complex, as they are less predictable and vary based on weather and location. Additionally, energy companies must make both short- and long-term forecasts, each of which requires a different model, making predicting demand even harder.
Energy companies also often struggle with operational efficiency throughout their complex processes, from drilling and production to distribution. With an aging infrastructure as the foundation, organizations are constantly maintaining and upgrading technology, resulting in downtime and a strain on resources. At the same time, companies are constantly monitoring and adapting to new regulation changes, especially in safety and environmental protection, to stay in compliance, which is often stressful and time-consuming.
Keeping costs down is also a constant priority and challenge for energy companies. The core pieces of equipment, including drilling rigs, pipelines, and renewable energy plants, require a significant investment, making it hard to expand operations while staying on budget. With the equipment often being older and aging, energy companies also must spend considerable money on maintenance and replacement. Because the energy sector requires highly skilled resources, labor costs are often a large portion of the budget as well. At the same time, the revenue brought in often varies significantly based on constant price and demand volatility.
Limitations of traditional tools
Energy companies increasingly turn to data for the insights to overcome these industry wide challenges. However, companies are often overrun with a high volume of data, often from different data sources. Additionally, the data must be complete, accurate, and as close to real-time as possible for the best insights and results.
As the industry continues to evolve and face new obstacles, companies must turn to harnessing this data to improve their decision-making and operational efficiency. By using data analytics tools, companies can significantly improve their ability to analyze, model, and use data. Many energy leaders turn to traditional tools, which typically fall into two categories.
Statistical data tools focus on finding patterns and trends while analyzing and interpreting the data. These tools can be simple, such as a spreadsheet in Google Sheets or Microsoft Excel, or exceptionally complex, such as MATLAB and SAS.
While statistical tools are typically accurate, they also add new problems to the data analytics process. The complexity of most statistical tools requires specialized skills only found in expert users, which limits their usability throughout the company. Even spreadsheets, which appear straightforward, require a power user’s expertise to use advanced data analytics skills.
The tools also often create data silos and do not easily integrate with other tools, creating limited data connectivity. Energy companies need the ability for data to seamlessly integrate tools to allow better data analytics results. Limited data connectivity often results in inaccurate results and makes it harder for departments to collaborate across the company.
At the same time, the tools also provide restricted exploration capabilities. While you can use the tools for basic functions, they cannot interact and visualize the data at the level most energy companies need. The tools typically include predefined analysis and templates, so you cannot perform custom calculations and modeling. Because the tools often do not allow building custom types of charts, reports, and dashboards, energy companies often cannot fully visualize data based on the specific needs of the project or company.
Statistical data tools
Specialty data tools focus on a specific industry or data type. Taking into account the unique workflows and needs of the industry, these tools can provide industry-specific knowledge to the analytics process. These types of tools for the energy sector include energy management systems, geographic information systems, and advanced metering infrastructure.
While statistical data tools require specific knowledge, specialized tools take it to a higher level by needing a product guru, often requiring a position devoted to the specific tool. Because only a single team member can use the tool, this limits the use cases and ability to create models. This level of experience increases the budget and makes it out of reach for some energy companies. Additionally, engineers also are limited in using creativity to solve complex problems due to the constraints of the tools.
Specialized tools also have features that make this type of technology less flexible. The tools must use native data, which requires additional processing and resources. While the predefined workflows and proprietary models make it easy to quickly get up and running, these also limit the number of customizations. Energy companies often find it impossible to customize the tools, or that they require significant time to create models for their specific needs.
Specialty data tools
Because neither type of tool meets the requirements for analyzing and modeling data in the energy sector, many companies are turning to tools using the visual data science approach. By blending the best of both statistical and specialized tools, the visual data science approach democratizes data analysis for all engineers.
Visual data science connects multiple data sources dynamically, which allows the interactive exploration of data, models, and visuals. The three key attributes of this approach are:
Data intensive The first step is connecting and combining multiple data sources into a single source. Next, the tool wrangles and visualizes the technical data. By focusing on the data first, energy companies have the flexibility to maneuver and explore the data in meaningful ways.
Industry focus Because the energy industry brings specific requirements, visual data science applies industry standards to all data during the workflow. Additionally, companies can add proprietary techniques to the process.
Enterprise scale The data needs of energy companies change as the volume of services increases and decreases due to market conditions. The visual data science approach makes it easy to capture and deploy best practices for an ever-changing demand. Energy companies also can add and manage new users – even thousands – within minutes as demand changes based on company needs. Because each user has different needs, you can assign each user a profile based on their access needs.
It's not only about the data volume able to analyze and process, but also about the possibility scale the platform to thousands of end users. The product can scale based on the company's needs.
Energy companies take advantage of the Spotfire approach and technology to use data more efficiently. Hunt Oil was looking for a faster and more efficient way to gain applicable insights from its application. Because of inflexible vendor-supplied applications, the oil company generated out-of-date insights, which limited its agility. After working with Spotfire, Hunt Oil launched Hunt’s Smart IoT Drilling System, using the Spotfire Drilling Accelerator, combined with other tools, to provide a single, real-time view of data.
“The Spotfire Drilling Accelerator gave us the foundation and the framework to build the Smart IoT Drilling System, so we didn’t have to start from scratch,” says Brian Alleman, senior drilling engineer.
The visual data science approach gave them the flexibility and accuracy that were previously not possible with the legacy-based tools. Previously, Hunt Oil team members took 18 to 24 hours to spot new patterns in data. However, with the new platform, they had more accurate and insightful insights at their fingertips in seconds.
One of the first projects was batch optimizing wells, which brought significant time and money savings. Next, engineers added downhole, bit assembly, hydraulic, and pump data, which allowed optimizing in new ways. Team members also used the visual data science approach to determine the right force and RPM combinations for striking the surface with the drill bit. With the platform, all streaming data is connected, making it easy to develop new projects and access more accurate query-the-future insights, such as what equipment is likely to fail or need repairs in the short term.
“We are combining sets of data in ways we were never able to do in the past, leveraging data from our reporting and engineering databases. With Spotfire, we take data from the server and turn it into an asset that is going to make a difference,” says Alleman.
What is the Visual Data Science revolution?
Hunt Oil optimizing data in new ways with visual data science technology
41%
35%
2016
2017
2018
2019
Harnessing the power of visual data science
Disclaimer: The Reuters news staff had no role in the production of this content. It was created by Reuters Plus, the brand marketing studio of Reuters. To work with Reuters Plus, contact us here.
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The Reuters news staff had no role in the production of this content. It was created by Reuters Plus, the brand marketing studio of Reuters.
Produced by Reuters Plus for
The Reuters news staff had no role in the production of this content. It was created by Reuters Plus, the brand marketing studio of Reuters.
Produced by Reuters Plus for
Disclaimer: The Reuters news staff had no role in the production of this content. It was created by Reuters Plus, the brand marketing studio of Reuters. To work with Reuters Plus, contact us here.