Artificial intelligence technology is changing the rules of oil extraction with 90% accuracy

The artificial intelligence revolution in the energy and oil extraction sector

The global energy sector is undergoing a radical transformation thanks to rapid advancements in digital technology, with artificial intelligence becoming a crucial tool for improving the efficiency of industrial processes. In this context, researchers at the Moscow Institute of Physics and Technology (MIPT) have developed a new molecular model based on machine learning techniques, aiming to improve the accuracy of oil characterization during extraction processes by up to approximately 90%, representing a significant leap forward for the oil and gas industry.

Historical background and challenges of traditional methods

Historically, oil companies have relied on traditional methods and complex laboratory tests to analyze fluid properties and understand the behavior of oil, gas, and water within shale reservoirs. As Nikolay Kondratyuk, Executive Director of the Institute's Center for Computational Physics, explained, these traditional methods were extremely time-consuming to determine precise parameters such as water salinity and gas composition in each field. Furthermore, previous models suffered from a significant margin of error, sometimes as high as 40%, and relied on months-long laboratory tests that consumed considerable financial and human resources. This long-standing challenge has made it essential to seek advanced computational solutions that reduce costs and accelerate decision-making.

The mechanism of the new model and the technologies used

The innovative model focuses primarily on calculating the surface tension between fluids within porous rocks, a crucial factor in improving oil extraction efficiency and accuracy. The new system relies on high-performance computing that considers multiple and complex physical and chemical factors, such as temperature, pressure, oil and gas composition, and dissolved salts. This comprehensive data integration has contributed to achieving a 90% accuracy rate in predictions using machine learning algorithms, significantly streamlining traditional steps.

Expected impact: locally, regionally, and internationally

This technological development is of paramount importance on several levels. Internationally, improving extraction efficiency contributes to meeting the growing global demand for energy while reducing operating costs. Regionally, and particularly in oil-rich countries, adopting these technologies enhances the sustainability of oil fields and extends their lifespan. Researcher Ilya Kopanichuk explained that the prototypes have successfully passed testing, noting that there are future plans to expand their application to include heavy oil and to study fluid behavior in nanostructures, thus opening new avenues for dealing with complex oil fields.

Environmental dimensions and applications of carbon dioxide injection

The benefits of this model extend beyond the economic sphere to include environmental dimensions. This technology is particularly important in applications such as injecting carbon dioxide into rock formations to increase extraction efficiency, a process known as enhanced oil recovery (EOR). This process not only helps extract additional quantities of oil but also contributes to carbon capture and storage underground, a crucial step in global efforts to reduce greenhouse gas emissions and combat climate change.