AI, Digital Tools Can Operationalize Readiness for Crisis and Emergency Management
Chris Carpenter_
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 229328, “Operationalizing AI for Crisis and Emergency Management,” by Ashish Chandrashekhar Kulkarni and Ashwina Das Kulkarni, The Bell Group. The paper has not been peer-reviewed.
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Crisis and emergency management (CEM) in high-risk sectors such as oil and gas, petrochemicals, and transportation faces growing challenges from operational complexity, interagency coordination gaps, and evolving hazards. While compliance frameworks set essential standards, many organizations still struggle to maintain continuous operational readiness. This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can operationalize readiness.
Industry Incident Landscape
The authors cite a report of the Institution of Chemical Engineers’ Safety and Loss Prevention Special Interest Group, “Learning Lessons From Major Incidents.” In the report, root causes of 25 various incidents since Piper Alpha in 1988 were analyzed to identify underlying lessons. The lessons learnt for crisis and emergency management are provided in Table 1 of the complete paper. The table indicates that gaps in crisis and emergency management often lie in lack of compliance, inadequate planning, training and competency issues, and response. Reviewing these CEM failures, the most prevalent weakness across oil and gas incidents is a lack of procedures, an issue present in most cases. This indicates a widespread gap in scenario-specific emergency protocols and formalized response frameworks. Escalation control and equipment deficiencies also feature prominently. Overall, the data underscore that while technical safeguards are important, procedural readiness and operational discipline remain the most critical determinants of effective crisis and emergency management. Conventional CEM approaches, characterized by static plans and procedures, periodic drills, and post-event reporting, are inadequate for addressing the dynamic, high-consequence scenarios encountered in the oil and gas sector. These realities necessitate the integration of advanced, data-driven technologies capable of augmenting human decision-making and improving the fidelity of situational awareness. These technologies include the following:
- Large language models
- Cognitive modeling frameworks
- Virtual emergency-operations centers, leveraging secure, cloud-based collaboration platforms, that enable distributed teams to share a unified operational picture
Rather than focusing solely on documenting outcomes, technologically enhanced CEM systems can continuously anticipate evolving hazards, recommend optimized courses of action, and evaluate execution effectiveness.
Business Case for AI in CEM
From a regulatory and compliance perspective, AI can support continuous adherence to industry frameworks and international standards. Automated documentation, performance auditing, and real-time readiness scoring provide verifiable evidence for regulators and insurers.
Financial impact is equally tangible. Case studies indicate that AI-enabled anomaly detection and predictive-resource allocation can reduce mobilization times by up to 40%, cut training logistics costs by 30–60%, and prevent incident escalation that could otherwise result in multimillion-dollar losses.
In terms of readiness and resilience, AI-based cognitive modeling and performance analytics identify capability gaps before they manifest in live operations. This allows targeted interventions, enhancing overall team efficiency and improving readiness indices by measurable margins.
Importantly, AI adoption in CEM supports the United Nations Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).
The compete paper devotes several pages to translating the business case into applied enhancements with ALERTSim, a platform developed by the authors for CEM training. The authors describe the platform’s features in terms of procedural completeness, decision authority, communication protocols, training and simulation, change management, and scalability and integration.