Data Analytics Help Identify Critical Components in Offshore Operations
ABS Group developed quantitative analytics to identify critical components in offshore operations. Read more about our data analytics solution.
An organization sought to improve its understanding and identify critical equipment used in offshore oil and gas facilities during drilling, well workover, well completion, production and abandonment operations. Understanding critical components is also a key input to risk-informed prevention activities and performance metrics for communicating organizational progress.
ABS Group was retained to develop risk-based strategies that help the client keep pace with technological and process innovations. Working closely with the organization, we developed data analytics and data management tools that enable comparing, assessing and identifying risks early using a systematic process.
- Identify risks associated with equipment used in offshore operations
- Understand critical components for risk-informed prevention activities
- Develop strategies for evaluating advanced technology risks
Provide an all-inclusive definition of critical components identified in offshore operations
Identify critical components and provide a list of candidate components for inspection emphasis
Develop and implement data analytics addressing risk and criticality of offshore equipment
ABS Group developed data analytics to systematically identify critical components and prioritize the various risks associated with offshore systems and equipment. The following steps were taken to develop our solution:
Data Gathering and Consolidation
Our first step was to perform a detailed review of an aggregate of offshore incident data to identify frequently recurring components and consequences. Using the data, we built a pragmatic equipment taxonomy based on industry standard taxonomies (e.g. ISO 14224), tailored to the implicit taxonomy reflected in incident reporting documents. After developing the equipment taxonomy, ABS Group processed the data further, identifying categories for incident types, operational activities, water depth levels, consequence types and incident severity. We also applied offshore equipment reliability data.
- Collected and examined anonymized offshore accident data
- Established an equipment taxonomy that defined the hierarchy for the analysis and terms for systems, equipment and components leveraging recognized industry practices
- Consolidated incident data from the various databases supplemented with equipment reliability data
- Performed an in-depth review of incident records from which equipment failures were identified as a causal factor or were related to failures of safety systems that exacerbated the consequences of an incident
- Categorized each incident considering a number of attributes: incident type, incident date, operational activity, function, water depth and involved equipment (using hierarchy)
Apply Data Analytics
After completing the initial data gathering and consolidation phase, we developed a statistical approach to normalize and analyze the incident and equipment reliability data. The data analytics process identified top drivers of accident occurrences and consequences, deriving results directly from the conditioned data. These results were then presented as a tiered list of prioritized systems, equipment and components.
In addition, the project served to inform a series of recommendations as to how to institutionalize the taxonomy, improve data collection and enhance the analytics approach. The effort laid the groundwork for advanced analytics techniques such as machine learning.
ABS Group developed robust, risk-based data analytics for the identification of critical components using actual loss experience. This approach provided flexibility and could be tailored to each specific facility and system design.
Instead of trying to develop and apply a prescriptive, "one-size-fits-all" single list of critical components based on failure frequency, we developed a performance-based approach to address the issue of identifying critical components considering occurrence rates and consequence contribution of equipment and component failures.