TALKING

A centralised operations data management platform uses standardised and templatised tag-naming conventions .

Quickly identifying pipeline leaks is key to minimising risks and preventing major spill overs , and many leaks go undetected by a single solution . There is no one-size-fits-all solution , and multiple detection technologies are often needed to detect different types of leaks that exhibit dissimilar flow patterns . Analytics can simulate liquids and gas flow and detect any subtle changes that could point to a pipeline leak . conventions and assets are catalogued in a flexible hierarchy . This platform becomes an operational system of record , creating the foundation to democratise insights across any pipeline business model .

Using the data model , companies can accelerate digital transformation by combining operational data into a digital replica of physical assets . This can be enabled by developing an digital twin of the entire system using information such as drawings , 3D models , materials , engineering analysis , dimensional analysis , real-time pipeline data , and operational history .

During the operational life cycle , the digital twin is updated automatically , in real time , with current data , work records , and engineering information , to optimise maintenance and operational activities . Engineers and operators can easily search the asset tags to access critical up-to-date engineering and work information in order to diagnose the health of a particular asset .

Previously , such tasks would take considerable time and effort , and issues were often missed , to failures or pipeline outages . With the digital twin , operational and asset issues are flagged and addressed earlyon and the workflow becomes proactive instead of reactive . Pipeline companies can easily benchmark operational performance , such as pipeline throughput and energy consumption , to uncover gaps and improve pipeline efficiencies .

Use cases for pipeline data

Advanced simulation and analytics tools can be used to model and predict fluid flows in pipeline . This not only allows product and batch tracking and line pack , but also helps uncover improvement of throughput in existing assets . This improved visibility into operations enables pipeline operators to optimise throughput . What ’ s more , they can plan for future infrastructure expansion to increase efficiencies and throughput to improve competitive advantage .

Advanced simulation tools can be used to model gas flow behaviours and predict loads for current and future gas days in near real time . These insights enable pipeline operators to better balance supply and demand , optimise capacity , and better adhere to gas contracts .

Training controllers are critical for ensuring operational safety and integrity while making sure all operations are adhering to a pipeline operators ’ safety and compliance program . The pipeline training simulator is an operator training system that allows pipeline controllers to train on normal and abnormal operating scenarios in a safe and realistic environment .

Operators can navigate actual pipeline operations and receive certifications before they assume the roles and responsibilities as a controller in a live operating environment . Major oil and gas operators have significantly reduced training costs and time to proficiency by using simulators as part of enterprisewide training programs .

The accurate measurement of volumes is extremely important to ensure correct and timely accounting , both internally and at custody transfer points , and pipeline companies must use available data to improve accuracy .

Through analytics , expert systems , and artificial intelligence , AI , future operational systems will become even more automated , from the scheduling through to product delivery . This level of autonomous control will be enabled by better data for operator decision making , followed by recommendations and what-if scenarios , and will gradually result in automated control during normal , and eventually abnormal , operations .

Pipeline companies are starting to make use of action sequences which allow canned sets of commands chained together with pre-and postaction verification to ensure the commands are executed safely and correctly . Utilising pipeline data analysis to drive actions using a rule-based approach can enable pipeline companies to improve outcomes when performing operations where timing of the actions is critical .

The oil and gas pipeline industries are under tremendous pressure to adhere to constantly changing regulatory requirements , increase agility to succeed in a rapidly changing business climate , protect cybersecurity concerns , and meet global sustainability goals . p

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