Natural gas transportation comprises a complicated network of pipes that span large areas. Natural gas leaks cause human deaths and significant financial losses and contribute to global warming. According to a recent assessment, natural gas leaks claimed approximately 100 lives, cost $1.1 billion, and emitted 17.55 billion cubic feet of methane gas in the United States alone between 2010 and 2017.
To counteract corrosion that causes gas leaks, expensive methods, including special coating & cathodic protection, are required. Traditional maintenance methods like “run to failure” and “time-based maintenance” are inefficient and unreliable. Due to this, old methods of doing things must be reviewed, and fresh alternatives with predictive maintenance leveraging new-age technology must be explored.
The white paper seeks a solution for gas distribution pipeline vulnerability assessments and maintenance management by implementing a digital twin approach.
Gas Distribution Pipeline System
In the current industrial scenario, the pipeline system is one of the most vital infrastructures for the natural gas industry. The safety and security of this infrastructure are essential to protect life and property. Numerous accidents in gas distribution pipelines have occurred due to various reasons such as third-party damage, equipment failure, material defects, human error, etc. Gas utilities use GIS systems to simulate pipeline networks.
Gas pipelines are modelled using standard data models such as the utility and Pipeline Data Model (UPDM) and Pipeline Open Data Standard (PODS). These are open-source, extendable standards for modelling various pipeline systems, including gas transmission, oil pipelines, hazardous liquid pipelines, and distribution pipelines. IoT analytics and IoT machine learning are used to monitor the gas distribution pipelines.
Industry NeXT is a cognitive ecosystem that enables sharing of real-time intelligence across digitally interconnected systems and stakeholders, powered by a fully integrated digital thread. This is achieved through advanced technologies such as digital twins, 5G/AR/VR, hyperscalers, Blockchain and more, that enable the modelling, identification, and analysis of vulnerabilities and opportunities to capture value iteratively and quickly – feeding off the shared intelligence.
Techniques and Challenges
- Existing approaches for pipeline vulnerability and pipe section maintenance face the following issues, affecting overall system efficiency and operational dependability.
- Detecting pipeline concerns poses operational hurdles.
- Surveillance and maintenance of pipelines
- Pipe section evaluation and prioritizing for replacement programs. HCLTech can help overcome these challenges with their valuable services.
The suggested approach is to create a digital twin model of the gas pipeline system. Identifying the impacted business process is the first step in developing a Digital Twin. The SCADA system monitors gas stations and other essential infrastructure, including specific pipeline portions. The control room operators watch the alarms, manually examine the problem, and decide on a course of action, including cancelling false alarms, starting control actions in SCADA, scheduling a crew visit, or directly drafting a work order.
Pipeline sections are given inspection and maintenance schedules per their risk profile and operational requirements. The inspection data, along with the IoT machine learning, is used for updating the digital twin model. It will enable condition-based maintenance and improve the decision-making process.
Modelling of a Digital Twin
GIS is a digital depiction of a pipeline network and the primary data source for generating a Digital Twin (together with EAM or other external entities). Every pipeline section between two terminals is given a Feature id in GIS. The junction might be an angled bracket, a welded joint, a T-junction, or a device mounted on the pipeline. Moreover, big data technologies are used to monitor the gas distribution pipelines.
Assumptions & Considerations
The significant assumptions and considerations include:
- All pipe sections from offtakes to individual customer premises are intended to be modeled in GIS. Without it, the system will be inadequate, affecting the quality of the insights provided.
- One of the most severe difficulties faced by gas utilities is that the GIS model doesn’t accurately portray the physical network. Because GIS is the primary source of network models, it’s critical to start there.
- It is anticipated that seamless integration would be built across essential applications, allowing specific pipeline segments to be identified across all of these systems.
- The quality of the insights gained varies all over the pipeline system since different system areas are monitored differently.
- The benefits of building a digital duplicate of the piping system and connecting with other IT/OT systems are undeniable.
- Gas utility firms can begin by developing a prototype Digital Twin model for a small piece of the gas pipeline network. Gradually expand to include additional sections of the pipeline.
- The technique can subsequently be expanded to include equipment mounted on pipelines integrated to form a single system.