An intelligent algorithm for identifying dropped blocks in wellbores
Real-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions. The cause and type of wellbore instability can be identified by analyzing the dropped blocks brought to the surface by the drilling fluid, enabling preventive measur...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-04-01
|
| Series: | Natural Gas Industry B |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352854025000208 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849713211970945024 |
|---|---|
| author | Qian Wang Zixuan Yang Chenxi Ye Wenbao Zhai Xiao Feng |
| author_facet | Qian Wang Zixuan Yang Chenxi Ye Wenbao Zhai Xiao Feng |
| author_sort | Qian Wang |
| collection | DOAJ |
| description | Real-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions. The cause and type of wellbore instability can be identified by analyzing the dropped blocks brought to the surface by the drilling fluid, enabling preventive measures to be taken. In this study, an image capture system with fully automated sorting and 3D scanning was developed to obtain the complete 3D point cloud data of dropping blocks. The raw data obtained were preprocessed using methods such as format conversion, down sampling, coordinate transformation, statistical filtering, and clustering. Feature extraction algorithms, including the principal component analysis bounding box method, triangular meshing method, triaxial projection method, local curvature method, and model segmentation projection method, were employed, which resulted in the extraction of 32 feature parameters from the point cloud data. An optimal machine learning algorithm was developed by training it with 10 machine learning algorithms and the block data collected in the field. The XGBoost algorithm was then used to optimize the feature parameters and improve the classification model. An intelligent, fully automated feature parameter extraction and classification system was developed and applied to classify the types of falling blocks in 12 sets of drilling field and laboratory experiments and to identify the causes of wellbore instability. An average accuracy of 93.9 % was achieved. This system can thus enable the timely diagnosis and implementation of preventive and control measures for wellbore instability in the field. |
| format | Article |
| id | doaj-art-44b5945e2a214c2e91f72ce8aabf3a52 |
| institution | DOAJ |
| issn | 2352-8540 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Natural Gas Industry B |
| spelling | doaj-art-44b5945e2a214c2e91f72ce8aabf3a522025-08-20T03:14:01ZengKeAi Communications Co., Ltd.Natural Gas Industry B2352-85402025-04-0112218619410.1016/j.ngib.2025.03.003An intelligent algorithm for identifying dropped blocks in wellboresQian Wang0Zixuan Yang1Chenxi Ye2Wenbao Zhai3Xiao Feng4CNPC Engineering Technology R&D Company Limited, Beijing, China; National Engineering Research Center of Oil & Gas Drilling and Completion Technology, Beijing, China; Corresponding author. CNPC Engineering Technology R&D Company Limited, Beijing, China.CNPC Engineering Technology R&D Company Limited, Beijing, China; National Engineering Research Center of Oil & Gas Drilling and Completion Technology, Beijing, ChinaCNPC Engineering Technology R&D Company Limited, Beijing, China; National Engineering Research Center of Oil & Gas Drilling and Completion Technology, Beijing, ChinaCNPC Engineering Technology R&D Company Limited, Beijing, China; National Engineering Research Center of Oil & Gas Drilling and Completion Technology, Beijing, ChinaCNPC Engineering Technology R&D Company Limited, Beijing, China; National Engineering Research Center of Oil & Gas Drilling and Completion Technology, Beijing, ChinaReal-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions. The cause and type of wellbore instability can be identified by analyzing the dropped blocks brought to the surface by the drilling fluid, enabling preventive measures to be taken. In this study, an image capture system with fully automated sorting and 3D scanning was developed to obtain the complete 3D point cloud data of dropping blocks. The raw data obtained were preprocessed using methods such as format conversion, down sampling, coordinate transformation, statistical filtering, and clustering. Feature extraction algorithms, including the principal component analysis bounding box method, triangular meshing method, triaxial projection method, local curvature method, and model segmentation projection method, were employed, which resulted in the extraction of 32 feature parameters from the point cloud data. An optimal machine learning algorithm was developed by training it with 10 machine learning algorithms and the block data collected in the field. The XGBoost algorithm was then used to optimize the feature parameters and improve the classification model. An intelligent, fully automated feature parameter extraction and classification system was developed and applied to classify the types of falling blocks in 12 sets of drilling field and laboratory experiments and to identify the causes of wellbore instability. An average accuracy of 93.9 % was achieved. This system can thus enable the timely diagnosis and implementation of preventive and control measures for wellbore instability in the field.http://www.sciencedirect.com/science/article/pii/S2352854025000208Wellbore instabilityDropped block classification3D scanningPoint cloud dataFeature extractionMachine learning |
| spellingShingle | Qian Wang Zixuan Yang Chenxi Ye Wenbao Zhai Xiao Feng An intelligent algorithm for identifying dropped blocks in wellbores Natural Gas Industry B Wellbore instability Dropped block classification 3D scanning Point cloud data Feature extraction Machine learning |
| title | An intelligent algorithm for identifying dropped blocks in wellbores |
| title_full | An intelligent algorithm for identifying dropped blocks in wellbores |
| title_fullStr | An intelligent algorithm for identifying dropped blocks in wellbores |
| title_full_unstemmed | An intelligent algorithm for identifying dropped blocks in wellbores |
| title_short | An intelligent algorithm for identifying dropped blocks in wellbores |
| title_sort | intelligent algorithm for identifying dropped blocks in wellbores |
| topic | Wellbore instability Dropped block classification 3D scanning Point cloud data Feature extraction Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352854025000208 |
| work_keys_str_mv | AT qianwang anintelligentalgorithmforidentifyingdroppedblocksinwellbores AT zixuanyang anintelligentalgorithmforidentifyingdroppedblocksinwellbores AT chenxiye anintelligentalgorithmforidentifyingdroppedblocksinwellbores AT wenbaozhai anintelligentalgorithmforidentifyingdroppedblocksinwellbores AT xiaofeng anintelligentalgorithmforidentifyingdroppedblocksinwellbores AT qianwang intelligentalgorithmforidentifyingdroppedblocksinwellbores AT zixuanyang intelligentalgorithmforidentifyingdroppedblocksinwellbores AT chenxiye intelligentalgorithmforidentifyingdroppedblocksinwellbores AT wenbaozhai intelligentalgorithmforidentifyingdroppedblocksinwellbores AT xiaofeng intelligentalgorithmforidentifyingdroppedblocksinwellbores |