Getting Started
Step Guide to iris risk knowledge
Last updated
Step Guide to iris risk knowledge
Last updated
iris risk knowledge can be used by petro-technical experts in different domains of Major Capital Projects and Operating Assets to classify risks in a risk matrix, and therefore prioritize mitigation actions. This is relevant in the context of HAZOPs, Risk Assessments, Technical Audits and other risk-informed activities.
iris risk knowledge not only reduces the effort in risk classification, but it can also help standardize the process of risk scoring and capture the tacit knowledge inside oil & gas companies.
For classifying risks via iris risk knowledge, upload an Excel Table (or CSV file) via the iris risk knowledge user interface.
iris is by default configured to accept risk entries in the format shown above, this includes: 1) Sub-Discipline the risk corresponds to it, 2) Title, 3) Description, 4) Cause and 5) Consequence. A large number of risks (>10,000) can be uploaded on a single file).
iris risk knowledge user interface allows for an interactive experience in which information can be explored from different angles. The 4 panels in iris risk knowledge user interface are dynamically linked, so that any change / filtering in one of them is immediately reflected on the others.
By default, the Risk Stream will show all items in the risk matrix. Selecting one bucket in the matrix will filter the Risk Stream view to show only risks which are pertinent for the particular bucket.
Selecting a keyword will also result in the Risk Stream filtering only risk entries relevant to the keyword of interest.
iris risk knowledge incorporates feedback from the user. Visual queues (red circles) indicate items for which iris has a low confidence in its predicted classification.
iris risk knowledge users can also modify the risk classification of any entry. This can be done, via the edit button (pencil in circle) in the risk stream.
The risk score as well as text fields can be edited. Note that this information is incorporated onto iris' backbone models, so that the accuracy of future predictions will be improved.