Large language models and foundation models in oil and gas exploration and development
Abstract
In oil and gas exploration and development, large language models and foundation models offer a new basis for integrating knowledge, data and engineering workflows. However, fragmented applications and unclear distinctions between model capabilities obscure how these technologies can support integrated decision-making. This review synthesizes the development and upstream applications of these models through a layered framework linking multimodal representation, domain knowledge, engineering computation, and operational decision-making. The findings indicate that the principal value of these models lies in connecting established petroleum technologies across disciplinary and organizational boundaries, rather than replacing specialized models or physical understanding. Within this framework, language models support knowledge interaction and workflow coordination, while foundation models extend representation learning across heterogeneous subsurface and operational data. The integration of these models with engineering models provides a pathway from isolated task automation toward coordinated intelligence across the exploration and development life cycle. The central challenge is to translate general model capabilities into reliable, transferable and economically sustainable engineering systems under geological uncertainty and operational constraints. However, addressing this task requires a closer integration of domain knowledge and physical mechanisms, alongside consistent data semantics and workflow-level evaluation. Herein, a staged development pathway is proposed, progressing from scenario-specific assistance to cross-disciplinary collaboration and bounded autonomous operation. This perspective reframes petroleum artificial intelligence as a system-integration problem and provides a conceptual basis for advancing human-machine collaboration in complex energy-resource development.
Document Type: Invited review
Cited as: Chen, Z., Li, Y., Ding, R., Liu, B., Ahmadi, M., Xiong, W. Large language models and foundation models in oil and gas exploration and development. Advances in Geo-Energy Research, 2026, 22(1): 11-25. https://doi.org/10.46690/ager.2026.10.02
DOI:
https://doi.org/10.46690/ager.2026.10.02Keywords:
Large language models, foundation models, multimodal learning, AI agents, digital twins, exploration and developmentReferences
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