In order to support its goals of zero incidents through risk-based asset management, Access Midstream (Access) requires a solution for providing decision support by modeling integrity threats that indicate potential safety and environmental incidents on all of its pipeline assets. The objective is to tailor existing pipeline risk methods to properly account for the unique conditions inherent to midstream gathering assets while still being flexible enough to account for traditional gas transmission and liquids transportation lines.
Access has created a unique risk-based spatial decision support system (RB-SDSS), customized to the needs of a gas gathering company that also owns and operates incidental gas transmission and hazardous liquids pipelines. Access’ business needs call for a comprehensive solution that includes a risk model that can handle a diverse asset base. Most of the company’s pipelines are unregulated, but risk-based management and decision-making are still desired for regulated and non-regulated pipelines alike. Access has designed a decision support system that automatically retrieves and integrates data, estimates risk with directly and indirectly related inputs in its algorithm, and disseminates the data to business users across the enterprise. The processing model is calibrated for risk assessment and is capable of distinguishing between high and low risk lines. It is able to generate results that can be understood and upon which appropriate action can be taken. These results are refreshed monthly as new assets are constructed or purchased and existing asset attributes are updated. Access’ decision support system is repeatable and scalable owing to maximum use of automated processes.
The focus of this paper is to outline Access’ approach to building a risk assessment solution for its pipeline assets. Each component of the RB-SDSS is reviewed: (1) Data Integration, (2) Risk Modeling, and (3) Organizational Utilization. This includes strategies for assessing traditional pipeline threats, geospatial techniques used to infer data, how to assess data quality and completeness, challenges, lesson’s learned, and future improvements.
A Digital Twin for Risk Modeling and Decision-Support in a Smart Energy Grid