Summary: Promotes development of ML algorithms for early detection and classification of undesirable events in offshore oil wells.
| Parameter | Value |
|---|---|
| Name | 3W |
| Labeled | Yes |
| Time Series | Yes |
| Simulation | Both |
| Missing Values | NIA |
| Dataset Characteristics | Multivariate, Time-Series |
| Feature Type | Real |
| Associated Tasks | Regression |
| Number of Instances | N/A |
| Number of Features | N/A |
| Date Donated | 2022-04-06 |
| Source | GitHub |
This is the first repository published by Petrobras on GitHub. It supports the 3W Project, which aims to promote experimentation and development of Machine Learning-based approaches and algorithms for specific problems related to detection and classification of undesirable events that occur in offshore oil wells.
The 3W Project is based on the 3W Dataset, a database described in this paper, and on the 3W Toolkit, a software package that promotes experimentation with the 3W Dataset for specific problems. The name 3W was chosen because this dataset is composed of instances from 3 different sources and which contain undesirable events that occur in oil Wells.
Timely detection of undesirable events in oil wells can help prevent production losses, reduce maintenance costs, environmental accidents, and human casualties. Losses related to this type of events can reach 5% of production in certain scenarios, especially in areas such as Flow Assurance and Artificial Lifting Methods. In terms of maintenance, the cost of a maritime probe, required to perform various types of operations, can exceed US $500,000 per day.
Oil and Gas, Real events, Fault detection, Multivariate data, Sensor data, Time-series analysis, Oil wells, Machine learning benchmark