An Advanced in-Line Sensing AI Framework for Enhanced Drilling Operations

Klemens Katterbauer, Abdallah Al Shehri
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Abstract

4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas. From AI inspired well logging data interpretation to real-time reservoir monitoring, technologies have led to cost savings, increase in efficiencies and infrastructure centralization. In this work we provide an overview of how autoregressive deep learning methodologies can lead to major advances in the field of formation evaluation and reservoir characterization, providing a comprehensive overview of the technologies developed and utilized in this domain. Furthermore, we provide a future outlook for smart technologies in formation evaluation, and how these sensor-derived data can be integrated. This also describes the challenges ahead. Future developments will experience a growing penetration of 4IR technology for enhancing formation evaluation in subsurface reservoirs.
一种用于增强钻井作业的先进在线传感AI框架
第四次工业革命(4IR)技术在油气行业发挥了至关重要的作用,使数据分析和自动化达到了前所未有的水平。储层评价和储层监测是优化储层产量、最大化波及效率和表征储层的关键领域。自动化、机器人技术和人工智能(AI)已经导致了这些领域的巨大变革。从受人工智能启发的测井数据解释到实时油藏监测,这些技术节省了成本,提高了效率,并实现了基础设施的集中化。在这项工作中,我们概述了自回归深度学习方法如何在地层评价和储层表征领域取得重大进展,并全面概述了该领域开发和利用的技术。此外,我们还展望了信息评估中的智能技术的未来,以及如何将这些传感器衍生的数据集成在一起。这也描述了未来的挑战。未来的开发将越来越多地采用4IR技术,以提高地下储层的地层评价。
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