摘要：The rapid integration of artificial intelligence into oil and gas exploration and development offers transformative opportunities within the context of the global energy transition. This article highlights the key advancements and challenges in artificial intelligence applications. Machine learning algorithms enable data-driven shale sweet spot prediction, overcoming the limitations of traditional methods by capturing complex controlling factors. Intelligent core image analysis, leveraging computer vision and foundation models, enables automatic mineral identification, pore analysis, and rock structure characterization, thereby providing a comprehensive framework for microscopic reservoir appraisal. Physics-informed neural networks address the limitations of purely data-driven reservoir simulation by embedding governing seepage equations into their loss functions, thereby ensuring physical consistency and improved generalization. Multimodal architectures significantly enhance unconventional shale gas production prediction by integrating geological heterogeneity with dynamic production behavior, leading to more accurate and stable forecasts. Collectively, these AI-driven approaches underscore the importance of combining domain expertise, multi-source data, and physics-aware modeling to achieve efficient and intelligent oil and gas development. ? The Author(s) 2025.

ISSN号：2207-9963

卷、期、页：卷17期3页179-183

发表日期：2025-09-01

影响因子：10.000000

期刊分区（SCI为中科院分区）：一区

收录情况：EI（工程索引）,ESCI（新兴资源引文索引）

发表期刊名称：Advances in Geo-Energy Research

参与作者：任义丽,毕剑飞,王牧明

通讯作者：刘程前

第一作者：惠钢

论文类型：期刊论文

论文概要：惠钢,任义丽,毕剑飞,王牧明,刘程前，Artificial intelligence applications and challenges in oil and gas exploration and development，Advances in Geo-Energy Research，2025，卷17期3页179-183

论文题目：Artificial intelligence applications and challenges in oil and gas exploration and development