The Indian Institute of Technology Madras (IIT-Madras) has developed an indigenous lifecycle management system for the government-owned Oil and Natural Gas Corporation (ONGC). It will reduce the cost of maintenance and rehabilitation of offshore oil platforms.
The system, SIMS (Structural Integrity Management System), is a multi-parameter optimisation and decision-making tool. It was developed by the Offshore Structures Group from the Department of Ocean Engineering, IIT-Madras. It has a large database management system that houses crucial and accessible structural/design-related data.
ONGC spends a lot of effort and time on the underwater inspection, repair, and rehabilitation of oil platforms, which were constructed decades ago. A major risk associated with offshore platforms is joint cracks that result from fatigue and corrosion. SIMS provides a way to determine the optimal frequency of health checks and evaluates risks using inspection data, structure characteristics, and information obtained from surveys.
It is crucial to ascertain the structural adequacy of offshore platforms to ensure their extended usage. During their life span, these platforms undergo several structural modifications like the addition of clamp-on conductors, flow lines/risers, and deck extensions. As per a recent revision in API code, a structural integrity management system is mandatory in the management of existing offshore structures.
SIMS will be implemented in ONGC with access to various stakeholders like the Institute of Engineering and Ocean Technology (IEOT), offshore assets, offshore engineering services, and ONGC inspection, maintenance, and repair teams. The system provides a proactive approach to monitoring, evaluating, and assessing structural conditions and establishing a procedure to validate the fitness-for-service of an offshore structure.
An official from IIT-Madras stated that SIMS is an important system because along with the oil that ONGC is drilling, it is also drilling data. Real-time sensors allow for consistent inspection, instead of periodical ones. The Institute will also develop artificial intelligence (AI) and machine learning (ML)-based tools to predict when inspections would be required.
More than 330 offshore platforms operated by ONGC contribute around 70% of crude oil and 78% of Natural Gas production of ONGC’s domestic hydrocarbon supply. The platforms operated have been installed with a design life of 25 years. More than 50% of these structures have outlived their design life. These platforms are required to be in operation for an extended period of production of hydrocarbon and extending their life is more effective than decommissioning them.
The exploration and production (E&P) industry is a technology-intensive sector and ONGC is keen on developing and adopting the latest technologies. Meanwhile, IIT-Madras has been a front runner in the domain of indigenous technology development and has been providing a number of high-tech solutions based on industry and government needs. Earlier this year, IIT-Madras used AI tools to study fuel production from biomass. With increasing environmental concerns associated with petroleum-derived fuels, biomass is a practical solution as a source of energy-dense fuel. In a statement, IIT-Madras explained that the researchers used AI and computer simulation and modelling to understand the concept, which saved time and costs.
The team used a machine-learning method called recurrent neural networks (RNN) to study the reactions that occur during the conversion of biomass into energy-dense syngas (gasification of biomass). As OpenGov Asia reported, the technology is able to predict the composition of the biofuel produced as a function of the time the biomass spends in the reactor. The team used a statistical reactor for accurate data generation, which allows the model to be applied over a wide range of operating conditions. The team believes extracting fuel from biomass could help the country attain fuel self-sufficiency.
