Subsea Safety Valve Leak Rate and Leak Fluid Identification for Environmental Safety

#Custom software development #Machine Learning Model Development #ML Ops #Dashboards & Analytics #Process and workflow automation #AWS #ML/AI

Project goals

Detect potential leakage and prevent it from happening.

Result

Reduced risk of safety equipment failure.

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.

Project goals

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

Key challenge

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.

Our solutions

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.

Result

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.

Industries

Energy

Launch date

07/1/2023

Technologies

AWS

ML/AI

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Bits Orchestra team are outstanding developers‚Äč. They listen carefully to our business needs and easily turns our business objectives into a well thought out and executed development effort. Roman is very bright and definitely the most capable developer that has worked on our site. He is not only a Kentico expert but has successfully tackled other complicated development assignments demonstrating expertise in both front and backend development. Roman takes initiative to suggest enhancements that make site maintenance easier while improving the customer experience. The team is very responsive to our work requests and has great follow up. They have also worked very business partners and this has reflected positively on our company. Roman is a true partner for us and a tremendous asset to our organization. We will continue to work with them and would highly recommend Roman and his team for your development needs. He and his team will exceed your expectations!
 Alan Lehmann
Alan Lehmann
President at In energy sector

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