The development prospects and key challenges of artificial intelligence technology in oil reservoir characterization - Liding Wang; Zhenpeng Li (Resources Data Journal, 2025)
This paper explores recent advancements and future development trends in the application of artificial intelligence (AI) to oil reservoir characterization. It focuses in particular on breakthroughs in deep learning, machine learning, and multi-source data fusion that enhance the accuracy of reservoir description, automate data processing, enable real-time monitoring, and support dynamic prediction. The paper highlights innovative directions such as customized model development, human–machine collaborative decision-making, uncertainty quantification, and risk assessment, outlining key pathways through which AI is transforming reservoir characterization from traditional experience-based methods into intelligent, data-driven approaches. In addressing current challenges—including data complexity, limited model generalization, and adaptability to diverse application scenarios—it proposes targeted research directions and development strategies. This paper aims to provide a systematic and comprehensive technical reference for researchers and engineers in oil and gas exploration and development, promoting the deep integration of AI with reservoir characterization and contributing to the efficient and sustainable utilization of hydrocarbon resources.