Name
Abstract | ||
|---|---|---|
One of the main challenges in the construction of oil and gas wells is the need to detect and avoid abnormal situations, which can lead to accidents. Accidents have some indicators that help to find them during the drilling process. In this article, we present a data-driven model trained on historical data from drilling accidents that can detect different types of accidents using real-time signals. The results show that using the time-series comparison, based on aggregated statistics and gradient boosting classification, it is possible to detect an anomaly and identify its type by comparing current measurements while drilling with the stored ones from the database of accidents. |
Year | Venue | DocType |
|---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.02667 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ekaterina Gurina | 1 | 0 | 0.34 |
| Nikita Klyuchnikov | 2 | 0 | 1.35 |
| Alexey Zaytsev | 3 | 0 | 2.37 |
| Evgenya Romanenkova | 4 | 0 | 1.01 |
| Ksenia Antipova | 5 | 0 | 1.35 |
| Igor Simon | 6 | 0 | 1.35 |
| Victor Makarov | 7 | 0 | 0.68 |
| Dmitry Koroteev | 8 | 2 | 3.45 |