Digitalization and Data

Convergence of Data Science and Energy

Digital solutions are critical to tackle both economic growth and deep decarbonization. Digital simulation platforms act as convergence point to integrate models, tools, and business operations.

The whole energy system needs to be considered—how we make, move, store, and use fuels and gases for clean energy transitions, and these systems are extremely complex.

There are multivariate issues and changing, dynamic parameters that need to be considered. Software automations and digital solutions are a tool to aid in reducing the complexity of systems-thinking and provide options for better solutions. We can simulate what happens before implementing, seeing how it affects not only the process itself but also organizations, people, and other interacting entities.

Today, we’re using digital solutions to improve things like field operations, record information in databases rather than on paper, and analyzing that information to understand how we can make improvements and maximize the use of our current infrastructure. We’re starting to explore AI applications and taking a more predictive approach to operations, at times even changing business processes to leverage more of those automations and predictivity. We’re also using new low-carbon energy sources that change the way in which we interact with our existing energy systems and meet demand of consumers. At times, energy consumers can also be localized producers when you consider solar energy and battery storage.

To get to 2030 and 2050 goals, we need to use tools that integrate disparate applications and demonstrate ways to optimize our decisions while also ensuring equity and access while these energy transitions occur.

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Enabling energy transitions with digital solutions

This compelling session explores how automations, actionable data, advanced analytics, digital twin applications, and other cutting-edge technologies can be leveraged to support decarbonization commitments with a people-focused approach.

Applying Artificial Intelligence to Pipeline Operations and Maintenance

In the energy sector, artificial intelligence (AI) often comes into play as either machine learning or data science applications. A variety of applications and technologies involving AI are permeating the energy industry and particularly the oil/gas pipeline sector. A critical aspect to generating accurate results is ensuring that high-quality data is put into the tool and the people who use it have a high degree of knowledge.

Project work on excavation damage by GTI Energy has resulted in a new system now in the marketplace. One example of GTI Energy’s AI work is a system that leverages unique algorithms to detect when different types of construction equipment are digging near buried infrastructure. Each type of equipment has its own signature based on a number of sensors in the cab that identifies when the equipment is moving, digging, or performing any other activity in the field. Combining that data with location data will send real-time alerts when it may be digging too close to a pipeline.

AI also has been used by GTI Energy to build “mesh sensor networks” to link field sensors that can detect and pinpoint pipeline leaks. Similarly, if there are disturbances or unwanted equipment in rights-of-ways, stationary sensors are able to detect the threats and communicate specific messages regarding what the disturbance is based on trained data sets.

Proactively Protecting Pipeline Infrastructure with Digital Agents

Digital agents embedded into new technologies can automate actions, either presenting information to human agents or taking autonomous action based on trained conditions, such as enabling a valve to close without human interaction when the appropriate conditions are presented.

There will always be a need for figuring out ways to extend the lifetime of infrastructure investments, and GTI Energy is looking at doing the same things more intelligently, exploring opportunities to use robotics, sensors, and scanning technologies.

GTI Energy has developed a lot of specific AI applications and technologies to address narrow problems. But researchers are also looking at integrating disparate technologies and systems to make them interoperable—applying the sensors and analytics that can assist in identifying risk to aging infrastructure and leak detection to other networks and systems.

Recent investments in AI have focused has been on machine learning and data science along with operations and maintenance—particularly for various technologies that relate to near-term methane emissions reduction. There is also a lot of interest in finding ways to merge energy systems with the digital world, combining broad energy system applications with digital tools to optimize solutions for greener energy.

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