Accelerating the Pace of Exploration with Generative AI

SparkCognition's proprietary Generative AI Platform technology delivers unprecedented capabilities attuned to the needs of the industrial sector.

Learn about the breakthrough generative AI approach being developed by Shell and SparkCognition that uses deep learning to generate reliable subsurface images.

How is SparkCognition leveraging generative AI?

SparkCognition has announced a collaboration with Shell to accelerate the pace of imaging and exploration of subsurface structures using generative AI technology.

The proprietary generative AI approach being developed by Shell and SparkCognition uses deep learning to generate reliable subsurface images using far fewer seismic shots than traditionally necessary while preserving subsurface image quality. By creating a highly accurate visualization of the seafloor substructure’s seismic profile, petroleum reserves can be identified much faster and more efficiently.This ground-breaking approach can be applied to other critical problems, including on-shore exploration, carbon sequestration, threat assessment for national defense, satellite imaging, and more.

Like smartphones disrupted apps with never-before-seen features, generative AI will inspire a new generation of disruptive AI applications without requiring massive training data. SparkCognition is pioneering the use of generative AI for industrials, developing industry-specific large language models (LLM) leveraging deep learning algorithms that can recognize, summarize, translate, predict, and generate content from large unstructured datasets. SparkCognition’s generative AI capabilities enable organizations to reduce the amount of foundational information needed to make informed decisions by a factor of 20X or more while executing complex tasks in a fraction of the usual time. Applications of this technology will enhance how organizations prioritize R&D investments, manage production, optimize supply, direct distribution, and more.


Migration modeling in oil and gas exploration typically consumes 80% of the exploration time. Using AI inferencing, the amount of data required for the model can be reduced by up to 99%, minimizing the time investment for this step to less than 5% of standard approaches.

What is generative AI?

Generative AI is a category of AI focused on creating new data or content based on patterns and relationships in existing data. New tools like ChatGPT, DALL-E, Midjourney, and others have heightened awareness of the potential of leveraging generative AI for business and personal use cases.

In broad terms, machine learning models have always been partitioned into two groups: discriminative AI models and generative AI models. As their name suggests, discriminative models are used to discriminate between different kinds of existing data, while generative models are used to generate new data from existing data. For example, a discriminate model can interpret what types of shoes exist in a given data set (e.g. sandals vs sneakers vs pumps) and a generative model can create entirely new shoe forms based on examples it has seen in the given data set (e.g. a sneaker-sandal hybrid). While discriminant models learn the differences between different categories of the data, they don’t necessarily bother with how the data behaves. Generative models capture the whole distribution of the data. By doing so, they provide an ability to generate new instances of this data based on sampling from the the data distribution. Having learned a generative model of the data, we can actually produce (generate) new data.

Three benefits of generative AI (that scale)

“Collaborating with SparkCognition and leveraging their expertise in generative AI is opening an exciting opportunity to deliver a new wave of innovation at Shell.”

— Gabriel Guerra, Vice President of Innovation & Performance at Shell



Large language models can be trained on vast amounts of text data, allowing them to recognize patterns and relationships in natural language to generate highly intuitive and relevant answers to queries.


By analyzing and learning from existing visual art, image data, text works, etc., Generative AI algorithms can generate new artworks in the style or brand of existing works or create entirely new styles.


Generative AI can help create synthetic data to provide more examples for models to learn from. This can be especially useful when there is a limited amount of real-world data available, such as seismic imaging.

Learn more about generative AI

Learn more about how generative AI can recognize, summarize, translate, predict, and generate content from large unstructured datasets to deliver real-time operational guidance from simple conversational prompts, turn low-fidelity data into provide high-fidelity insights, and much more.
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    Learn more about our Generative AI platform

    With a focus on solving critical problems across multiple sectors, our Generative AI Platform empowers organizations to drive unprecedented innovation, unlock new opportunities, and achieve remarkable outcomes. 

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