Mechanical drilling rate is the most important indicator for measuring drilling efficiency,and mechanical drilling rate prediction can help optimize the drilling process more effectively.However,the prediction of mechanical drilling rate is difficult due to various factors such as geological characteristics,drill string composition,drilling fluid performance,and drilling parameters.In this paper,based on the actual drilling data of a block in the Bohai Sea,a mechanical drilling rate prediction model based on the Bayesian optimization algorithm is proposed to optimize the long short-term memory(LSTM)neural network.The model is compared and analyzed with the standard LSTM neural network prediction model and the LSTM neural network prediction model optimized by the grey wolf optimization al-gorithm.Three wells in the Bohai Sea block were selected for the experimental evaluation of the model.The results show that the Bayesian optimization LSTM mechanical drilling rate prediction model has better prediction accuracy compared to the other two models.
维普
万方数据