Applications of artificial intelligence in shale oil and gas exploration research: a review

Abstract

The massive multi-modal datasets accumulated through long-term global shale oil and gas exploration and research provide critical support for in-depth elucidation of reservoir characteristics and hydrocarbon accumulation mechanisms. However, efficient governance and intelligent analysis of these datasets remain formidable challenges. Leveraging its robust feature extraction capabilities and advantages in nonlinear modeling, artificial intelligence (AI) is driving a paradigm shift in this field—from the traditional "experience-driven" model to a "data-driven" paradigm—empowering researchers to fully unlock the latent value of geoscience big data. This paper systematically reviews the latest advancements in AI applications for shale oil and gas exploration and development, covering over ten core tasks including data analytics, intelligent seismic data processing, and seismic interpretation. These applications, built on mainstream machine learning algorithms such as supervised learning, semi-supervised learning, and unsupervised learning, have significantly enhanced the accuracy and efficiency of exploration predictions. Looking forward, the "knowledge-data" dual-driven modeling paradigm—which integrates geological prior knowledge with multi-source heterogeneous data—will emerge as the key pathway to overcoming current technical bottlenecks. Furthermore, this paper provides an in-depth analysis of the practical constraints facing AI in areas such as data quality control, model interpretability, and the cultivation of interdisciplinary compound talents. It also offers forward-looking perspectives on the development trends of geoscience data standardization, the construction of shale oil and gas-specific large models, and intelligent geoscience software. Despite the limitations of existing technical systems, AI holds broad application prospects in the shale oil and gas sector and is expected to propel the entire industry toward a new stage of intelligent development.

Keywords

Artificial intelligence / big data / shale oil and gas / intelligent core analysis / seismic data / logging data

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