Risk-Based Process Engineering Framework for Strengthening Reliability Across Complex Natural Gas Infrastructure Systems

Abstract

Natural gas infrastructure is a coupled system of wells, gathering lines, processing units, compressors, transmission pipelines, storage assets, metering stations, and distribution interfaces. Reliability failures within this system rarely arise from one technical defect. They emerge through interactions among degradation, process variability, protection-system performance, human decisions, organizational controls, and external hazards. Existing approaches often treat process safety, asset integrity, inspection, maintenance, and operational risk as separate programs. This paper develops a unified risk-based process engineering framework that connects these programs through a common decision structure. The study uses an integrative review of standards and scholarly literature published to date. The synthesis draws on risk management, risk-based inspection, functional safety, pipeline integrity management, barrier theory, human factors, and reliability engineering. The proposed framework contains eight linked functions: system definition, hazard identification, consequence modelling, failure-likelihood assessment, barrier evaluation, risk aggregation, intervention selection, and performance learning. A bow-tie representation connects initiating causes, preventive barriers, loss-of-containment events, mitigative barriers, and consequences. A dynamic risk register then links each scenario to operating limits, inspection findings, maintenance tasks, management-of-change actions, and leading indicators. The framework replaces isolated equipment rankings with system-level prioritization and treats uncertainty as a decision variable. It also introduces reliability, safety, environmental, and economic criteria into one intervention portfolio. The paper contributes a practical architecture for allocating engineering attention across heterogeneous gas assets while preserving traceability from hazard evidence to action. The framework is suitable for conceptual testing, case-study validation, and later quantitative implementation. Its central proposition is that reliability improves when risk information moves continuously across lifecycle functions rather than remaining within specialist silos.

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