Data-driven digital twin method for leak detection in natural gas pipelines

Leak detection in natural gas pipelines is an extremely important and persistent problem in the oil and gas industry. The construction of accurate physical models of pipelines is limited by the high complexity, unavailable closed-form solution, and strict experienced personnel requirements. Moreover, industrial automation has the common problems of large amount of data with little information. This paper proposes a data-driven digital twin (DT) method for leak detection as a new paradigm solution to these challenges. From the perspective of knowledge-based data-driven, a DT pipeline learning and updating scheme based on normal data directly from operational data during the entity pipelines life-cycle is proposed to enhance DT adaptability. A DT-driven leak detection method is proposed, making effective use of data interaction and fusion of DT. The effectiveness and performance of the proposed approach is illustrated by deploying the DT pipeline in a simulated leak scenario of a real running natural gas pipeline. & COPY; 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd

Automated summary

Leak detection in natural gas pipelines is a significant and ongoing problem in the oil and gas industry. The complexity of pipeline construction, lack of closed-form solutions, and the need for experienced personnel limit the creation of accurate physical models. Additionally, industrial automation faces challenges with data overload and limited information. This paper proposes a data-driven digital twin (DT) method as a new solution to address these challenges. The proposed approach includes a DT pipeline learning and updating scheme based on operational data and a DT-driven leak detection method that utilizes data interaction and fusion. The effectiveness of the approach is demonstrated through a simulated leak scenario in a real running natural gas pipeline.