Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM–FNN

Abstract

The drilling process is complex, especially for ultra-deep wells, which face the problems of high temperature, high pressure and poor drilling resistance in their formation. In order to establish an ROP (the Rate of Penetration) prediction model for ultra-deep wells, the characteristics of ultra-deep well drilling operations, such as formation temperature and formation pressure, are fully considered in the process of parameter optimization. Combined with the drilling mechanism and mutual information correlation coefficient, the final input parameters are determined. The powerful nonlinear fitting ability of the artificial intelligence method is very suitable for predicting the ROP. Considering the time sequence of multi-source data, this paper combines the powerful timing information-based mining ability of the LSTM (Long Short-Term Memory Neural Network) with the nonlinear fitting ability of FNN (Fully Connected Neural Network), and establishes an intelligent prediction model of the ROP based on a LSTM–FNN. The results show that the average relative error and R2 of the LSTM–FNN model on the data of well 1 and well 2 are better than the FNN and LSTM models. In addition, the accuracy of the LSTM–FNN model on the data of adjacent wells is reduced by only 5%, which further verifies the good mobility of the model.

Keywords

• rate of penetration
• neural network
• artificial intelligence
• long short-term memory