Artificial Intelligence in Oil and Gas Industry
Over the past couple of years, the digital revolution in the oil and gas industry has become the main topic of discussion for specialized media, industry experts, and occasionally large-scale forums.
But is it a revolution, or is it evolutionary development? After all, the use of computer technology, for example, to solve such a problem as reservoir modelling, began back in the 60s of the last century, and in the early 1970s, the use of large workstations for processing field data made it possible to increase production by 1%.
In the 1990s, the industry, having mastered the creation of computer 3D seismic models, reduced the cost of finding new deposits by an average of 40%, as a result of which the volume of proven reserves increased by 2.5 times.
Now, the global oil and gas industry, as always one of the first to use the latest technical achievements, has taken up the banner of artificial intelligence. The oil and gas industry has changed rapidly in recent years, with new technologies adopted by the energy sector to meet the challenges of a digital economic landscape, while trying to fully implement artificial intelligence in oil and gas industry.
Most common AI Use Cases
Detection with Drones
Due to the amount of drilling done in an area, automation at scale is the ideal solution, as well as ai in oil and gas. So, the instant there were any problems, the AI system would quickly kick in, identifying and resolving the defect that way, as the damage appears in the gas or oil drilling, they will be registered in the AI memory, and if it happens again, it will take little time to solve.
Another option of artificial intelligence is learning everything about the location of the well, be it gas or oil. So, the more time it spends installed in a location, helping to solve problems, the more able it will be to repair the damage without the help of an on-site engineer. However, the implementation tests are still in their initial stage. The results are quite positive after all; optimizing production time, keeping machines active in oil drilling, with a long term for maintenance, is something beneficial and generates higher income throughout the month. We just hope that ai trends in oil and gas will come to life.
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Our Project Approach
The AI project lifecycle has been adopted from an existing standard used in software development. Also, the approach takes into account the scientific challenges inherent in machine learning projects involving software development processes. The approach aims to ensure the quality of development. Each phase has its own goals and quality assurance criteria that must be met before the next stage can be initiated.