Case Study

Renewable Energy

PETAI Services provide SOTA to drive your decisions based on AI. Our Research and development team builds SOTA architectures for enterprise data.


Attribute Selection: 

Seismic attributes contain information (energy) in a given seismic volume. In PETAI we use state of the art deep learning algorithms in selecting the right attributes for seismic interpretation. At PETAI we provide solutions to classification of multiple seismic attributes and enable seismic interpretation of geologic features and their geometries. 


Use machine learning techniques to find CO2 storage and enhanced oil recovery related projects, especially in the area of development of computationally fast empirical models, reduced order models (ROMs) or proxy models in subsurface modeling. Machine learning techniques are used to predict geologic CO2 sequestration monitoring design. Empirical models are used to prediction of CO2 storage and oil recovery potential in Residual Oil Zones (ROZ) can be developed using different machine learning techniques. 


Machine learning is used to better predict seismic activity during geothermal exploration and to optimize geothermal energy production. Geothermal systems require the creation of fractures through hydraulic stimulation. This fracture formation and stimulation is associated with microearthquakes (MEQs) that can damage buildings and other surface structures. Machine learning (ML) algorithms they will be able to forecast and predict seismic events such as MEQs.