MIT-WPU Develops AI Models to Boost Oil Recovery

24 Mar 2026 CW Team

With oil and gas contributing nearly 32–37 per cent of India’s energy consumption and crude imports estimated at USD 161 billion, improving domestic production has become a strategic priority. Researchers from the Department of Petroleum Engineering at MIT-WPU are applying AI to address complex challenges in reservoir management.

A research team led by Dr Rajib Kumar Sinharay, along with Dr Hrishikesh K Chavan, has developed a machine learning model that identifies the most suitable Enhanced Oil Recovery (EOR) techniques for complex reservoirs. Trained on global field data, the model achieved 91 per cent accuracy and significantly reduced evaluation time from months to hours. The findings were published in Petroleum Science and Technology.

In another development, Prof Samarth Patwardhan and Dr Soumitra Nande created a deep learning model capable of identifying carbonate reservoir rocks with 97 per cent accuracy, similar to formations in Bombay High. This research appeared in the Arabian Journal for Science and Engineering in 2025.

The team has also developed a machine learning model for forecasting oil production in mature fields, achieving 92 per cent accuracy when tested on Indian onshore reservoir data. The research, published in Physics of Fluids, highlights the importance of accurate forecasting in investment planning and reservoir management.

Additionally, an AI-based model for optimising oil production tubing design has been developed, enabling efficient pipe size selection for extraction. This work was presented at the International Conference on Computational Science and Applications and later published in Springer Nature’s Algorithms for Intelligent Systems series. The team has secured a patent for this innovation.

Ongoing research includes identifying ‘sweet spots’ in unconventional reservoirs and developing sustainable drilling fluids for high-temperature and high-pressure conditions. These advancements underscore the growing role of AI in improving oil recovery and strengthening India’s domestic energy capabilities in an uncertain global landscape.

Amid global energy market volatility driven by geopolitical tensions and oil supply disruptions, researchers at MIT World Peace University (MIT-WPU), Pune, have developed advanced artificial intelligence (AI) and machine learning (ML) models to enhance oil recovery from mature reservoirs and improve production forecasting. The research is expected to support India’s energy security by increasing efficiency in existing oil fields and reducing reliance on crude oil imports.With oil and gas contributing nearly 32–37 per cent of India’s energy consumption and crude imports estimated at USD 161 billion, improving domestic production has become a strategic priority. Researchers from the Department of Petroleum Engineering at MIT-WPU are applying AI to address complex challenges in reservoir management.A research team led by Dr Rajib Kumar Sinharay, along with Dr Hrishikesh K Chavan, has developed a machine learning model that identifies the most suitable Enhanced Oil Recovery (EOR) techniques for complex reservoirs. Trained on global field data, the model achieved 91 per cent accuracy and significantly reduced evaluation time from months to hours. The findings were published in Petroleum Science and Technology.In another development, Prof Samarth Patwardhan and Dr Soumitra Nande created a deep learning model capable of identifying carbonate reservoir rocks with 97 per cent accuracy, similar to formations in Bombay High. This research appeared in the Arabian Journal for Science and Engineering in 2025.The team has also developed a machine learning model for forecasting oil production in mature fields, achieving 92 per cent accuracy when tested on Indian onshore reservoir data. The research, published in Physics of Fluids, highlights the importance of accurate forecasting in investment planning and reservoir management.Additionally, an AI-based model for optimising oil production tubing design has been developed, enabling efficient pipe size selection for extraction. This work was presented at the International Conference on Computational Science and Applications and later published in Springer Nature’s Algorithms for Intelligent Systems series. The team has secured a patent for this innovation.Ongoing research includes identifying ‘sweet spots’ in unconventional reservoirs and developing sustainable drilling fluids for high-temperature and high-pressure conditions. These advancements underscore the growing role of AI in improving oil recovery and strengthening India’s domestic energy capabilities in an uncertain global landscape.

Infrastructure Energy

30 May 2026

Sigenergy Launches AI Agent For Energy Systems

Sigenergy has introduced SigenAgent, described as the energy industry’s first all-domain AI agent, during its ‘AI in All’ event. The platform is designed to move solar-and-storage systems from reactive hardware to autonomous, goal-driven energy systems for households and businesses.SigenAgent works through a continuous loop of perception, reasoning and action. It analyses real-time factors such as weather patterns, electricity prices and grid conditions to determine and execute the most efficient operating strategy. Users can set broad goals, such as reducing electricity bills or securin..

Equipment

30 May 2026

XCMG Festival Records US$1 Billion Orders

XCMG concluded its eighth International Customer Festival in Xuzhou, drawing more than 2,500 customers and business partners from global markets. The event recorded signed orders exceeding US$1 billion, marking a new high for the festival and reflecting continued international confidence in XCMG’s products, technologies and integrated solutions.The orders covered key segments including lifting machinery, earthmoving machinery, mining machinery, road machinery and aerial work platforms. Green energy and smart mining products saw strong traction, with XCMG’s ‘Qingshan Green’ low-carbon r..

Infrastructure Energy

30 May 2026

Haver & Boecker Expands Dealer Network

Haver & Boecker Niagara has appointed Boreal Mining Equipment as its newest authorised dealer, strengthening its regional presence and customer support across the mining and aggregates industries.Under the agreement, Boreal Mining Equipment will represent Haver & Boecker Niagara’s full range of screening technologies, including vibrating screens, engineered processing plants, condition monitoring systems and aftermarket services.The partnership will give customers improved access to high-performance equipment, local technical expertise and faster service support. Steve Dery, Vice Pre..