For the measurement of dynamic liquid level of oil wells

the traditional manual work has many shortcomings

such as low precision

poor real-time performance etc. According to analysis of actual production data

a soft sensor method based on empirical mode decomposition (EMD) and black hole-least squares support vector machine (BH-LSSVM) was proposed to realize soft sensor modeling of dynamic liquid level. In oilfield production

static model cannot fully reflect the production conditions which may lead to model failure. Therefore a dynamic model was proposed by building a performance evaluation module

which could improve adaptive ability and prediction precision. The proposed method had higher measurement accuracy of dynamic liquid level and stronger adaptive ability for production fluctuation

which met requirement of oil production. Automation in oil production was improved.