基于短时能量与LSTM的油井动液面深度研究

Research on Depth of Oil Well Moving Liquid Surface Based on Short-term Energy and LSTM

油井动液面深度计算一直是油田行业关注的重要课题,高效、准确地获取井下液面的动态深度信息对石油行业发展至关重要。为此,针对油井动液面的深度测算受环境噪声的影响而导致计算误差较大的问题,研究基于声波法的油井动液面深度估计与预测算法。通过设计改进型短时能量过零函数和三电中心削波函数,以及融合多渠道液面位置估计信息,获得动态液面的深度估计算法;将此法获得的液面位置和平均声速作为LSTM神经网络的输入,以及实测液面深度作为期望输出,获得可预测液面深度的预测模型。比较性的实验结果表明,所获液面深度计算算法较之短时能量和短时能量过零函数法,更能有效测算动液面深度;得到的预测模型能有效预测不同时段声波下的液面深度。

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.