COMPUTER VISION FOR OIL AND GAS INDUSTRY
In an industry where oil prices are constantly rising, it has become imperative to act on operating costs, productivity and lead times in order to have better returns on investment.
The implementation of computer vision solutions reduces many security and financial constraints by helping operators control critical tasks through automated systems.
This is the worldwide turnover of the oil industry in 2017.
OF OIL COMPANIES
have already taken digitalization initiatives.
MILLION + BARRELS
produced per day in August 2018, including 32.5 million produced by OPEC.
The collapse of the barrel price in 2014 created a strong impetus among oil and gas operators to find solutions to streamline their activities and reduce their costs.
- Know the state of deterioration of equipment and infrastructure.
- Automate the inspection operations that are still done manually.
Detection of anomalies on industrial installations (turbines, pumps and compressors)
Implement a predictive maintenance process and optimize your interventions at the first signs of infrastructure failure.
Refinery corrosion detection
Be alerted of a risk of malfunction at the first sign of corrosion.
About 46% of professionals in the oil and gas sector believe that too little investment is allocated to the inspection of installations and equipment for safety purposes.
- Prevent the risk of explosion in fuel stations
- Prevent occupational accidents
- Prevent environmental disasters
Detection of abnormal fire and smoke outbreaks
Initiate emergency procedures at the first sign of a fire hazard.
Detection of risky behavior of clients
Send an alert when a client does not comply with safety instructions
Seismic prospecting is one of the most resource-intensive methods of exploration: seismic monitors generate more and more data while the number of geologists does not increase.
- Identify oil and gas deposits at a lower cost
Analysis of subsurface data from seismic surveys
Facilitate the work of analysts by automatically prioritizing seismic images based on the probability of the presence of a deposit.
Seismic image quality control process