Physics-Informed Adaptive Drift Compensation Framework for Machine Learning-Based Instrumentation Transmitter Calibration in Oil and Gas Industry

Accurate calibration of instrumentation transmitters is critical for ensuring operational safety, process efficiency, and regulatory compliance in the oil and gas industry. Traditional calibration methods and existing machine learning approaches treat calibration as a purely data-driven regression problem, ignoring the underlying sensor physics and failing to adapt to evolving drift patterns. This paper proposes a novel Physics-Informed Adaptive Drift Compensation (PI-ADC) framework that integrates first-principles sensor models with data-driven machine learning for automated calibration of pressure, temperature, flow, and level transmitters. The PI-ADC framework incorporates three key innovations: (1) a physics-constrained loss function that enforces thermodynamic consistency, (2) an adaptive drift detection mechanism using statistical process control, and (3) a selective retraining strategy that updates models only when significant drift is detected. We conduct comprehensive experiments comparing nine machine learning algorithms—SVR, RF, XGBoost, KNN, ANN, LSTM, 1D-CNN, GPR, and LSTM-SVM hybrid—enhanced with the proposed PI-ADC framework using industrial datasets from 45 instrumentation transmitters across three petroleum facilities over 18 months. Results demonstrate that the PI-ADC-enhanced XGBoost achieves 52.3% RMSE reduction compared to traditional polynomial fitting (p < 0.001), with 34.7% improvement in long-term stability over standalone ML approaches. The framework reduces required retraining frequency by 67% while maintaining calibration accuracy within ±0.1% of full scale. A pilot deployment at an offshore platform validates the practical applicability of the proposed approach.