# Modeling the Submergence Depth of Oil Well States and Its Applications
## 1 Introduction
Then, the calculation model of the discharge coefficient was improved. [PERSON] et al. [11] used generalized Darcy's law to model fluid flow, which showed that ultralow matrix permeability could determine the pressure field and displacement field. However, it was also clearly stated that, due to the influence of various factors, developing accurate models is still a major challenge. [PERSON] et al. [12] built a submergence depth model of oil well liquid storage according to the percolation characteristics of the oil layer, combined with the variation law of oil well permeability, liquid pressure, and oil well pressure. The model theoretically gives the variation law of submergence depth and time. Regarding the production cost of oil field production, [PERSON] et al. [13] established a matching model between the periodic average submergence depth and the best speed by analyzing the relevant factors that affect the efficiency of oil wells in the oilfield, combined with the relationship between periodic average pump efficiency and average submergence depth. In recent years, the rapid development of artificial intelligence has also provided new ideas for studying submergence depth or the dynamic liquid surface state. According to the characteristics of the time series of the submergence depth of pumping oil, [PERSON] et al. [14] analyzed and processed the sample data of the submergence depth and time change of the measured site and proposed a submergence depth prediction method based on a support vector machine (SVM). The impulse information of the linear motor was introduced as the input of the prediction model to form an improved submergence depth prediction method. [PERSON] et al. [15] predicted the production efficiency of relevant oilfield pumping data by establishing a differential autoregressive moving average model in a time-series analysis. [PERSON] et al. [1] proposed a prediction method for submergence depth based on a hybrid model to solve problems of production cost. To ensure that the prediction of the submergence depth met the actual production requirements, an analysis model was established. The data-driven model was used to compensate for the prediction results of the analytical model, and the influence of various parameters on prediction accuracy was reduced. Through the simulation experiment of predicting the submergence depth, the advantage of comprehensive prediction performance was proven. [PERSON] and [PERSON] [2] obtained the depth-estimation algorithm for the dynamic liquid level by designing an improved short-time energy zero-crossing function and three electric center clipping functions, integrating multichannel liquid-level position estimation information, and then obtaining the dynamic liquid-level-prediction model. In summary, since the first modern industrial oil well, \"Drake\", was drilled in Pennsylvania over 160 years ago, various oil-well-pumping-related models and calculation methods have been developed. These models and methods have played a positive role in applications, but these models contain some empirical factors that are difficult to fully adapt to the actual pumping state of oil wells. When pumping, it is necessary to manually adjust the corresponding parameter values based on experience. As for modeling using artificial intelligence technology, the key factor is that the source and amount of sample data determine the model's accuracy. Because oil well states change dynamically during pumping in oilfield production, a reasonable pumping strategy is formulated according to the current submergence depth of oil wells so that the pumping unit can not only work in an efficient state but also significantly improve oil well production. Therefore, obtaining the submergence depth of pumping wells in real time and accurately is crucial for efficient pumping in oilfields.