Drilling of oil and gas wells will be improved with the help of an IT support system

Innopolis University developers have set up an IT support system for drilling processes in

real time based on information transfervia specialized communication channels, its display in the user interface for operational control and decision-making by the engineer. As part of the project, specialists ensured the continuous accumulation of an array of information for analysis using machine learning.

Evgeniy Danilov, Director of the Oil and Gas Centertechnologies from Innopolis University: “Drilling is an expensive process, which is further complicated by the need to build high-tech engineering facilities - deep wells. Expert control and decision-making still remain a priority in the construction of wells, so a drilling support engineer needs as much and as high-quality understanding of the subsoil as possible, especially in conditions of drilling complex well trajectories - horizontal, multi-lateral.”

When wells have already been drilled in the production areait is important to determine the risk of newly drilled wells crossing existing underground infrastructure. Innopolis University specialists have developed statistical algorithms for accounting for uncertainties, which help the drilling engineer to make an objective decision.

Development of software and hardware systems forthe world's leading oilfield service companies such as Schlumberger, Halliburton, ROGII and others are also involved in drilling robotization. “The uniqueness of our hardware and software complex is the creation of a synergistic IT model, which includes big data, machine learning and engineering experience. All this will make it possible to make objective decisions on drilling at an expert level, ”added Evgeny Danilov

In addition, the specialists of the Center for Oil and Gastechnologies are working to create a digital field, with the help of which it is possible to observe the processes at the wellsite, make quick and effective decisions and minimize the risks of emergencies. Software algorithms for the implementation of such solutions are at the prototyping stage; platform unification is planned by the end of 2025.

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