Natural Language Processing Enables Better Operational Risk Management

Safe and efficient operations on an offshore facility require careful planning, often taking into account current conditions and the risk potential of any particular job.

In the past, Equinor manually consulted multiple data systems with separate user interfaces to get a view of a facility’s technical status and previous incidents. On top of the time-consuming nature of such work, planners still could not get a complete overview of the full asset status, leading planners to focus individually on the tasks within their disciplines.

To help alleviate these bottlenecks, Equinor developed its Operation Planning Tool (OPT) to provide a single interface displaying data from all of its sources, highlighting the technical barriers and incidents relevant to a planned job, as well as concurrent planned jobs that could lead to an increased risk level.

“If you were to go out offshore and you were building some scaffolding, then it would make sense that you could check all of the incidents that have ever happened while you were building scaffolding in the past when someone else was doing it,” said Claire Birnie, a senior data scientist at Equinor. “This is what we have tried to capture in our operational planning tool. We’re trying to use that information to describe why something could happen or why it did happen, so that we can use that information to prevent something from happening.”

Speaking at the SPE Offshore Europe Conference in Aberdeen, UK, Birnie presented a paper (SPE 195750) she co-wrote with six other authors (Jennifer Sampson, Eivind Sjaastad, Bjarte Johansen, Lars Egli Obrestad, Ronny Larsen, and Ahmed Khamassi) on the development of the OPT by the Knowledge AI team at Equinor’s Digital Center of Excellence.

Birnie described the OPT as an intrusive web application that visualizes different data sources into one place. It provides a near real-time overview of the integrity status of a facility, allowing users to examine the risk profiles of specific areas or of the facility as a whole. Utilizing natural language processing (NLP) techniques, the tool leverages unstructured data to supplement structured data and create additional information that previously did not exist in a machine-readable format, such as the task being performed.

“It aims to answer questions like, did an incident happen for the same task on a similar piece of equipment, or are we planning to do two activities at the same time that shouldn’t be done together in the same place?” Birnie said.

The OPT examines several integrated data sources, including incident reports. It has three primary elements: the status of the facility, the operational plan with integrated risk information, and lessons learned from previous incidents. It overlays the data obtained onto an outline of the physical locations of the platform to help give managers a common view of the risks.