Designing a predictive framework on logistical failures for the global oil and gas supply chain

• DBN-GNN-FIS based hybrid framework predicts multimodal disruptions for the GSC. • Trained on 2013-2023 disruptions and validated using 2024 benchmarking data. • Delivers predictive precision at 95% CI, CV <3%, SD <0.45%, observed deviation <4%. • Strengthens predictive planning and aligns with SDG 7 (7.3, 7.a, 7.b) targets. The global oil and gas supply chain (GSC) is a complex, multimodal network that depends on seamless coordination across upstream extraction, midstream transport, and downstream distribution. However, existing models often treat these layers independently, overlooking the probabilistic interdependence and propagation of failures. Addressing this limitation, the present study develops a Diagnostic-Predictive-Adaptive learning-based hybrid framework that integrates Diagnostic Bayesian Network (DBN), Graph Neural Network (GNN), and Fuzzy Inference System (FIS) to predict and interpret logistical failures across the GSC. Historical disruption data from 2013-2023 are used to train the framework which combines probabilistic inference (through DBN), relational learning (through GNN), and adaptive weighting (through FIS) to map the interconnectedness among logistical modes (i.e., pipelines, rail-roadways and waterways) for the GSC network. Validation through sensitivity analysis, convergence stability, and benchmarking using 2024 disruption data within a 95% Confidence Interval (CI) demonstrates robust predictive performance, with Co-efficient of Variation (CV) below 3%, Standard Deviations (SD) under 0.45%, and a prediction deviation below 4% relative to observed disruption patterns. It also accurately captures the directional imbalance between inbound-outbound failures, strengthens early-warning systems for capacity and strategic logistical planning and further supports resilient supply chain operations aligned with the United Nations Sustainable Development Goal 7 (7.3, 7.a, and 7.b) targets on energy efficiency, innovation, and reliability.