In practice

dynamic fluid level is traditionally measured onsite by using the acoustic method. This method

however

has its limitation in determining real-time dynamic liquid level. Determining real-time dynamic liquid level by analyzing the measured dynamometer card has poor precision. Model aging happens as time goes by with the data driven soft sensing modeling method. An incremental dynamic Gaussian process regression (IDGPR) was presented for the soft sensing modeling in order to realize real-time determination of dynamic liquid level. At the beginning a basic soft sensing model based on dynamic Gaussian process regression was established. After the model was put into application

it could be updated on-line through an incremental learning method. The model could be constantly adaptable to the change of operating condition and precisely predict dynamic liquid level. The application result in the oil field showed that the proposed soft sensing model achieved high prediction precision and good generalization ability

meeting engineering requirement.