Abstract: 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 combined 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 results. 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 verifies the feasibility of applying this method to actual production prediction in oil fields.

Key words:  , yield prediction； empirical wavelet transform； convolutional neural network； bidirectional long short-term memory network； self-attention mechanism