cleaner and smarter
Cyentech respects the value of carbon neutrality. On a mission of promoting clean energy solutions, we provide a series of environment-friendly and high-efficiency earth exploration services by adopting state-of-the-art EM modeling and machine learning techniques.
We are specialized in
SUBSURFACE EM MODELING AND INVERSION
Learning from the inside earth is never easy and sometimes compromised by the failed drilling and data interpretation. We are specialized in EM modeling and inversion of modern propagation resistivity measurements. The highlight tasks include:
- Fast and accurate 1D and 2D forward EM modeling
- Conventional and machine learning assisted inversion algorithms
- Model uncertainty assessment to ensure the minimum risk of real-time data inversion
- GPU and Cloud accelerated forward/inversion modeling
HIGH-performance em telemetry
Telemetry helps send data from the downhole to the surface and
from the surface to the downhole in real-time. Compared to mud-pulse telemetry, electromagnetic (EM) telemetry can be applied in underbalanced drilling and in areas where the loss of circulation is prevalent, such as shale gas and geothermal drilling. Electromagnetic telemetry also has the potential to achieve a much higher rate of data transmission. Cyentech has rich experience in fast EM simulation. We offer
- Fast and reliable simulation for the service companies to achieve the maximum capacity of their EM telemetry system
- Our enhanced GPU-accelerated solution is low-cost and high-performance than the current generic commercial software
co2 plume monitoring
As a key step that moving forwards to the green energy era, the applications in Carbon Capture and Storage become more important than ever. We prioritized the research and development specifically for the CO2 plume monitoring task.
Cyentech investigated a deep learning enhanced framework specifically for CO2 plume reservoir monitoring in real-time. It minimizes the risk of failed CO2 plume monitoring and guarantees the safety and efficiency of real-time reservoir monitoring.
- Joint inversion of multi-physics monitoring data including seismic and electromagnetic data
- Deep learning framework using deep neural network and large training dataset
- Tuned specified for CO2 storage reservoirs and reached high-performance and accuracy