About the client
Multinational energy company that operates in all areas of the oil and gas industry, from exploration and production to refining, distribution, and marketing. The company operates in over 70 countries and is committed to providing reliable and sustainable energy solutions, focusing not only on traditional fossil fuels but also on renewable and low-carbon alternatives.
Interpretation of 3D Sonar data using Machine Learning to detect leakage through the safety valves Application of ML and AI techniques to detect gas leakage on surface facilities using thermal sensor data Detecting early signs of leakage in Subsurface Safety Valves through analysis of fluid flow and pressure data before a possible failure event Reducing operating costs by lowering the physical inspection rate of subsea and on-surface safety valves
Early Leakage Detection: Utilized AI and machine learning for early identification of leaks in Subsurface Safety Valves, mitigating equipment failure risk and reducing physical inspections.
Operational Efficiency & Revenue Enhancement: Minimized non-productive time through predictive modeling and data analysis, optimizing operational efficiency and increasing revenue.
Environmental Conservation Commitment: Employed innovative solutions to swiftly identify and address potential leaks, safeguarding marine ecosystems and minimizing environmental impact.
3D Volumetric Sonar: Employed to capture comprehensive data of subsea conditions and identify early signs of leaks.
Machine Learning & AI: Utilized for interpreting intricate 3D sonar data, enabling the development of predictive models for potential safety valve failures.
Data Analysis: Conducted comprehensive fluid flow and pressure data analysis to engineer indicators for the predictive model, enhancing the accuracy of early leakage detection.
Reduced Physical Inspections: The advanced system significantly lowered the frequency of physical inspections, contributing to operational efficiency and cost reduction.
Reduced risk of safety equipment failure due to early detection of such possibilities. Increased revenue due to a decline in Non-Productive Time and operational costs. Decreased impact on the environment and the animal life at sea.