Research On Oil Production Prediction Algorithm Of Oil Wells Based On Deep Belief Network

Predicting future oil well production based on historical production data has always been a focal point of research for oilfield scientists.When using statistical learning methods for oil well production prediction,challenges such as low prediction accuracy and excessive demand for domain expertise exist.With the advancement and application of machine learning,these issues have been partially addressed.However,new challenges have emerged,including the insufficient ability of machine learning to extract time series features present in oil well production data and the high training cost and potential overfitting associated with deep learning.To address these challenges,this study proposes an oil well production prediction algorithm based on deep belief networks.The main research objectives are as follows:(1)The maximum information coefficient(MIC)is used to perform feature importance selection on the oil well production data.Considering the vast amount of irrelevant features in the production data,the MIC algorithm is employed to extract important features from the production data.A single deep belief network is then constructed for oil well production prediction.Experimental results validate that compared to models such as the backpropagation(BP)neural network and support vector regression,the single deep belief network algorithm significantly improves prediction accuracy and model generalization.(2)This study implements a prediction algorithm for oil well production using a combination of Long Short-Term Memory(LSTM)neural network and deep belief network.Considering the time series nature of oil well production data,LSTM neural network is employed to capture the temporal features of the data.To prevent overfitting during the feature extraction process,a Dropout layer is introduced between LSTM layers.Experimental results demonstrate that LSTM neural network efficiently extracts the time series features present in the production data,thereby improving the accuracy of the prediction model.(3)This study presents a prediction algorithm for oil well production using a combination of Temporal Convolutional Network(TCN)and deep belief network.Addressing the issues of parallelism and gradient stability in LSTM neural networks,TCN is utilized to extract the time series features from long-term sequences.To capture the interdependencies among input features,a self-attention mechanism is integrated into the prediction algorithm to enhance prediction accuracy.Experimental results demonstrate that TCN exhibits stronger feature extraction capabilities,resulting in higher prediction accuracy and reduced training time for the model.The comprehensive experimental results demonstrate that,compared to other methods,the algorithm proposed in this study for oil well production prediction not only improves the accuracy of the predictions but also enhances the efficiency of the prediction process.This research provides important theoretical and technical support for the development of petroleum extraction plans.