Energy data giant TGS is undertaking a significant transformation, leveraging artificial intelligence and cloud-based platforms to create a dynamic, predictive digital model of the Earth’s subsurface. By applying advanced computational analysis to the world’s largest library of geological data, the company is accelerating exploration and development for traditional energy sources while simultaneously building foundational tools for emerging sectors, including carbon capture and geothermal energy.
This strategic pivot addresses a dual challenge facing the global energy industry: rising demand that requires more efficient and successful exploration, and the urgent need to develop sustainable, low-carbon solutions. The initiative, led by Executive Vice President of Imaging & Technology Wadii El Karkouri, aims to reposition the firm from a data provider into a technology-first enterprise that delivers actionable intelligence. By centralizing vast and varied datasets in the cloud and deploying sophisticated machine learning algorithms, TGS is creating new ways to understand and utilize the complex geological systems beneath the surface.
Unifying Complex Geological Datasets
A comprehensive understanding of the subsurface relies on integrating multiple types of highly complex information. The primary data source is seismic imaging, which functions like a planetary-scale ultrasound to map the size, shape, and structure of underground rock layers, faults, and folds. This is complemented by well data, which includes information gathered from drilling operations. Well logs are produced by lowering geophysical instruments into a borehole to measure the rock’s properties, while physical core samples provide a direct, detailed look at the rock and fluid compositions. Production data from existing wells offers further insight into how fluids and pressures change over time.
Historically, managing and interpreting these massive, terabyte-scale datasets presented a significant challenge. The information is collected by different teams using various technologies, creating a complex patchwork of data that must be systematically collected, stored, and integrated to build accurate three-dimensional models of underground reservoirs. These models are critical for making high-stakes decisions, from identifying promising drilling locations to mitigating risks associated with extraction and storage projects.
The Role of AI in Subsurface Analysis
TGS is deploying artificial intelligence, particularly deep neural networks, to automate and enhance the analysis of this complex geological information. Machine learning models are proving transformative in seismic data processing, a crucial step that has traditionally been a laborious task for geophysicists. These algorithms excel at separating the desired geological signals from background noise, resulting in clearer and more accurate images of subsurface structures.
Predictive Geological Modeling
The company is moving beyond data processing toward predictive analytics. In a partnership with machine learning firm Earth Science Analytics, TGS applied AI algorithms to a vast 1,500-square-kilometer dataset to automatically interpret geological features. The AI models were trained to predict key rock properties such as porosity, lithology, and water saturation directly from seismic and well data. This approach provides insights with a high degree of confidence, helping to identify potential hydrocarbon reservoirs or stable formations for other uses. El Karkouri has described the company’s ambition as building “seismic foundation models,” likening the goal to creating a “Chat GPT for the subsurface” that can generate new exploration ideas based on the entirety of TGS’s data library.
Cloud Platforms for Scalable Access
Making this data and the associated AI tools accessible and usable is another core component of the company’s strategy. TGS has launched several cloud-based platforms, including Well Data Analytics and TGS Data Verse, to provide its clients with on-demand access to its extensive resources. These platforms function as a form of Data Management as a Service (DMaaS), handling the complex backend work of data ingestion, quality control, and cataloging.
Driving Efficiency and New Insights
The TGS Data Verse platform empowers energy companies to streamline their data workflows and accelerate decision-making, moving from months-long processes to days. A key innovation is the use of Multidimensional Input/Output (MDIO), an open-source data storage format specifically designed for large, multi-dimensional energy datasets like seismic and wind models. According to the company, this technology can reduce cloud storage costs by over 30% while enabling faster data access for computational workloads. By centralizing data and providing advanced analytics in a scalable cloud environment, TGS enables geoscientists and engineers to uncover trends and patterns that might otherwise remain hidden in disparate files.
Applications for the Energy Transition
While these technologies enhance traditional oil and gas exploration, TGS is increasingly applying them to support the development of renewable energy and carbon-reduction initiatives. The same subsurface intelligence used to find hydrocarbons is essential for identifying secure carbon storage sites and prospecting for geothermal resources.
Carbon Capture and Sequestration
The company offers a tool called Carbon AXIOM, which leverages its subsurface data library to screen and identify feasible locations for permanent CO2 storage. This process involves a detailed geological assessment of saline aquifers, considering factors like the integrity of the caprock, the characteristics of the reservoir, and fluid dynamics to ensure long-term sequestration. TGS is actively expanding its CO2 storage assessment coverage, with projects underway to map the potential of numerous onshore basins across the United States.
Geothermal and Wind Energy
TGS is also positioning its data to advance geothermal exploration. The company provides comprehensive databases and a tool known as the Geothermal Pathfinder, which uses an accessible interface to visualize subterranean temperatures and other relevant data to assess an area’s geothermal potential. This data-driven approach de-risks exploration for geothermal system assessments. Beyond geology, the company is leveraging its data integration expertise in the offshore wind sector, providing models and observational data to support the analysis and development of wind farm projects.
A Strategic Shift in Perspective
This fusion of a vast historical data library with modern AI and cloud technologies represents a fundamental shift in the company’s business model. As El Karkouri stated, TGS is evolving from a seismic company enabled by technology into a technology company that is enabled by its unparalleled access to seismic and other subsurface data. [from browse 1] By creating an integrated, intelligent, and accessible digital twin of the Earth’s subsurface, the company is building a platform designed to serve the full spectrum of the energy industry, from optimizing current resources to pioneering the solutions required for a low-carbon future.
