Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review

Fluid loss to subsurface formations is a challenging aspect during drilling operations in petroleum industry. Several other drilling issues such as fluid influx and pipe sticking can be triggered in such scenarios, posturing a significant risk to rig personnel, environment, and economical drilling....

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Main Authors: Krishna, S., Ridha, S., Vasant, P., Ilyas, S.U., Sophian, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29724 /
Published: Elsevier B.V. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090552029&doi=10.1016%2fj.petrol.2020.107818&partnerID=40&md5=89d4074774724b5f9e8a354d0a807818
http://eprints.utp.edu.my/29724/
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spelling utp-eprints.297242022-03-25T02:45:34Z Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review Krishna, S. Ridha, S. Vasant, P. Ilyas, S.U. Sophian, A. Fluid loss to subsurface formations is a challenging aspect during drilling operations in petroleum industry. Several other drilling issues such as fluid influx and pipe sticking can be triggered in such scenarios, posturing a significant risk to rig personnel, environment, and economical drilling. Therefore, prediction and early detection of lost circulation events are required for safe and economic drilling operation. Several theoretical studies have been performed to detect and predict fluid loss event during hydrocarbon extraction. This paper reviews the existing conventional and intelligent models developed for early detection and prediction of lost circulation events. These predictive and detecting models comprise of Artificial Intelligence (AI) algorithms that require improvements for data reduction, universal prediction and compatibility. The review also covers several sensor-based techniques, different geostatistical-based models and Pressure-While-Drilling (PWD) tools for their applications in early loss circulation detection. In addition, loss circulation zones types, severity level, scenario and common preventive measures are also included in this review. This study aims to provide a systematic review of the published literature from the last forty years on the developed conventional and intelligent models for detection and prediction of fluid loss events and emphasizes on increasing AI involvement for precise results. © 2020 Elsevier B.V. Elsevier B.V. 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090552029&doi=10.1016%2fj.petrol.2020.107818&partnerID=40&md5=89d4074774724b5f9e8a354d0a807818 Krishna, S. and Ridha, S. and Vasant, P. and Ilyas, S.U. and Sophian, A. (2020) Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review. Journal of Petroleum Science and Engineering, 195 . http://eprints.utp.edu.my/29724/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Fluid loss to subsurface formations is a challenging aspect during drilling operations in petroleum industry. Several other drilling issues such as fluid influx and pipe sticking can be triggered in such scenarios, posturing a significant risk to rig personnel, environment, and economical drilling. Therefore, prediction and early detection of lost circulation events are required for safe and economic drilling operation. Several theoretical studies have been performed to detect and predict fluid loss event during hydrocarbon extraction. This paper reviews the existing conventional and intelligent models developed for early detection and prediction of lost circulation events. These predictive and detecting models comprise of Artificial Intelligence (AI) algorithms that require improvements for data reduction, universal prediction and compatibility. The review also covers several sensor-based techniques, different geostatistical-based models and Pressure-While-Drilling (PWD) tools for their applications in early loss circulation detection. In addition, loss circulation zones types, severity level, scenario and common preventive measures are also included in this review. This study aims to provide a systematic review of the published literature from the last forty years on the developed conventional and intelligent models for detection and prediction of fluid loss events and emphasizes on increasing AI involvement for precise results. © 2020 Elsevier B.V.
format Article
author Krishna, S.
Ridha, S.
Vasant, P.
Ilyas, S.U.
Sophian, A.
spellingShingle Krishna, S.
Ridha, S.
Vasant, P.
Ilyas, S.U.
Sophian, A.
Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
author_sort Krishna, S.
title Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
title_short Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
title_full Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
title_fullStr Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
title_full_unstemmed Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
title_sort conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: a comprehensive review
publisher Elsevier B.V.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090552029&doi=10.1016%2fj.petrol.2020.107818&partnerID=40&md5=89d4074774724b5f9e8a354d0a807818
http://eprints.utp.edu.my/29724/
_version_ 1741197289646456832
score 11.62408

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