Artificial general intelligence for the upstream geoenergy industry: A review

作者：Jimmy Xuekai Li, Tiancheng Zhang, Yiran Zhu, Zhongwei Chen · 发表于：Gas Science and Engineering · 年份：2024 · DOI：10.1016/j.jgsce.2024.205469 · 被引用次数：21 · 研究领域：Reservoir Engineering and Simulation Methods、Oil and Gas Production Techniques、Hydraulic Fracturing and Reservoir Analysis

Artificial General Intelligence (AGI) is set to profoundly impact the traditional upstream geoenergy industry (i.e., oil and gas industry) by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the upstream geoenergy landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising m...