Approach to Risk Management Uses Knowledge-Based Artificial Intelligence
This paper details experiences gained while developing a novel technology-driven approach to risk assessment methodologies such as process hazard analysis, hazard identification, and hazard operability in oil and gas.
From a technology perspective, the advent of AI has often made it difficult for non-technologists to understand, let alone explain how a particular recommendation is derived from a given digital application. The language in which business logic is encoded is often only decipherable to those who built the application and difficult to replicate at a large scale.
Recent adoption of Systems Thinking philosophies across industries have sparked the need for “knowledge-centric” technology that puts subject matter and domain expertise at the center of software development. Making software products more comprehensible and inclusive to those who benefit from them while retaining a solid representation of system complexity, is a key premise of digital transformation at scale.
This paper details experiences gained while developing a novel technology-driven approach to risk assessment methodologies such as process hazard analysis, hazard identification, and hazard operability in oil and gas. Emphasis has been placed on combining encoded human knowledge with artificial intelligence (AI) techniques in a way that fosters safer designs and operations while keeping subject-matter experts (SMEs) at the center of decision making.
Encoding of human knowledge (e.g., subject-matter expertise and industry best practices) in digital applications traditionally has been associated with creating static pieces of information, such as lessons learned documentation and validation activities for hazard analysis. New digital technologies, however, make it possible to create truly dynamic knowledge representations that capture key concepts and their relationships, creating a new type of “source of truth.” As a result, corporate and external knowledge can be made more readily accessible to engineers and operations personnel participating in decision making.
Digital corporate knowledge can also be supplemented with AI techniques, which can help uncover latent threats and better guide optimal decision making. This is particularly relevant in workforce, health, and safety and process safety contexts, where flawed or suboptimal decisions can have catastrophic consequences.
Practical examples from an oil and gas major show how the risk-assessment domain can be represented in a computational knowledge graph in a format that is comprehensible not only to software developers but also, more importantly, to oil and gas SMEs. A presentation of different AI techniques overlaid on top of this computational knowledge graph can also offer a glimpse of the possibilities of marrying SME expertise with emerging digital technologies.
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