公开含罕见井下异常事件的多变量时序数据集，助力智能预警系统研发。
3W Dataset 2.0.0: a realistic and public dataset with rare undesirable real events in oil wells
- 构建包含专家标注的多变量时序数据，涵盖真实井下异常事件。
- 2.0.0版新增数据与结构优化，提升模型训练与评估可靠性。
- 适合油气工业智能监测、故障预测研究者使用。
石油工业中井下异常事件可能导致经济损失、环境事故及人员伤亡。基于人工智能与机器学习的早期检测方案在多个领域已证明其价值。2019年，巴西石油公司Petrobras为应对相关公共数据集稀缺的问题，发布了首个3W数据集，该数据集由专家标注的多变量时间序列构成。此后，3W数据集持续协作开发，成为本领域的重要基准。本文介绍当前公开的3W数据集2.0.0版本，包含结构改进与新增标注数据。详细说明旨在支持3W社区及新用户提升已有研究成果，推动更鲁棒的检测方法、数字产品与服务的发展，实现对井下异常事件的充分提前预警，以采取纠正或缓解措施。
原文摘要 · Abstract (English)
In the oil industry, undesirable events in oil wells can cause economic losses, environmental accidents, and human casualties. Solutions based on Artificial Intelligence and Machine Learning for Early Detection of such events have proven valuable for diverse applications across industries. In 2019, recognizing the importance and the lack of public datasets related to undesirable events in oil wells, Petrobras developed and publicly released the first version of the 3W Dataset, which is essentially a set of Multivariate Time Series labeled by experts. Since then, the 3W Dataset has been developed collaboratively and has become a foundational reference for numerous works in the field. This data article describes the current publicly available version of the 3W Dataset, which contains structural modifications and additional labeled data. The detailed description provided encourages and supports the 3W community and new 3W users to improve previous published results and to develop new robust methodologies, digital products and services capable of detecting undesirable events in oil wells with enough anticipation to enable corrective or mitigating actions.
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