Improving the Quality and Efficiency of Operational Planning and Risk Management with ML and NLP
- Claire Emma Birnie (Equinor ASA) | Jennifer Sampson (Equinor ASA) | Eivind Sjaastad (Equinor ASA) | Bjarte Johansen (Equinor ASA) | Lars Egil Obrestad (Equinor ASA) | Ronny Larsen (Equinor ASA) | Ahmed Khamassi (Equinor UK LTD)
- Document ID
- Society of Petroleum Engineers
- SPE Offshore Europe Conference and Exhibition, 3-6 September, Aberdeen, UK
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Operational Planning, Risk Management, Machine Learning, Natural Language Processing
- 1 in the last 30 days
- 161 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
To ensure safe and efficient operations, all offshore operations follow a plan devised to take into account current operation conditions and identify the optimum workflow with the minimum risk potential. Previously, planners had to manually consult eight data sources, each with a separate UI, and summarise the plan in a.pdf document. Equinor's Operation Planning Tool (OPT) has been developed to easily present the planners with the technical conditions of a platform, identify potentially dangerous combinations of concurrent activities, and propose learnings from eight years’ worth of incident recordings – all relevant to the current list of planned activities. The tool aims to answer questions such as ‘are other activities planned for the same time which would make this activity unsafe?’ or ‘have incidents previously occurred whilst performing similar tasks on this equipment type?’.
This paper details the development of the OPT with a particular focus on the application of Natural Language Understanding for extracting equipment types and tasks involved in previous incidents and relating these to planned activities. Utilising natural language processing techniques, a system has been developed that mines the content of Equinor's incident database, and assigns context to incidents, by identifying the systems, activities and equipment involved and the conditions on the asset at the time of the incident. The same context is also discovered from the content of planned activities. These key concepts are organised into a knowledge graph synthesising Equinor's institutional safety and operational experience.
The OPT has reduced time spent planning by providing a single interface detailing a plant's technical conditions, all planned work orders and relevant lessons learned from previous incidents. By reducing the reliance on personal experience, the tool has provided subjectively improved risk identification and handling, plus faster knowledge transfer to new employees as well as focussed cross-platform knowledge sharing. The success of the tool highlights the strength of combining data and leveraging the vast quantities of historic data available both in unstructured and structured forms to create a safe, offshore work environment.
|File Size||1 MB||Number of Pages||14|
Honnibal, M., and Montani, I., 2013. ’Prodigy: A new tool for radically efficient machine teaching'. Available at: https://explosion.ai/blog/prodigy-annotation-tool-active-learning (Accessed: 12 April 2019).