LNG Plant Operations and Surveillance

Overview

Our team developed PI Vision based Operations Surveillance screens for a large LNG Facility. The screens utilized PI AF Templates developed for repeating the process for future Trains being planned. The BKOai staff developed PI AF templates for the GAS Inlet skids, Warm and Cold SideTrains, Gas and Steam Turbines along with all utilities.

Project Duration: 1 years

Challenge

Contracted resources did not complete the project as planned, the timeline only allowed for 5-6 months prior to first LNG introduction. Additionally, the PI tags required were not all available.Previous PI AF database was incomplete and required complete rework.

Solution

Our team identified required P&ID drawings for each area of the terminal. PI AF databases were developed with Templates for the equipment hierarchy. Additional PI AF templates were created to track Startup events such as Propane Truck unloading, Gas Turbine operations and LNG TankLoad and Unload Operations. Missing PI tags were added to the PI AF data base once they were included in the PROD PI server.

Volumetric calculation for Storage Tanks were developed to augment the Tank Management System which was isolated in the Control Room.

A schema of Level 1 (Summary) Level 2 (area overview with some PI data) and Level 3 (Full detail) was used. Every stakeholder had inputon their respective area screens. In some cases, the DCS screens were mimic to provide remote Operations view of the control room.

Additionally, the client was also using SeeQ for Analytics.PI AF based element hierarchy were tuned and presented to the SeeQ instance toprovide a single data source.

Key Benefit

• Improved operational visibility across long distances: Enabled quick, focused surveillance for trains and LNG loading between the control room, admin/engineering building, and berths.
• Earlier anomaly detection: Rate-of-change calculations provided rapid indication of abnormal operating conditions.
• Faster future train deployment: PI AF templates and asset hierarchy can be copied from Train 1 and adapted rapidly for Train 2—no rebuild required.
• Standardized, scalable PI Vision build: Screens were created using Asset Context and Collections for child elements, enabling maintainable expansion.
• Consistent plant context across tools: The same PI AF hierarchy supports both PI Vision operations screens and Seeq analytics, reducing confusion and improving adoption.

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