Oil and gas well production prediction is of great significance for efficient development of oil and gas resources.A two-channel production prediction model incorporating empirical wavelet transform(EWT)and convolutional bi-directional long and short-term memory network is proposed to address the problem of strong nonlinearity and difficulty in prediction of production data due to inter-opening production and other artificial operational factors.One part of the model uses EWT to decompose gas production data,and the decomposed subseries are extracted in the time and frequency domains using a bi-directional long and short-term memory network(BiLSTM);the other part of the model uses a one-dimensional convolutional neural network(1D-CNN)to extract local time-series features from the multidimensional historical production data,and then uses BiLSTM com-bined with a self-attentive mechanism to extract the output features from the 1D-CNN module output features to mine the global features of gas well production data.Finally,the features of the two parts of the model are fused to generate the final prediction re-sults.Experimental modeling analysis is carried out using the late production history data of a gas well,and it is found that the prediction results of this method are more accurate for complex production sequences with frequent manual measures,which veri-fies the feasibility of applying this method to actual production prediction in oil fields.
维普
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