Oilfield Production Prediction Method Based on PCA and Deep LSTM Neural Network

Oil fields have accumulated a substantial amount of data throughout the long-term production and development processes, providing a robust data foundation for machine learning applications. Addressing the challenge that simple LSTM neural networks exhibit poor predictive performance on oil field production series characterized by significant variation and fluctuation, this study first employs Principal Component Analysis (PCA) for dimensionality reduction to transform the original dataset into uncorrelated principal components. Subsequently, we optimize the architecture of the LSTM network layers and train a production prediction model. The practical application results from oil fields demonstrate that both training accuracy and prediction accuracy of the deep LSTM neural network model-trained using PCA-based dimensionality reduction-have significantly improved, with a prediction error as low as 6.47%. The developed oil field production prediction model exhibits strong generalization capabilities for production series with considerable fluctuations, enabling more accurate predictions of oil field output. This advancement holds important practical significance for the dynamic development and adjustment of oil field operations.