AI-Driven Optimization and Predictive Maintenance for Electrical Submersible Pump Systems Using Digital Twin Architecture

Abstract

Electrical Submersible Pump (ESP) systems are the primary artificial lift method employed in hy- drocarbon production, yet they remain vulnerable to unpredictable failures that result in costly un- planned downtime. Conventional threshold-based monitoring systems generate approximately 70% false alarms while simultaneously failing to detect genuine failure precursors hidden within complex, multi-sensor interactions. Furthermore, manual optimization of operating parameters—namely, Vari- able Speed Drive (VSD) frequency and choke position—leads to an estimated 15–22% energy waste and accelerated equipment degradation. This thesis presents an integrated Artificial Intelligence framework that addresses three inter- connected challenges: failure prediction, anomaly detection, and dynamic operational optimization, while ensuring that all predictions remain consistent with established pump hydraulic laws. The pro- posed system combines four complementary AI models within a unified Digital Twin architecture: 1. A Bidirectional LSTM network with multi-head self-attention for 24–48 hour failure predic- tion across eight distinct failure modes. A two-stage training pipeline—synthetic pre-training (5.4M parameters) followed by compact real-data fine-tuning (36k parameters)—achieves an AUC of 0.6914 on real field data from three ESP wells, within the industry benchmark range of 0.65–0.75; 2. An LSTM-Autoencoder for real-time anomaly detection through reconstruction error analysis with adaptive thresholding, achieving a 98% reduction in training reconstruction loss; 3. A Deep Reinforcement Learning (DRL) agent based on Soft Actor-Critic (SAC) that au- tonomously adjusts pump operating frequency and choke position, converging to a stable pol- icy with a 73% reward improvement over a random baseline; 4. A Physics-Informed Neural Network (PINN) that enforces pump affinity laws (Q ∝ N, H ∝ N2, P ∝ N3) and energy conservation constraints, achieving R2 > 0.999 with 100% physical compliance. The framework is validated on a synthetic dataset of 50,000+ hourly samples (11 DHM-standard sensor channels, eight failure modes) and on 296,393 real operational records from three ESP wells. A Streamlit-based Digital Twin dashboard provides real-time visualization, SHAP-based explainability, attention weight visualization, and interactive what-if scenario capabilities.