Title : Chaos-informed artificial intelligence for early fault detection in oil and gas production systems
Abstract:
The increasing complexity of oil and gas production systems requires advanced monitoring tools capable of capturing nonlinear, nonstationary, and unstable behaviors that often arise under harsh operational conditions. Traditional data-driven monitoring approaches are generally limited by linear assumptions and frequently fail to detect early-stage faults in the presence of noise and hidden chaotic dynamics This paper proposes a novel chaos-informed artificial intelligence framework for early fault detection in oil and gas production systems. The proposed approach integrates nonlinear dynamical analysis with machine learning by combining chaos indicators such as Lyapunov exponents, spectral features, and entropy-based measures with a customized neural network architecture specifically designed to capture intrinsic chaotic patterns in pressure, flow rate, and vibration signals. The framework is evaluated using simulated datasets and real-world-inspired operational scenarios representing typical petroleum production environments. Experimental results demonstrate that the proposed method significantly improves fault detection accuracy, robustness, and sensitivity to incipient anomalies compared to conventional machine learning techniques. The findings highlight the importance of incorporating nonlinear dynamics into intelligent monitoring systems and demonstrate the potential of chaos-aware artificial intelligence as an effective decision-support tool for predictive maintenance, operational safety, and reliability enhancement in oil and gas engineering applications.
