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Probabilistic Digital Twins For Transmission Pipelines

Product Number: 51321-16780-SG
Author: Francois Ayello; Yonghe Yang; Long Li; Guanlan Liu; Yuchong Zhang; Shuhui Zhang
Publication Date: 2021
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$20.00
$20.00

Digitalization in the oil and gas industry has led to the formation of digital twins. Digital twins bring closer the physical and virtual world as data is transmitted seamlessly between real time sensors, databases and models. The strength of the digital twin concept is the interconnectivity of data and models. Any model can use any combination of inputs (e.g. operator owned data sets and sensors, third-party databases such as soil composition or weather data, results from other models such as flow assurance, threat modelling or risk modelling). Consequently, the result of one model may become the input of another. This strength is also a weakness, as uncertain (or missing data) will lead to a great source of uncertainty and may lead to wrong results. Worst case scenarios have been used to solve this issue without success. This paper presents a new concept: probabilistic digital twins for pipelines. Probabilistic digital twins do not lose uncertainty as results pass from one model to another, thus providing greater confidence in the final results. This publication reviews the probabilistic digital twin concept and demonstrates how it can be implemented using gas pipeline data from West Pipeline Company, CNPC.

Digitalization in the oil and gas industry has led to the formation of digital twins. Digital twins bring closer the physical and virtual world as data is transmitted seamlessly between real time sensors, databases and models. The strength of the digital twin concept is the interconnectivity of data and models. Any model can use any combination of inputs (e.g. operator owned data sets and sensors, third-party databases such as soil composition or weather data, results from other models such as flow assurance, threat modelling or risk modelling). Consequently, the result of one model may become the input of another. This strength is also a weakness, as uncertain (or missing data) will lead to a great source of uncertainty and may lead to wrong results. Worst case scenarios have been used to solve this issue without success. This paper presents a new concept: probabilistic digital twins for pipelines. Probabilistic digital twins do not lose uncertainty as results pass from one model to another, thus providing greater confidence in the final results. This publication reviews the probabilistic digital twin concept and demonstrates how it can be implemented using gas pipeline data from West Pipeline Company, CNPC.

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Corrosion Data Management Using 3D Visualisation and a Digital Twin

Product Number: 51320-14535-SG
Author: Robert Adey, Cristina Peratta, John Baynham
Publication Date: 2020
$20.00

There is a gap between the Integrity management systems used by companies to manage their assets and the needs of the CP engineer. Integrity management systems do not fully meet the needs of the engineer responsible for corrosion as they do not provide access and visualizations of all the data the engineer needs to make fast and informed decisions. There is also often no easy way to see the trends in the data, or easily access the relevant video and photographic data also recorded during the survey.

Data from surveys is normally contained in reports and EXCEL spreadsheets often with different measurement locations and inconsistent naming of the locations between reports. In this paper a system is introduced which enables engineers to manage and visualise in 3D CP survey data and provide access to all the relevant information through a 3D visual interface to any member of the teams. The software gives the engineer the ability to visualize in 3D the historical and predicted CP protection on the structure and the status of the anodes in the CP system. It also provides information on long term trends in the survey data.

By integrating the corrosion data with a simulation model a “digital twin” of the structure can be created to make predictions of the present and future protection of all parts of the structure. For example the engineer can easily use the software to systematically monitor the differences between the model predictions and survey data to identify anomalies and give early identification of problems which will require action.

The paper will describe the system developed and present applications of both the 3D corrosion data visualisation and the simulation based digital twin