A novel ensemble learning-based soft measurement method for rod-pumping system efficiency

Abstract

Accurate prediction of rod-pumping system efficiency is crucial for evaluating the performance of such systems. Currently, the efficiency of rod-pumping systems is primarily estimated using mechanistic models. With the continuous advancement of information technology and the improvement of oilfield databases, some researchers have employed single neural networks for prediction. However, single neural networks often suffer from low prediction accuracy and poor robustness to noise. To solve this problem, we propose a new integrated learning-based soft measurement of the efficiency of rod pumping systems. Firstly, we proposed five soft measurement methods for rod pumping system efficiency: BiGRU-BiLSTM-CrossAttention, BiRNN-BiGRU-KAN, CNN-BiGRU-KAN, BiLSTM-BiGRU-KAN, and BiLSTM-Transformer-KAN. Then, using these five methods as base learners and FNN as the meta-learner, we constructed a novel rod pumping system efficiency soft measurement method based on the Stacking ensemble learning framework. The hyperparameters were optimized using a multi-strategy integrated Crayfish optimization algorithm, and the model was validated using 5-fold cross-validation. To verify the accuracy of the proposed soft measurement method, we applied it to 10,250 real oil wells for calculation and conducted a comparative analysis with baseline models. The results demonstrate that the proposed soft measurement method can effectively predict the efficiency of rod pumping systems.