Project Abstract

AbstractResearch into rig moves utilizing Artificial Intelligence (AI) aims to refine the logistical procedures involved in relocating drilling rigs within the oil and gas sector. By harnessing advanced AI algorithms, machine learning methods, and data analytics, this interdisciplinary approach seeks to streamline efficiency, safety, and cost-effectiveness throughout rig relocations. Central to this endeavor is the gathering, consolidation, and examination of diverse datasets relevant to rig mobilization, encompassing factors such as weather conditions, ocean currents, vessel capabilities, and regulatory prerequisites. Through sophisticated AI algorithms, these datasets undergo analysis to derive actionable insights and predictive models, empowering informed decision-making across the rig move lifecycle.A key objective of this research is to optimize route planning and vessel scheduling, minimizing downtime and maximizing operational output. AI-driven predictive modeling facilitates the identification of optimal travel routes, factoring in variables such as weather patterns, sea conditions, and vessel performance attributes. By preemptively addressing potential hurdles and optimizing route selection, AI-driven solutions mitigate risks associated with delays, accidents, and unforeseen circumstances, thereby bolstering overall operational efficiency.Moreover, this research contributes significantly to elevating safety standards and ensuring regulatory compliance. By scrutinizing historical data and real-time environmental conditions, AI algorithms can proactively pinpoint potential safety hazards and propose preemptive measures to mitigate risks. Additionally, AI-powered simulations and scenario modeling empower stakeholders to evaluate the ramifications of regulatory alterations and operational decisions on safety protocols, ensuring alignment with industry standards and best practices.The focal challenge addressed in this endeavor revolves around enhancing the efficiency and safety of rig moves within the oil and gas domain through the integration of AI technologies. Rig moves entail intricate operations demanding meticulous planning, coordination, and execution. The impetus for this project stems from the industry’s continual drive to optimize operations, minimizing downtime, curbing costs, and mitigating risks associated with rig moves. This research endeavors to optimize route planning, institute predictive maintenance for equipment, fortify safety protocols, automate operations, and enable real-time performance monitoring through the utilization of AI technologies. By pursuing these objectives, substantial progress is envisaged in the efficiency, safety, and sustainability of rig move operations within the oil and gas industries, fostering value creation across the sector.Keywords: Rig move, Artificial Intelligence, Route optimization, Predictive maintenance, Safety enhancement

Keywords: Rig Move, Artificial Intelligence, Route optimization

 Conference Details

Session: Presentation Stream 8 at Presentation Slot 5

Location: GH037 at Tuesday 7th 13:30 – 17:00

Markers: Galileo Sator (GTA), Ulrich Berger

Course: MSc Data Science, Masters PG

Future Plans: I’m looking for work