A BIM-based approach for predicting corrosion under insulation

Highlights

• Integrate BIM and sensors for corrosion prediction and visualisation.

• A mathematical model has been developed for corrosion prediction.

• A prototype has been developed to validate the efficiency of the proposed approach.

Abstract

Corrosion under insulation is one of the most important issues in the petroleum industry. Ordinarily, in order to check the corrosion, inspectors remove the insulation of pipelines to measure the level of corrosion on each section of pipelines. This procedure may take weeks for a site which distinctly affects the financial aspect of oil and gas companies due to the pause production of its high-value products; therefore, in most cases, inspectors spot-check pipeline corrosion based on their experience. However, because the environments on sites are various, experience-based inspection may not be suitable for every site. On the other hand, even though inspectors want to access more data for better understanding of the site before the site trip, historical data sometimes are lost or scattered which leads to a hard situation for preparation of corrosion inspection. This paper utilises passive RFID sensors, which are smart sensing technologies, to collect site data and then integrate them into a Building Information Modeling (BIM) system. A uniform corrosion model is also adapted from the theories of corrosion to leverage both sensor data and BIM elements' properties. They serve as inputs to calculate the corrosion rate which is the key value of corrosion prediction. Then, the corrosion prediction results are colour-coded on a BIM model which helps inspectors intuitively understand the prediction and prepare for the site inspection. In result, the proposed research could provide a novel approach for corrosion management 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.