Study On Rate of Penetration Prediction Model of Offshore Drilling Machinery and Structure Optimization Base on Deep Learning

Abstract

With the development of modern downhole monitoring technologies such as measurement while drilling, the data obtained during drilling has the characteristics of huge volume, rich variety, and strong timeliness, which greatly promotes big data and artificial intelligence in drilling rate prediction from the data level. Progress. How to further effectively excavate and use drilling big data, improve drilling efficiency, and reduce drilling risks is still a hot research topic. This article explores the real-time drilling data of an exploratory well in the South China Sea, and establishes a reasonable machine learning model to accurately predict the ROP and its changing trend. The research first cleans the real-time drilling data, and improves the data quality used for modeling through standardized data processing; then establishes a deep neural network, optimizes input and output parameters, trains the model, and validates the prediction results; finally, optimizes the model structure to improve prediction The accuracy and efficiency of the model. The research results show that when the amount of data is sufficient and the missing values are few, the neural network can more accurately predict the ROP and its change trend, and at the same time, reasonable optimization of the model can also improve the accuracy and calculation efficiency of the drilling rate prediction.