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Authors: Kishansingh Rajput and Guoning Chen

Affiliation: University of Houston, Houston, U.S.A.

Keyword(s): Drilling Well Logs, Anomaly Detection, Casing Wear, Visual Analysis.

Abstract: In oil and gas industries, to monitor the drilling well status and take actions when needed to prevent damage, different logs are recorded and compared with the reference logs of the nearby existing wells. The deviation of the log of the current well from the majority of the reference logs may indicate potential issues of drilling. Currently, the standard methods used in the industry are line/scatter plots. Due to limitations such as clutter and lack of quantitative information, these plots are not effective. In this paper, a probabilistic envelope based technique is proposed for the visualization and anomaly detection of drilling data. This technique provides quantitative information, is able to avoid the outliers in the reference data and works well even with a large number of reference sequences. This technique is applied to the detection of anomalies from drilling well logs to demonstrate its effectiveness. It is also adapted to the detection of over/under gauge during drilling.

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Paper citation in several formats:
Rajput, K. and Chen, G. (2022). Probabilistic Envelope based Visualization for Monitoring Drilling Well Data Logging. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 51-62. DOI: 10.5220/0010774900003124

@conference{ivapp22,
author={Kishansingh Rajput. and Guoning Chen.},
title={Probabilistic Envelope based Visualization for Monitoring Drilling Well Data Logging},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP},
year={2022},
pages={51-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010774900003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP
TI - Probabilistic Envelope based Visualization for Monitoring Drilling Well Data Logging
SN - 978-989-758-555-5
IS - 2184-4321
AU - Rajput, K.
AU - Chen, G.
PY - 2022
SP - 51
EP - 62
DO - 10.5220/0010774900003124
PB - SciTePress

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