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Amid global oil volatility: MIT-WPU develops AI models to boost recovery, forecast output
In another development, Prof Samarth Patwardhan and his PhD student Dr Soumitra Nande developed a deep learning model capable of identifying carbonate reservoir rocks with 97 per cent accuracy
The model, trained using data from numerous oil-producing fields worldwide, achieved an accuracy of 91 per cent in predicting the most effective recovery methods.
Amid volatility in global energy markets driven by geopolitical tensions and disruptions in oil supply chains, researchers at MIT World Peace University (MIT-WPU), Pune, have developed advanced artificial intelligence (AI) and machine learning (ML) models to improve oil recovery from mature reservoirs and forecast production more accurately.
Researchers from the Department of Petroleum Engineering at MIT-WPU are applying AI to address complex challenges in petroleum reservoir management. A team led by Dr Rajib Kumar Sinharay, Professor in the department, along with his PhD student Dr Hrishikesh K Chavan, has developed a machine learning model capable of identifying the most suitable Enhanced Oil Recovery (EOR) techniques for complex reservoirs.
The model, trained using data from numerous oil-producing fields worldwide, achieved an accuracy of 91 per cent in predicting the most effective recovery methods. The findings were published in the international journal Petroleum Science and Technology. The AI-based model significantly reduces the time required to evaluate oil recovery strategies — from several months using conventional methods to just a few hours.
In a statement, Dr Sinharay said, “Artificial intelligence has the potential to transform reservoir management in the oil and gas industry. Our research focuses on developing data-driven tools that can help operators select the most effective recovery techniques and make more accurate production forecasts, particularly for mature oil fields.”
In another development, Prof Samarth Patwardhan and his PhD student Dr Soumitra Nande developed a deep learning model capable of identifying carbonate reservoir rocks with 97 per cent accuracy. These formations are similar to those found in Bombay High, India’s largest offshore oil field. Their research was published in the Arabian Journal for Science and Engineering in 2025.
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