Summary
This technical report discusses the application of large language models (LLMs) and multimodal learning in subsurface exploration and production (E&P) within the energy sector. It outlines how LLMs, originally designed for natural language processing, are being adapted to enhance operational efficiency and improve decision-making in subsurface geology. The report details the role of multimodal models, which can integrate various data types, such as seismic surveys and geological maps, to provide a comprehensive understanding of subsurface environments. Furthermore, it introduces seismic foundation models (SFMs) developed by TGS, which are tailored to process seismic data and other geophysical inputs. The report highlights the significance of large-scale pre-training on diverse datasets for effective generalization across geological formations. It emphasizes the potential for integrating multiple data modalities to deliver accurate insights, positioning these models as critical tools for the offshore oil and gas industry.
