Abstract: Dynamic oil well liquid surface depth estimation has been being a crucial issue in the field of oil. It will be extremely important for the development of oil enterprise how to efficiently and precisely acquire the dynamic information of liquid surface depth. Therefore, for the problem that the depth estimation accuracy of oil wells’ dynamic fluid surface is influenced greatly by environmental noises and depth estimation errors, the current work probes into the algorithms of oil wells’ surface depth estimation and prediction based on acoustic wave curves. Therein, a depth estimation algorithm suitable for estimating the depth of dynamic liquid level is acquired through designing an improved short-time energy zero-crossing function and an improved three-electric center clipping function, in which multi-channel liquid level position estimations are fused to decide the position of liquid level. After that, a liquid surface depth prediction model is obtained based on the LSTM neural network, in which the gained liquid surface positions and average sound velocity are taken as the input of the network, and the actual liquid level depth is viewed as the desired output. The comparative experiments have confirmed that the current depth estimation method can effectively decide the depth of dynamic liquid surface, and the prediction model can well predict oil wells’ liquid surface depth.

Key words: fluid level, acoustic logging, LSTM neural network, short-time energy zero-crossing function, center clipping function