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A Review of Data-Driven Prediction of Undesirable Events in Offshore Oil Wells
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Abstract:
The production and operation of offshore oil wells present typical characteristics of strong coupling, high nonlinearity, obvious time-varying behavior, and high operational risks. The occurrence of adverse events is rarely triggered by the over-limit behavior of a single variable; rather, it usually stems from the gradual deviation of multivariate correlation patterns in downhole systems from normal operating conditions. Traditional anomaly recognition methods based on threshold rules and manual experience are widely used in engineering practice and have practical value. Nevertheless, under complex operating-condition switching, weak precursor anomalies, and cross-well operational discrepancies, these methods are often affected by high false-alarm rates, poor transferability, and delayed responses. With the rapid development of technology, data-driven methods have gradually evolved into a core technical solution for anomaly detection and adverse-event prediction in offshore oil wells. Effective anomaly identification and early prediction based on production monitoring data have become important research directions in intelligent oilfield construction. Targeting research progress in predictive analytics for the oil and gas industry and state-of-the-art multivariate time-series anomaly detection methods, this paper conducts a systematic review of adverse-event prediction in offshore oil wells. It summarizes existing studies based on the public 3W benchmark dataset and analyzes key data challenges, including structural missingness, cross-well heterogeneity, class imbalance, and weak precursor features. Furthermore, this paper presents a review-oriented conceptual design of a missing-aware multi-scale time-frequency graph. Transformer framework tailored to offshore oil well scenarios, which is intended to support anomaly detection, adverse-event recognition, and early prediction in future empirical validation. From a design-rationale perspective, this study compares the proposed framework with conventional methods and existing deep models, and discusses its potential advantages in four aspects: missing-aware input encoding, dual-domain time-frequency representation, physics-prior-initialized learnable graph structure, and multi-task learning.
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