ROP Prediction Method Based on PCA-Informer Modeling

Abstract

Increasing the rate of penetration (ROP) is an effective means to improve the drilling efficiency. At present, the efficiency and accuracy of intelligent prediction methods for the rate of penetration still need to be improved. To improve the efficiency and accuracy of rate of penetration prediction, this paper proposes a ROP prediction model based on Informer optimized by principal component analysis (PCA). We take the Taipei Basin block oilfield as an example. First, we use principal component analysis to extract data features, transforming the original data into low-dimensional feature data. Second, we use the PCA-optimized data to build an Informer model for predicting ROP. Finally, combined with actual data and using the recurrent neural network (RNN) and long short-term memory (LSTM) as baselines, we perform algorithm performance comparative analysis using root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R ²). The results show that the average MAE, RMSE, and R ² of the PCA-Informer model are 9.402, 0.172, and 0.858, respectively. Compared with other methods, it has a larger R ² and smaller RMSE and MAPE, indicating that this method significantly outperforms existing methods and provides a new solution to improve the rate of penetration in actual drilling operations.

Figure 1

Forecasting workflow diagram.

Figure 2

Raw collection data.

Figure 3

Informer model structure.

Figure 4

Informer’s encoder.

Figure 5

Accuracy of the model on the training set before and after PCA optimization.

Figure 6

Prediction results of all methods on the test set.

Figure 7

Distribution of predicted values for the models.

Figure 8

Parameter Sensitivity of Components in Informer.

Figure 9

Total runtime in the training/testing stage.