A BIM-based approach for predicting corrosion under insulation
Introduction
Corrosion is one of the most common damages of pipelines for oil and gas transmission. According to the statistics [1], 25% of the pipeline incidents attributes to corrosion over the last ten years. These damages could cause a waste of gas which leads to a great amount of financial loss. In fact, it is indicated that the expenditures of potential repairs and monitoring for pipeline corrosion cost billions of dollars globally each year [2]. Furthermore, corrosion damages could influence the transmission of oil and gas and cause pipeline failures [3]. According to Cosham et al. [4], when corrosion failures on pipelines occur, it is not a simple ‘corrosion’ failure, but a ‘corrosion control system’ failure which ageing coating, aggressive environment, and rapid corrosion growth may need to be monitored. Therefore, approaches for efficiently managing external corrosion on pipelines are eagerly needed.
Pipeline corrosion is an electrochemical reaction and the main component of the corrosion process is the electron transfer which electrons relocate in an aqueous media. Monitoring the voltages and currents of electron transfer is one of the common methods for corrosion integrity management [5]. In practice, pipelines are inspected periodically by non-destructive test (NDT) tools such as in-line inspection (ILI) tools to identify potential corrosions. The corrosions could be located through different techniques such as ultrasonic transducers and magnetic flux leakages [6,7].
However, monitoring plant sites with the aforementioned inspection tools could cost a lot of manpower and time as the tools interrupt the sites from producing high-value products. In fact, many research studies focus on modeling corrosion defects to highlight potentially corroded elements. Implementing reliability-based assessment of corrosion model is one of the significant approaches. Rather than extrapolating the corrosion depth or corrosion rate mainly from site experience, a reliability-based model could consider several factors such as site condition and sensor values which provides an accurate reference while determining an inspection plan [8]. These corrosion models are various and could be different from assumptions. For instance, deWaard et al. [9] developed a corrosion model considering the effect of temperature and carbon dioxide. In the following research, several correction factors are added to enhance their result. Another model proposed by Nesic et al. [10] focuses on complex effects such as protective scale formation, water wetting, and the hydrogen sulfide effect in order to solve the multiphase flow problem. Additionally, Nyborg [11] indicates that there are a considerable amount of corrosion models accounting for oil wetting and the effect of protective corrosion films. In short, while highlighting potential corrosion defects, it is important to choose a model which fits the site environment and the chemical reactions.
Smart sensing technology enables the data streaming from sites to the office which makes possible for monitoring sites and providing proper inputs for corrosion models. This technology has been implemented on pipeline monitoring in the industry due to its main features: little processor, small size, wireless, and low-cost [12,13]. Smart sensors are able to collect data on site and send the data back to stations wirelessly with the low installation and maintenance cost [14]. In terms of the communication mechanisms, Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) are the two common systems used in smart sensing [15]. WSN consists of several sensor nodes which can use Bluetooth, Wi-Fi, or ZigBee to communicate. On the other hand, RFID systems are formed of active or passive RFID tags and RFID readers [16]. Because of different demands of monitoring on sites, such as low cost, long read range or long durability, these systems have been implemented in the industry in different scenarios [17]. In short, smart sensing technology is a reliable method and widely used in industry to steam data from site to office.
Building Information Modeling (BIM) technology has been implemented as an integration platform to serve multiple sources of data. Generally, these data could be categorised to graphical and non-graphical data [18]. 2D drawings and 3D models are categorised as graphical data, while documents, sensor values, or materials are categorised as non-graphical data. All the aforementioned information can be stored or referred to BIM models. In fact, many research studies have proved that using BIM techniques to integrate project data is beneficial [[18], [19], [20]]. Sensor data and related maintenance documents from various stakeholders could be integrated and a comprehensive interface could be provided for decision makers while corrosion management. In fact, BIM technology is widely used during construction to visualise multiple sources of data, and it is proved that the efficiency of information delivery could be improved [21]. Sensors can be modeled or highlighted on BIM models to present the location on site. Besides, non-graphical data such as documents or sensor values could be stored or linked to BIM elements. Then, the elements in BIM model could be colour-coded corresponding to non-geometric data. Managers could understand the critical area on the model without reviewing each data on elements. This presents a global view of the data on site. Briefly, BIM technology could serve as an integration platform for data including but not limited to documents, models, and sensors which offers decision makers another way of understanding the data.
This paper proposes an approach for predicting corrosion under insulation by utilising smart sensing, mathematical model of corrosion prediction, and BIM technology. Relative research regarding the corrosion prediction and the integration of RFID and BIM technologies will be introduced. Then, a corrosion prediction framework is designed which contains three modules including data collection module, corrosion prediction module, and inspection module. The process of how users interact with the system is illustrated. Besides, a system complied with the proposed framework is developed to validate its feasibility. A case study is adopted to demonstrate the proposed approach. To sum up, a novel approach for predicting corrosion under insulation is proposed and thoroughly introduced in this paper.
Section snippets
Related research studies
Two of the major topics are reviewed including approaches of corrosion prediction and research about integration of BIM and sensing technologies.
A BIM-based approach for predicting corrosion under insulation
An approach for predicting corrosion under insulation is proposed in the aspect of the system framework and the interactions between stakeholders and the system.
System design and implementation
A system has been designed and developed to validate the feasibility of proposed framework. A training site from one of the biggest oil and gas company in Australia is chosen to be the testbed. It could serve as the main area where the sensor data can be collected. Although sensors are not deployed on site at this stage, capability assessments of sensors are conducted. As a result, the read range, read angle, and sensor data from simulated environment could be acquired. These help determine a
Conclusions, limitations, and future works
Corrosion under insulation has been an issue in petroleum industry for decades. An approach for managing corrosion is eagerly needed to save the maintenance cost. This research proposed a novel approach to predict corrosion under insulation by implementing the sensing technology and mathematical model of corrosion prediction into a BIM-based system. The sensor values and analytic results of corrosion prediction could be visualised on BIM models. As a result, inspection areas could be decided
Acknowledgements
This work was undertaken with the benefits of a research project sponsored by Kaefer Integrated Services (Project Name: Pipeline External Corrosion Monitoring and Prediction through Passive RFID and BIM Integration).
References (36)
- et al.
Interaction of corrosion defects in pipelines–part 1: fundamentals
Int. J. Press. Vessel. Pip.
(2016) - et al.
Best practice for the assessment of defects in pipelines–corrosion
Eng. Fail. Anal.
(2007) - et al.
A review on pipeline corrosion, in-line inspection (ILI), and corrosion growth rate models
Int. J. Press. Vessel. Pip.
(2017) - et al.
Structural reliability of concrete bridges including improved chloride-induced corrosion models
Struct. Saf.
(2000) - et al.
Smart technologies with wireless sensor networks
Procedia Soc. Behav. Sci.
(2015) - et al.
Wireless sensor networks in oil and gas industry: recent advances, taxonomy, requirements, and open challenges
J. Netw. Comput. Appl.
(2018) - et al.
Artificial neural network models for predicting condition of offshore oil and gas pipelines
Autom. Constr.
(2014) - et al.
CoSMoS: a BIM and wireless sensor based integrated solution for worker safety in confined spaces
Autom. Constr.
(2014) - et al.
A BIM-and sensor-based tower crane navigation system for blind lifts
Autom. Constr.
(2012) - et al.
A knowledge-based BIM system for building maintenance
Autom. Constr.
(2013)
Validation of corrosion rates measured by the Tafel extrapolation method
Corros. Sci.
Gas pipeline incidents 10th report of the European gas pipeline incident data group (period 1970-2016)
Recommended Practice: Control of External Corrosion on Underground or Submerged Metallic Piping Systems
Magnetic flux leakage (MFL) technology for natural gas pipeline inspection
Nondestruct. Test. Eval. Int.
An overview of major methods for inspecting and monitoring external corrosion of on-shore transportation pipelines
Corros. Eng. Sci. Technol.
Predictive model for CO2 corrosion engineering in wet natural gas pipelines
J. Sci. Eng.
A multiphase flow and internal corrosion prediction model for mild steel pipelines
CO2 corrosion models for oil and gas production systems
Cited by (88)
Digital twin for indoor condition monitoring in living labs: University library case study
2024, Automation in ConstructionResearch on the electrical conductivity and mechanical properties of copper slag multiphase nano-modified electrically conductive cementitious composite
2022, Construction and Building MaterialsMolecular interfacial properties and engineering performance of conductive fillers in cementitious composites
2022, Journal of Materials Research and TechnologyCitation Excerpt :To suit the various and complex working fields, cementitious composites with these substitute materials should not only own excellent mechanical properties but also possess functionality for different applications [13–15]. For example, fillers with specific electrical conductivity, such as steel slags (SS) [16], ground granulated blast-furnace slag (GGBS), and graphite powder (GP) [17], were employed as partial replacements for cement clinkers to make electrically conductive cementitious composites (ECCC), which has a wide application in traffic detection, structural health monitoring and pavement deicing [18–20]. The mechanism to achieve these benefits derives from the piezoresitivity effect, laying foundations for measurements of the altered electrical resistivity as well as strain or stress changes in structures [21].
Corrosion of CK10 Steel Pipes Used at the Entrance of Water and Gas Condensate Separation Unit from Natural Gas
2024, Journal of Failure Analysis and PreventionCorrosion Under Insulation for Hot Structural Components
2024, Engineering MaterialsApplication of six neural network-based solutions on bearing capacity of shallow footing on double-layer soils
2023, Steel and Composite Structures