Oil field production prediction and regulation
based on bigdata analysis
Oilfield Underground Aquifer Injection
Monitoring
Water contamination is a primary concern in
a region where water and petroleum play such vital roles in the
economy, and where both industry and regulatory agencies pay close
attention to environmental quality. In this research, we built a
Distributed Ledger Technology (DLT) based prototype using R3 Corda.
Its purpose applies in the oil & gas underground injection control
(UIC) operations for the underground aquifer protection.
Oil field
healthy evaluation by ensemble learning
With the majority voting algorithm, we do not predict the oil
production capacity only by the oil data, but also the water and
pressure data. As a result, the final confidence of the prediction
will be much higher, compare to the previous method.
Preliminary results: Working on this
Computational fault location detection
algorithm based on the oil field production log data Based on the oil field production log
data, we can find out the behavior relationship between each pair of
the drilling points. By combining this relationship map with the
geology information, we can develop the algorithm to detect the
fault location in the oil field.
Preliminary results:
Working on this
Solar farm adaptive control
Heliostat control to prevent transient
thermal flux
The solar power tower attracts increasing
attentions in recent years in renewable power generation. Heat
transfer fluid is heated by focusing the concentrated solar
radiation on a tower-mounted receiver, and then is used to drive the
turbine, and thereafter generate electric power. One of the
challenges in solar power tower operation is due to sudden thermal
changes on the surface of the central receiver.
Geographic
information based heliostat control.
Based on the data from geographic information system, we can better
predict the weather change and increase the heliostat control
efficiency.