A Seq2Seq-Based Method in the Prediction of Oil Production with Long Short-Term Memory Network

This paper employs a deep learning approach based on Long Short-Term Memory (LSTM) networks to predict oil well production. Leveraging LSTM's ability to capture long-term time series patterns, historical production data, geological information, and operational parameters were integrated to construct an effective LSTM model. Experimental results demonstrate that the model excels in predicting oil well production, showcasing high accuracy and reliability. This method provides crucial decision support for oilfield management, with the potential to enhance oil well production efficiency, reduce production costs, and contribute to the sustainable development of the energy industry.