  Steady-state process models are common in virtual flow meter applications due
to low computational complexity, and low model development and maintenance
cost. Nevertheless, the prediction performance of steady-state models typically
degrades with time due to the inherent nonstationarity of the underlying
process being modeled. Few studies have investigated how learning methods can
be applied to sustain the prediction accuracy of steady-state virtual flow
meters. This paper explores passive learning, where the model is frequently
calibrated to new data, as a way to address nonstationarity and improve
long-term performance. An advantage with passive learning is that it is
compatible with models used in the industry. Two passive learning methods,
periodic batch learning and online learning, are applied with varying
calibration frequency to train virtual flow meters. Six different model types,
ranging from data-driven to first-principles, are trained on historical
production data from 10 petroleum wells. The results are two-fold: first, in
the presence of frequently arriving measurements, frequent model updating
sustains an excellent prediction performance over time; second, in the presence
of intermittent and infrequently arriving measurements, frequent updating in
addition to the utilization of expert knowledge is essential to increase the
performance accuracy. The investigation may be of interest to experts
developing soft-sensors for nonstationary processes, such as virtual flow
meters.
