loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.137.187.233

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic