Enterprise Knowledge Search · Risk Management
OMV needs to reduce uncertainty in complex geothermal and carbon capture and storage decisions, while also making large volumes of structured and unstructured subsurface data easier for geoscientists to find, summarize, and use in day-to-day technical work.
Reported impact
OMV says the AI approach reduces uncertainty, improves safety and risk assessment, lowers field-design and operational costs, speeds project execution, improves information quality, and supports more profitable low-carbon business development.
OMV is building two complementary in-house AI capabilities for its low-carbon energy business. For geothermal and carbon capture and storage planning, OMV and Stanford University are creating an AI-driven decision-making tool that ingests subsurface and surface operating data and evaluates field-development choices such as where to drill wells, how many wells to drill, when to inject CO2, hydrogen, or cold water, where to place CO2 injectors, and which monitoring techniques to use. The tool acts as a compute layer for simulation-heavy design and safety decisions, helping reduce uncertainty and field-design effort. In parallel, the OMV Energy Chat Bot is being developed with OMV colleagues in Norway to work over large volumes of structured and unstructured subsurface data. It uses GPT/LLM capabilities with knowledge graphs to summarize, extract, and present technical information to geoscientists, turning buried domain data into faster decision support.
OMV feeds structured and unstructured subsurface and surface data into two AI paths. The decision-making tool analyzes reservoir and field-design variables to recommend drilling, injection, injector-placement, and monitoring choices for geothermal and CCS projects. In parallel, the OMV Energy Chat Bot uses GPT/LLM processing over domain data linked through knowledge graphs, then serves summaries, extracted facts, and technical presentations to geoscientists for project execution and decision-making.
"For CCS, it strategically positions CO injectors and selects effective monitoring techniques, ensuring the highest level of long-term safety in CO storage."
"The OMV Energy Chat Bot, which is developed together with our colleagues in Norway, will leverage generative pre-trained transformers (GPTs), LLMs (Large Language Models), and knowledge graphs."
"They lower field design expenses such as simulation and labor costs. They also lower operational expenses related to monitoring, backup wells, and other activities."
"This solution significantly reduces uncertainty, increases profitability, and optimizes low-carbon business development."
"This will allow faster project execution, increased information quality, and better decision making by revealing hidden information."
"To address these challenges, we are collaborating with Stanford University to create an AI-driven decision-making tool."
"We manage a massive volume of unstructured and structured subsurface and surface data which contains valuable hidden insights, but retrieving it historically required substantial manual effort."
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Scoring methodology
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Traceability
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