Enhancing Offshore Drilling Safety: Identification and Integration of Leading Indicators for Loss of Offshore Well Control

Summary In an effort to enhance safety and efficiency in offshore drilling operations, we address critical gaps in the current understanding and application of leading indicators for loss of well control (LOWC) events. Existing indicators often fall short in predicting LOWC events accurately and in a timely manner due to a lack of integration of real-time data, which is crucial for proactive risk management. Our study aims to bridge these gaps by identifying robust leading indicators and incorporating them into an advanced real-time detection and response system. The research began with a comprehensive analysis of existing LOWC events to identify the key leading indicators. Using advanced data analytics techniques, including data assimilation (DA) and change-point Bayesian networks (CP-BN), we have developed an integrated real-time kick detection and state estimation framework and evaluated the effectiveness and reliability of the identified indicators. This evaluation was supported by extensive testing, including real-time and offline simulations, as well as full-scale experiments where operational parameters were adjusted to replicate various drilling scenarios. Several key leading indicators were identified based on this study, such as abnormal pressure fluctuations, unexpected changes in drilling fluid properties, and precursors detected through distributed fiber-optic sensing (DFOS) data. We conducted a series of surveys and engaged with industry professionals to gather practical insights and feedback to further validate these findings and enhance the applicability of our results. The integration of these indicators into the kick detection and state estimation framework significantly enhanced early detection capabilities. Experimental testing and assessment of the integrated framework in a full-scale system have demonstrated its operational viability, markedly improved preventive safety measures, and contributed to the reduction of overall system risks. This research represents an advancement in operational safety for offshore drilling operations, facilitating the development of proactive approaches that enable drilling crews to respond more effectively to emerging threats and reduce the likelihood of LOWC incidents. It serves as a foundational study for continued safety enhancements in the oil and gas industry, encouraging the ongoing refinement of safety practices.

Authors

Institutions

Publication Details

Journal
SPE Journal
Published
2025-10-31
DOI
https://doi.org/10.2118/231169-pa
Primary Topic
Drilling and Well Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Offshore Drilling Safety: Identification and Integration of Leading Indicators for Loss of Offshore Well Control

Yuanhang Chen, Oscar Gabaldon, Jyotsna Sharma, Chen Wei et al.
SPE Journal
Drilling and Well Engineering
article

Enhancing Offshore Drilling Safety: Identification and Integration of Leading Indicators for Loss of Offshore Well Control

Yuanhang Chen, Oscar Gabaldon, Jyotsna Sharma, Chen Wei, Pradeepkumar Ashok, Michael Yi, Omar Montes
article en

Abstract

Summary In an effort to enhance safety and efficiency in offshore drilling operations, we address critical gaps in the current understanding and application of leading indicators for loss of well control (LOWC) events. Existing indicators often fall short in predicting LOWC events accurately and in a timely manner due to a lack of integration of real-time data, which is crucial for proactive risk management. Our study aims to bridge these gaps by identifying robust leading indicators and incorporating them into an advanced real-time detection and response system. The research began with a comprehensive analysis of existing LOWC events to identify the key leading indicators. Using advanced data analytics techniques, including data assimilation (DA) and change-point Bayesian networks (CP-BN), we have developed an integrated real-time kick detection and state estimation framework and evaluated the effectiveness and reliability of the identified indicators. This evaluation was supported by extensive testing, including real-time and offline simulations, as well as full-scale experiments where operational parameters were adjusted to replicate various drilling scenarios. Several key leading indicators were identified based on this study, such as abnormal pressure fluctuations, unexpected changes in drilling fluid properties, and precursors detected through distributed fiber-optic sensing (DFOS) data. We conducted a series of surveys and engaged with industry professionals to gather practical insights and feedback to further validate these findings and enhance the applicability of our results. The integration of these indicators into the kick detection and state estimation framework significantly enhanced early detection capabilities. Experimental testing and assessment of the integrated framework in a full-scale system have demonstrated its operational viability, markedly improved preventive safety measures, and contributed to the reduction of overall system risks. This research represents an advancement in operational safety for offshore drilling operations, facilitating the development of proactive approaches that enable drilling crews to respond more effectively to emerging threats and reduce the likelihood of LOWC incidents. It serves as a foundational study for continued safety enhancements in the oil and gas industry, encouraging the ongoing refinement of safety practices.

SPE JournalVol. 31(01)
Louisiana State University (US), PharmAccess Group (NL)
Openalex Percentile: Top 60%
Drilling and Well Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.