December 30, 2023 [Journal of Petroleum Technology]- Health, safety, and environment operations can be greatly enhanced by using artificial intelligence (AI) techniques on HSE data. One important aspect inherent in this process is the need to establish trust in the AI system among the users.
Health, safety, and environment (HSE) operations can be greatly enhanced by reducing the uncertainty inherent in quantifying human activity. One way to do this is by using artificial intelligence (AI) techniques, in particular natural language processing (NLP), on HSE data to identify hidden trends, uncover and quantify textual data, and, ultimately, create a smart system that can alert users to risk before that risk is realized. This allows an organization to better target their resources, such as safety coaches, more effectively to prevent an adverse event, protecting vital equipment and potentially saving lives. One important aspect inherent in this process is the need to establish trust in the AI system among the users. This is especially the case during data collection, system rollout, and user adoption.
In order to realize the goal of an AI-driven HSE work flow, a system was implemented that allowed for a) the easy collection, structuring, and preprocessing of data associated with the performance of management observations and b) the development and implementation of a robust set of AI tools that allowed the users to enhance their existing work flows to better be able to identify, quantify, and address HSE risk. This created a new set of leading indicators for HSE awareness. While management observations were the first set of data considered because they were the most robust data set, the adaptable system was constructed in such a way that work orders, audits, and near misses could be easily incorporated in the future.
This study is part of a pilot project with Petroleum Development Oman for IHTIMAM, a process that creates a safety partnership between the workforce and management that continually focuses everyone’s attention and actions on their own and others daily safety behavior.
A novel, comprehensive approach for HSE data collection and development of industry-specific AI models is presented. In addition, a novel approach was identified to leverage user adoption and use cases in terms of both method and application.
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