AI-Driven Framework for Predictive Safety Performance in Intelligent Safety Instrumented Systems (SIS) for Oil & Gas Facilities

Abstract

The complexity of oil and gas operations has gone up, so the demand for intelligent and proactive safety management systems also went up, to reduce operational hazards, equipment failures, and those really bad industrial catastrophes. Traditional Safety Instrumented Systems (SIS) mostly rely on static rule-based designs and fixed threshold mechanisms, but they dont really have adaptive intelligence or predictive decision-making when the operational situation changes quickly. In the last few years, the integration of AI, IIoT, machine learning, and predictive analytics has been changing both industrial process safety and operational reliability in parallel. This study presents an AI-Driven Framework for Predictive Safety Performance in Intelligent SISs specifically for oil and gas facilities. The goal is to make hazard prediction more reliable, support defect detection, enable real-time monitoring, and enable autonomous safety decisions. The work tries to build and test a smart predictive safety framework which can identify abnormal process conditions, anticipate equipment failures, cut down false alarms, and strengthen safety during hazardous industrial scenarios. Industrial safety engineering principles are combined with AI-based predictive analytics, using Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Gradient Boosting, and Long Short-Term Memory (LSTM) networks. Before model training and testing, preprocessing industrial datasets from sensors, PLCs, SCADA systems, DCS, and IIoT-enabled monitoring devices is done, including normalisation, anomaly labelling, feature extraction, and noise reduction. After that, several industrial hazard simulations evaluate the framework, such as gas leakage, pressure escalation, fire dangers, valve malfunction, and pipeline collapse. The experimental results show that the proposed AI-driven SIS framework outperforms traditional SIS architectures in fault detection accuracy, response latency, false alarm generation, system reliability, and risk mitigation. LSTM had the highest predicted accuracy among AI models because it could analyse temporal industrial process data. Per the study, AI enabled predictive intelligence in industrial safety systems supports more or less autonomous decisions, predictive maintenance, and adaptive hazard management, in modern oil and gas facilities. The framework encourages intelligent industrial safety ecosystems that boost operational resilience and equipment reliability, as well as sustained safety performance.