2021 SGA Award Finalists

Program Description

We created a tool to stand between our data analytics and asset management systems to create work orders based upon data analytics alarms and our asset management system.

The Event Frame Management (EFM) tool was created to stand between our data analytics and asset management systems to create work orders based upon data analytics alarms. The OSI PIsoft data analytics system tracks multiple sensors and data points from our compressor fleet and places them into a data historian. When a data point is outside of parameters it logs an event frame with an alarm that is sent to our reliability engineers/specialists. Inside the Event Frame Management tool, the recorded event frames are captured electronically and reviewed by the reliability team based upon criticality. If an alarm requires onsite technicians to repair or look further into the alarm, with the push of a button the EFM tool will create an inspection work order in our asset management system with criticality and timeframe for scheduling the inspection.

Results/Response to the Program

Work order information captured upon investigation helps refine data analytics. Reliability Engineers and Specialists no longer have to create work requests manually for these efforts and additional data from work order completion is helpful for review and future enhancements.

As a result of this EFM tool, we have significantly reduced the amount of time required to build work orders for onsite review of critical alarms. In addition, we are able to capture additional data in the work orders from onsite inspections pertinent to the alarm that we generated from the OSI PIsoft program. Our reliability team has welcomed the reduction in effort to generate work orders that were previously done by hand. Our field technicians also have a standardized format with a work order to review the issue called out by the PIsoft system. The field technicians also have an embedded problem code in the work order to help guide them in their onsite inspection.

Resulting Benefits

With completed EA-identified work order data, Southern Star can now review PI Event Frames against field inspection data. EAM provides historical work order data that can be used to reveal repeat issues on a particular asset or solutions for similar assets based upon similar PI Event Frames. EAM work order data can also be used to refine critical setpoints that create PI Event Frames to extend predictive maintenance while still ensuring asset reliability and efficiency.

From a business perspective, we now have a complete loop of information regarding our reliability alarms. When an inspection work order is generated from the EFM tool the information and problem code from the event frame alarm is embedded in the work order. The technician uses this information to guide their onsite inspection. Preset codes for Failure, Cause, and Action to repair are part of the work order closing process for the technician. As event frames are reviewed against completed work orders that information is being used to find recurring patterns of failure as well as refine critical setpoints for alarm creation. In addition, data trends are being used to improve reliability on assets of similar kind and run parameters. We now see an increase in reliability across our entire footprint as a result of this innovation.

Program Description

Stress corrosion cracking (SCC) is a form of environmentally assisted cracking and in general is the result of stress, environmental and chemical conditions including pH, in a material that is susceptible to cracking. Circumferential SCC (C-SCC) occurs when longitudinal stress, typically from ground movement or localized bending, is the major stress component. Limited assessment alternatives available for gas pipelines make management of the CSCC threat extremely challenging, potentially allowing anomalies to grow to a severity impacting the safe and reliable operation of a pipeline.

Selective digging approaches have been successful in identifying circumferential SCC. However, this approach is insufficient to predict the severity of C-SCC or confirm that all C-SCC indications have been examined. Since the major stress that results in C-SCC is longitudinal or along the axis of the pipeline, critical defects are typically greater than 80% of wall thickness and hydrostatic testing, which increases hoop stress, is not an effective method for detection.

In 2018 Xcel Energy and Novitech Inc. undertook a multi-year project to develop an advanced MFL ILI system to reliably detect and quantify C-SCC in susceptible pipeline systems. The resulting ILI system can successfully discriminate C-SCC occurrences from other pipe anomalies such as metal loss, as well as rank C-SCC severity into three categories: subcritical, significant, and severe.

Results/Response to the Program

The resulting ILI system has been implemented on over 200 miles of natural gas transmission pipelines 6, 8, and 10-inches in diameter and has successfully confirmed 78 C-SCC anomalies out of the 81 identified by the tool (3 false positives). The ILI system has a POD and POI >90% for C-SCC >30% in depth and 0.8 inches of circumferential width.

Resulting Benefits

This innovation provides natural gas pipeline operators a reliable diagnostic system for the identification, management and repair of C-SCC. Prior to the development of this technology no assessment options existed to allow natural gas pipeline operators to identify and repair C-SCC anomalies prior to them reaching a severity that impacted the safe and reliable operation of the pipeline.

For Xcel Energy, in one specific scenario, this technology allowed the company to return a pipeline to full operation that was previously isolated at a reduced pressure with the only alternative being to pipeline replacement.