Page 7 - IJOCTA-15-2

An International Journal of Optimization and Control: Theories & Applications
ISSN: 2146-0957 eISSN: 2146-5703
Vol.15, No.2, pp.202-214 (2025)
https://doi.org/10.36922/ijocta.8524

RESEARCH ARTICLE

Artificial intelligence-assisted station keeping for improved drillship
operations

1*
1*
Mahalakshmi Perala , Srinivasan Chandrasekaran , and Ermina Begovic 2
1 Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
2 Department of Industrial Engineering, University of Naples Federico II, Via Claudio, Napoli, Italy
oe23d200@smail.iitm.ac.in, drsekaran@iitm.ac.in, begovic@unina.it

ARTICLE INFO ABSTRACT

Article History:
Received: January 13, 2025 The rising global demand for oil has driven the offshore industry toward deep
1st revised: February 10, 2025 and ultra-deepwater exploration. Drillships are critical in these operations due
2nd revised: February 17, 2025 to their high mobility and adaptability to challenging environments. Station-
Accepted: February 25, 2025 keeping is paramount for safe operations, as drifting beyond thresholds can re-
Published Online: March 19, 2025 sult in severe economic losses and environmental disasters. This study presents
a novel approach to drillship station-keeping by leveraging artificial intelligence
Keywords:
(AI) to locally control the dynamic positioning (DP) system, thereby elim-
Artificial intelligence
inating reliance on global positioning systems or internet-based systems. A
Drillship
numerical model of a drillship was developed, and simulations across multiple
Dynamic positioning system
sea states generated a comprehensive database to train an AI controller. The
Inertial navigation system
system focuses on key degrees of freedom: surge, sway, and yaw. Positional
changes detected by the onboard inertial navigation system are analyzed to
calculate displacement, representing the vessel’s response to external forces.
The trained AI matches these responses to database entries, calculates the re-
quired thrust force, and applies it through DP thrusters to restore the vessel’s
position. The results showed that the AI controller achieves high precision in
station-keeping across various sea states, confirming its robustness and relia-
bility. The key novelty of this method lies in its onboard, localized control
system, which enhances operational independence and safety by eliminating
external dependencies while significantly reducing the risk of positional loss in
ultra-deepwater environments. By combining advanced numerical simulations
with AI tools, this study introduces an innovative, safer, and more efficient
solution for maintaining drillship stability in demanding marine conditions.

1. Introduction primary role is exploratory drilling, supported by
robust rigs that ensure reliability and efficiency
Drillships are specialized vessels engineered for in these extreme conditions. A distinctive feature
offshore drilling operations. These advanced ships of drillships is the moonpool, a vertical aperture
are equipped with state-of-the-art dynamic posi- through the hull designed to accommodate the
tioning systems to maintain precise locations over passage of the drill string during operations. 1
deepwater drilling sites. Self-propelled and capa- When a drillship is actively engaged in
ble of achieving speeds of up to 12 knots, drillships drilling, it is designed to remain stationary. Un-
are tailored for oil and gas exploration in some of der operational conditions, water ingression over
the most challenging marine environments, often the moonpool edges (green water) disrupts its
1
operating at depths exceeding 10,000 feet. Their functions, leading to downtime. Analyzing the
*Corresponding Author
202