The results of our research indicate that the proposed methodology helps evaluate pipeline conditions and in assessing strategies for preventive maintenance. The research objectives of this study are: (1) to examine the current practices for pipeline inspection (2) to create a model for integrating artificial intelligence and drone technologies for image data collection (3) to develop deep learning algorithms for detecting the targeted problems from collected image data and (4) to develop a resilience assessment model. This solution is particularly aimed at identifying the targeted root causes of pipeline failure-misalignment and deterioration of supporting structures-and assessing the pipeline system resilience by processing image data. To this end, this study proposes DARTS (drone and artificial intelligence reconsolidated technological solution) to monitor and inspect pipeline conditions and support preventive maintenance of pipelines. Meanwhile, a large portion of failures can be prevented by increasing pipeline system resilience through timely problem detection and preventive maintenance. Pipelines can fail due to various causes, such as corrosion (internal and external), pipeline misalignment, deterioration of support structures, overgrown vegetation, equipment failure, improper operation, and other external forces (Fazzini et al. ( 2014) stated that more than USD 3.1 billion worth of natural gas was lost and unaccounted for annually between 20 in the United States. Apart from the property damages and environmental loss, Jackson et al. According to PHMSA, about 12,505 pipeline incidents occurred in the United States between 20, resulting in 270 fatalities, 1176 injuries, and USD 9.9 billion in property damages (PHMSA 2021b). Despite their indispensable role, pipelines are vulnerable to various types of risk. Natural gas consumption in 2020 increased by approximately 21% to 30.4 trillion cubic feet, compared with 24.08 trillion cubic feet in 2010 (EIA 2021). In the present era, the role of pipelines is crucial as the consumption of natural gas rises every year. energy commodities across the nation (PHMSA 2021a). The oil and gas pipelines, which typically serve a greater transportation demand than trucks and trains can handle, are responsible for moving about 64% of the U.S.
There are currently 2.8 million miles of regulated pipelines in the United States alone (PHMSA 2021a).
Pipelines are the primary assets of the oil and gas midstream industry. The test results show that DARTS can be effectively used to support decision making for preventive pipeline maintenance to increase pipeline system safety and resilience. This solution is aimed to detect the targeted potential root problems-pipes out of alignment and deterioration of pipe support system-that can cause critical pipeline failures and predict the progress of the detected problems by collecting and analyzing image data periodically. This research proposes a drone and artificial intelligence reconsolidated technological solution (DARTS) by integrating drone technology and deep learning technique. Currently, pipeline inspection is performed at pre-determined intervals of several months, which is not sufficiently robust in terms of timeliness. Thereby, it is increasingly imperative to monitor and inspect the pipeline system, detect causes contributing to developing pipeline damage, and perform preventive maintenance in a timely manner. The need for safe operation and effective maintenance of pipelines grows as oil and gas demand rises.
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