Hi r/machinelearning,
I have a problem that I am looking at where there are 50 or so sensors that are recording data every minute. One of the sensors is the variable of interest (subcool, a temperature difference), 6 of the sensors relate to variables that can be changed by an operator in order to keep the variable of interest in control (steam, choke, gas rates), and the rest of the sensors monitor the environment (temperatures, pressures). The goal is to better predict and control the variable of interest. The time lag between the changing of operator controlled variables and the variable of interest can vary depending on the state of the environment. The time to action right now is usually hourly, and an out of control event could take a day or two to be brought back in control.
Does anyone have any suggestions for how to attack this as a machine learning problem? What kind of preprocessing will I need to do? I have seen HMMs suggested in a few places, or the creation of time independent features using sliding time windows. I’m fairly new to this field, so any suggestions will help. Thanks!
Some public reference documents that I found on the subject:
Real-Time Optimization of SAGD Operations
Cointerpretation of Flow Rate-Pressure-Temperature Data from Permanent Downhole Gauges
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