TGS, a global leader in energy data and intelligence, has announced a strategic partnership with PetroAI. This collaboration aims to provide E&P companies with an integrated solution that allows users to access TGS’s licensed data via the PetroAI platform. By leveraging PetroAI’s advanced AI models, this partnership enhances the ability to accurately quantify exploration and development trade-offs.

PetroAI will utilize TGS’ extensive subsurface, production and well data to create advanced AI models capable of accurately predicting well performance and identifying design sensitivities. This partnership offers quick access to pre-built, high-quality models tailored for both Proved Developed Producing (PDP) and Proved Undeveloped (PUD) wells at county or basin levels.

TGS’ comprehensive data library, including production, completions, logs, geology, pressure data and well economics, provides a wide range of seed data to allow PetroAI to generate on-demand Earth models. These models adapt to varying reservoir qualities, well-to-well interactions and diverse completion designs, providing E&Ps with real-time insights to optimize development strategies. Subsurface-informed AI models can also be used to create well performance heat maps.

Carl Neuhaus, Vice President of Onshore Products at TGS, said, “Through our partnership with PetroAI, operators can leverage TGS’ extensive engineering and geologic data library to rapidly develop AI models that predict well performance. This initiative fully supports TGS’ mission to deliver superior data solutions that enhance the workflows and applications of our customers.”

Troy Ruths, Founder and CEO of PetroAI, said, “I am very proud to announce PetroAI’s collaboration with TGS Well Data Analytics. By combining best-in-class data with best-in-class AI, our customers will be able to confidently forecast well performance in today’s most challenging environments. From infill well design to exploration beyond well control, TGS Well Data Analytics and PetroAI deliver the most powerful predictions in shale.”