Energy Consumption Prediction and Optimization of Electrical Submersible Pump Well System Based on DA-RNN Algorithm

Abstract Electrical submersible pump(ESP) well system is widely used in the oil industry due to its advantages of high displacement and lift capability. However, it is associated with significant energy consumption. In order to conserve electrical energy and enhance the efficiency of petroleum companies, a deep learning-based energy consumption calculation method is proposed and utilized to optimize the most energy-efficient operating regime. The energy consumption of the ESP well system is precisely determined through the application of the Pearson correlation coefficient analysis method, which is utilized to examine the relationship between production parameters and energy usage. This process aids in identifying the input parameters of the model. Following this, an energy consumption prediction model is developed using the dual-stage attention-based recurrent neural network(DA-RNN) algorithm. To evaluate the accuracy of the DA-RNN model, a comparison of its errors is carried out in comparison to three other deep learning algorithms: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transform. Lastly, an orthogonal experiment is executed using the chosen model to pinpoint the most energy-efficient operating regime. Analysis of 325 ESP wells in the Bohai PL oil field indicated that ten parameters, including choke diameter, casing pressure, pump inlet pressure, pump outlet pressure, motor temperature, frequency, oil production, gas production, water production, and GOR significantly impact the energy consumption of the ESP well system. Consequently, these parameters were selected as input variables for the deep learning model. Due to the attention mechanisms employed in the encoding and decoding stages, the DA-RNN algorithm achieved the best performance during model evaluation and was chosen for constructing the energy consumption prediction model. Furthermore, the DA-RNN algorithm demonstrates better model generalization capabilities compared to the other three algorithms. Based on the energy consumption prediction model, the operating regime of the ESP system was optimized to save up to 12% of the maximum energy. The energy consumption of the ESP well system is affected by numerous parameters, and it is difficult to comprehensively evaluate and predict quantitatively. Thus, this work proposes a data-driven model based on the DA-RNN algorithm, which has a dual-stage attention mechanism to rapidly and accurately predict the energy consumption of the ESP well system. Optimization of production parameters using this model can effectively reduce energy consumption.