Pipeline Supervisory Control With Dynamic Models for Faster Set-Points
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Solution Overview
Problem
Conventional SCADA systems in oil and gas midstream facilities face challenges in efficiently and safely managing fluid pipeline operations due to delays in control actions affecting multiple points in the pipeline, laborious manual processes, and increased risk of errors from operator fatigue or inexperience, which can lead to unsafe and inefficient operations.
Innovation Solution
The implementation of a control system that automatically calculates and implements optimal control actions to achieve desired flow rates, reduces manual operations, and incorporates fully dynamic models to predict and manage pipeline conditions, allowing for simultaneous execution of commands across all control points and preemptive actions to mitigate faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual control actions are taken at one point in the pipeline, then the operator can adjust operating conditions, but delays occur before effects are observed at other points in the system
Solution Approach 1:
The system performs preliminary calculations and predictions of control action effects before actual implementation. The virtual twin simulates and predicts outcomes of control actions, allowing operators to plan and prepare control strategies in advance, reducing the effective response time when actions are implemented.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the physical pipeline is constantly compared with the virtual twin simulation. This real-time feedback allows the system to automatically adjust control actions and provide predictive information to operators, eliminating delays in observing control effects.
2Productivity
If more pump/compressor stations are added to the pipeline system, then transport capacity increases, but the complexity of monitoring and control increases
Solution Approach 1:
The system creates a virtual copy (digital twin) of the entire pipeline system including all pump/compressor stations. This virtual model replicates the complex system behavior, allowing simulation and analysis without adding physical complexity. The virtual twin serves as a testbed for control strategies, reducing the operational complexity burden on the actual system.
Solution Approach 2:
The virtual twin system serves multiple functions simultaneously: it monitors system state, predicts control outcomes, trains operators, optimizes operations, and provides diagnostic capabilities. This multi-functionality consolidates what would otherwise require multiple separate complex systems into a single unified platform.
3Ease of operation
If control actions are implemented manually step-by-step, then operator control is maintained, but the number of manual operations increases significantly
Solution Approach 1:
The system enables semi-autonomous operation where the virtual twin automatically calculates optimal control actions and presents them to operators for approval. The system serves itself by performing the computationally intensive simulation and optimization tasks, freeing operators from manual step-by-step control while maintaining supervisory oversight.
Solution Approach 2:
The virtual twin acts as an intermediary between the operator and the physical pipeline system. It translates operator intent into optimized control sequences, simulates outcomes, and presents recommended actions. This intermediary layer automates the complex calculation and coordination tasks while preserving operator decision-making authority.
4Reliability
If the pipeline system operates under unexpected disturbances, then rapid response is required, but manual response time is insufficient
Solution Approach 1:
The system continuously runs simulations in the virtual twin to predict potential disturbance scenarios and their outcomes before they occur in the physical system. By pre-calculating response strategies for various disturbance scenarios, the system is prepared to immediately implement or recommend appropriate control actions when actual disturbances occur, dramatically reducing response time.
Solution Approach 2:
The real-time feedback mechanism continuously monitors physical system parameters and compares them with virtual twin predictions. When disturbances are detected, the system automatically triggers predictive simulations and presents rapid response recommendations to operators, enabling faster response than manual monitoring alone could achieve.
Data Source
AI summary
In a system and method for supervisory management of fluid pipeline/LNG plant/gas plant/refinery/offshore oil and gas platform allowing simultaneous execution of commands at all control points, significantly increasing the speed at which an optimal set-point can be achieved in comparison to manual entry of commands. The pipeline/LNG plant/gas plant/refinery/offshore oil and gas platform control system has a cascade control configuration that can operate in conjunction with existing pipeline/LNG plant/gas plant/refinery/offshore oil and gas platform protection systems. The control room operator can activate automatic operation via the supervisory management system, and can subsequently command that the system switch back to manual control instantaneously. Dynamic models predict operating conditions of pipeline/LNG plant/gas plant/refinery/offshore oil and gas platform processes subject to constraints on pressure and other operating parameters. A steady-state optimization layer, operating in conjunction with real-time control, determines optimal states without operator intervention.