Source ForPressRelease.com

At a time when global energy markets are witnessing volatility due to 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 that can help improve oil recovery from mature reservoirs and accurately forecast future production. This research could play an important role in strengthening India’s energy security by enabling more efficient extraction of oil from existing fields and reducing dependence on crude oil imports.



India’s rapidly expanding economy continues to drive rising energy demand. Oil and gas account for nearly 32–37% of the country’s total energy consumption, and India spent an estimated USD 161 billion on crude oil imports, according to government data. Increasing domestic production from existing oil fields has therefore become a strategic priority, particularly at a time when global oil markets are influenced by geopolitical tensions and supply disruptions.



Researchers from the Department of Petroleum Engineering at MIT-WPU, the only dedicated upstream oil and gas academic department in Maharashtra, are applying artificial intelligence to address complex challenges in petroleum reservoir management.



A research team led by Dr. Rajib Kumar Sinharay, Professor in the Department of Petroleum Engineering, MIT WPU, 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 was trained using data from numerous oil-producing fields worldwide and achieved an accuracy of 91% in predicting the most effective recovery methods.



Their 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.



Dr. Rajib Kumar 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 breakthrough, Prof. Samarth Patwardhan and his PhD student Dr. Soumitra Nande developed a deep learning model capable of identifying carbonate reservoir rocks with 97?curacy. These rock 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.



The MIT-WPU research team has also developed a machine learning model for forecasting oil production in mature oil fields, achieving 92?curacy (R² score) when tested using real field data from an Indian onshore reservoir. Reliable production forecasting is critical for the petroleum industry, as it influences investment decisions, reservoir management strategies, and long-term supply planning. The research was published in the internationally reputed journal Physics of Fluids.



In addition, the team has developed an AI-based model for optimizing oil production tubing design, which helps determine the appropriate pipe size for efficient oil extraction. This research was presented at the International Conference on Computational Science and Applications and later published in Springer Nature’s Algorithms for Intelligent Systems series. The researchers have also secured a patent for this technology.



Currently, the MIT-WPU team is working on identifying “sweet spots” in unconventional hydrocarbon reservoirs and developing sustainable drilling fluids suitable for high-temperature and high-pressure environments.



These innovations highlight the growing role of AI-driven research in improving oil recovery, increasing efficiency in mature fields, and supporting India’s efforts to strengthen domestic energy production in an increasingly uncertain global energy landscape.