Remaining Useful Life Prediction and Operation Optimization of Offshore Electric Submersible Pump Systems Using a Dual-Stage Attention-Based Recurrent Neural Network

Abstract

Electric Submersible Pumps (ESPs) serve as the primary artificial lift technology in offshore oilfields and play a crucial role in ensuring stable and efficient marine oil and gas production. However, the harsh offshore operating environment—characterized by high temperature, complex multiphase flow, and frequent load fluctuations—makes ESPs highly susceptible to accelerated degradation and unexpected failure. To enhance the operational reliability and efficiency of offshore production systems, this study develops a Remaining Useful Life (RUL) prediction method for offshore ESP systems using a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN). The model integrates an input-attention mechanism to identify degradation-relevant offshore operating variables and a temporal-attention mechanism to capture long-term deterioration patterns in real marine production data. Using field data from a representative offshore oilfield in the Bohai Sea, the proposed method achieves an average prediction error of less than 28 days, demonstrating strong robustness under complex offshore conditions. Beyond prediction, an RUL-driven operation optimization strategy is formulated to guide controllable parameters—such as pump frequency and nozzle size—toward extending ESP lifespan and improving offshore production stability. The results show that combining predictive maintenance with operational optimization provides a practical and data-driven pathway for improving the safety, efficiency, and sustainability of offshore oil and gas development. This work aligns closely with the goals of marine resource development and offers a valuable engineering perspective for advancing offshore oilfield operations.

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