Safety

Data-Driven HSE Assurance Program Provides Effective Risk Management

This paper describes a tool that complements predictive analytics by evaluating top health, safety, and environment risks and recommends risk-management-based assurance intervention.

Fig. 1—Trending of assurance accuracy and HSE risk-management efficiency in the second quarter of 2024 after the deployment of the HSE Assurance Prescription Tool.
Fig. 1—Trending of assurance accuracy and HSE risk-management efficiency in the second quarter of 2024 after the deployment of the HSE Assurance Prescription Tool.
Source: SPE 221994.

Since 2019, the operator’s project-delivery arm has invested in establishing its Artificial Intelligence Incident and Risk Analysis (AIIRA) platform, an artificial-intelligence model designed to predict future health, safety, and environment (HSE) risks and incidents based on historical HSE-incident data, coupled with a prescription of control measures. Assurance is identified as one of the essential elements in the prescription of control measures of the enhanced module of AIIRA. This paper, therefore, advocates for an HSE Assurance Prescription Tool that complements predictive analytics by evaluating top HSE risks and recommending risk-based assurance intervention wherever appropriate.

Introduction

Conventional risk-management methodologies, characterized by rigid HSE assurance plans typically developed at the onset of a project, have become inadequate and inaccurate in the face of the dynamic and evolving landscape of project execution. Typical HSE plans often rely on a reactive approach and an annual review cycle of HSE assurance plans. The enhanced AIIRA predictive model is complemented by an HSE Assurance Prescription Tool that improves upon simple identification of top HSE risks by rationalizing holistic risk-based assurance interventions.

Frontline workers often are the first to encounter hazards and can provide valuable insights in managing operational risks.

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