In this episode of our Energy Transition Talks podcast series, CGI space expert Harjit Sheera shares with Peter Warren how the volume of space data is not only ever-increasing, but also growing in impact and application across industries. Discussing how processing space data for accessibility and effective use was previously an arduous task, they explore how artificial intelligence (AI) and advanced processing platforms are helping organizations make the most of their space data. From environmental impact monitoring to emissions mapping and data layering, space data is changing the way we see and act on energy transition goals.

Improving and accelerating traditionally cumbersome space data with AI

Operating across the entire space stakeholder chain, CGI space experts work as advisors for space organizations, collaborate with regulatory agencies and support end users through application development and managed services.

In her almost 20 years of experience working in space, Harjit knows the legacy challenges space data poses, specifically in terms of harnessing and translating its vast volume. “It takes a lot of processing power, a lot of storage energy and a lot of standardization to make that data available to people who can turn it into something that the end user will see.”

Emerging processing engines (including those processing earth observation data, examining imagery or setting standardized requisite parameters) are using AI, machine learning and advanced algorithms to refine further and perform better, faster. This means greater volumes of data can be processed more efficiently and more, diverse user requirements can be addressed.

Specifically, AI helps identify key elements in satellite images and processes them faster, based on set user requirements. For example, Harjit shares the use case of farmers leveraging AI and satellite imagery data to monitor and demonstrate how they’re farming their land and what kind of crops they’re growing, to claim government subsidies.

Peter highlights the positive implications the advanced deep learning and crop recognition use case has for energy organizations who want to monitor, for example, leaks or the growth of vegetation under power lines and near utility company infrastructure. It all helps to reduce the cost of maintenance and potential damage.

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AI strategies, asset optimization and data quality: the new frontier for oil and gas

In the latest episode of our Energy Transition Talks, Maida Zahid sits down with CGI experts Mark van Engelen and Curtis Nybo to discuss the growing role of artificial intelligence (AI) in the oil and gas space. Specifically, they look at the evolution of—and need for—generative AI in the industry, the value of an iterative, domain-based approach to implementation and cross-industry AI use cases to advance the energy transition.

The new frontier for AI in oil and gas: data, demographics and domain-based approaches

The use of AI to support the asset-heavy oil and gas industry has been in effect for some time, especially for optimizing asset maintenance and predictive maintenance. However, new areas of need are driving the evolving role and growing value of AI within organizations.

First, Mark mentions, is the need for generative AI to help unlock the vast amounts of data in the oil and gas companies (e.g., on the GIS side, on their land side, upstream, downstream, etc.). This rise of ‘data GTP’ as he calls it, means gaining access to that data in a natural language format to pose questions like, ‘How many barrels did you produce last month?’ without clicking through several layers of reporting.

Second, as shifting demographics and changing workforces expose a knowledge gap between retiring experts and new professional entrants, generative AI is helping organizations bridge the gap and provide access to legacy knowledge in an efficient manner.

More crucial than vast amounts of data is the quality of the data. When working on use cases with clients, Curtis says they begin with domains that have decent data quality or supporting data management processes, to maximize ROI and time to completion.

As he explains, “we take a domain-based approach, where in parallel as you’re working on an AI project in the one domain, you can clean up the data of another domain next on your list,” so you’re not applying AI to the whole company at once; you’re starting with one area or team and expanding throughout the organization.

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In part two of our Energy Transition Talks conversation on generative artificial intelligence (AI), CGI experts Diane Gutiw and Peter Warren further explore the implications and applications of AI in the energy and utilities industry. Building upon their discussion in part one, they examine how digital twins, change management and trusted data are shaping the use and performance of AI in energy organizations, ultimately looking to the future of AI as multimodal, human-driven technology solution.

The key to realizing AI value: integrated solutions and digital twins

Increasingly, the greatest benefits of generative AI are emerging not in single solutions, but in integrated, multi-model, multimodal ways of pulling in information, producing expert advice and automating certain functions.

The energy industry, says Diane, is “a great example of a very complex environment with lots of different types of media and data that can be leveraged by these new and upcoming technologies.”

In her view, AI is headed toward digital twin models and integrated solutions. In the energy industry, this increased data-driven automation can help make both the grid and operations more efficient.

Peter Warren shares one key use case for digital twins is to help organizations understand other markets better, as they transition their current model. “You might know your existing industry well,” he says, “but as you move from traditional carbon-based energy to something less carbon-based, be it hydrogen or electricity, you may not know those markets; being able to create a digital twin of something you haven’t formally understood is a huge benefit.”

Diane agrees and suggests that the adoption of a digital twin to represent an organization’s current environment is a great use case, especially where there’s a data-intensive end-to-end workflow. Not only does this provide a robust view of the existing environment, she says, “but also it allows organizations to look at different scenarios and leverage AI to say, for example, ‘What would happen to the grid if this event happened, and how could I automatically adjust?’”

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