Abstract The accurate prediction of post-fracturing production for re-fractured wells is crucial for selecting suitable targets for re-fracturing. Given that the post-fracturing production of re-fractured wells is essentially time series data, this study aims to provide an accurate deep neural network approach for production prediction of re-fractured wells. Traditional deep neural networks, such as Recurrent Neural Networks (RNN) and Long Short-Term Memory Networks (LSTM), are not adept at capturing long-range dependencies. To address this limitation, a new Transformer Variant for the Prediction of Production of Re-fractured Oil Wells is proposed, based on the time window method and the classic Transformer architecture. Experimental results demonstrate that the performance of the Transformer shows significant improvement compared to RNN and LSTM. Utilizing production data from Block W in the J Basin, a random sample of nine wells was selected for model fitting and prediction. The time series Transformer model exhibited the lowest Root Mean Square Error. The successful implementation of the proposed Transformer model for time series demonstrates its capability to effectively capture long-range dependencies, enabling more precise predictions than traditional deep learning methods.
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