AI-Driven geophysical technology
RC GeoVision is innovative AI-driven geophysical technology which enables one to find all kind of mineral deposits first remotely by the utilization of analog satellite images. Further detailed investigation of found mineral of interest is carried out on-site by means of special high-tech compact mobile equipment developed by us.
RC GeoVision technology underwent public testing in Utah (USA) during oil and gas exploration in challenging geological conditions. The results fully confirmed the presence of deposits with high efficiency indicators.
Technology patents: Germany, Ukraine. Tested in the USA, Spain, Africa, China, the Persian Gulf countries and Asia.
- Application territory – without limitations land or shelf
- Survey area – up to 50 thousand square km per month
- Survey depth – up to 5 km
- Sought-after minerals – oil, gas, gold, lithium and other metals, rare earth, water
- Potency – more than 90%
- Search duration – from 1 to 3 months
- Environmental safety – the method is completely safe for humans and the environment
Hydrocarbons
Crude oil, natural gas, and gas condensate (with pressure in caps indication).
Freshwater
Underground freshwater aquifers and streams, number of horizons.
Minerals
Liquid and solid minerals, including rare-earth and polymetallic ores.
Geothermal Energy Sources
Underground geothermal sources (rocks and springs).
- Map of the area with the boundaries and contours of the identified deposits plotted on it
- Coordinates of ground contours of deposits
- The quantity of horizons in the deposits and the depth of each horizon
- The most perspective areas for drilling
- Powers of horizons and forecast reserves.
The energy transition requires more intelligent and efficient ways to assess subsurface resources, and artificial intelligence (AI) is transforming the methods used to analyse and interpret geophysical data, particularly seismic and well logs.
Traditional methods often struggle with the sheer volume and complexity of geophysical datasets, leading to gaps in subsurface prediction.AI bridges this gap by automating feature detection, improving lithological classification, and enhancing real-time interpretation of rock properties. For seismic data, AI-based models can identify subtle geological features that are critical for optimising geothermal reservoirs or CO₂ storage sites.
In the case of well logs, AI algorithms provide faster and more accurate predictions of reservoir characteristics, even in complex carbonate or fractured systems. By combining geophysical data with machine learning, we can significantly reduce exploration risks, improve reservoir management, and ensure more reliable implementation of energy transition technologies in various geological conditions.
Save time
Typical remote search takes nearly 2 months. This is much faster in comparison with any other technology.
Efficient
Probability of discovery of a new deposit by means of RC GeoVision is higher than 90%.
Save money
You can reduce a few times your expenses for search and survey using RC GeoVision technology.
Check out the full presentation of RC Geo Vision technology
