Next Article in Journal
EBSD Characterization of 7075 Aluminum Alloy and Its Corrosion Behaviors in SRB Marine Environment
Next Article in Special Issue
Lateral Buckling of Subsea Pipelines Triggered by Sleeper with a Nonlinear Pipe–Soil Interaction Model
Previous Article in Journal
A Time-Domain Analytic Solution of Flow-Induced Undular Bores
Previous Article in Special Issue
Prediction Method and Validation Study of Tensile Performance of Reinforced Armor Layer in Marine Flexible Pipe/Cables
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JMSE
Volume 10
Issue 6
10.3390/jmse10060739
jmse-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines

by
Bai-Qiao Chen
1,
Paulo M. Videiro
1,2 and
C. Guedes Soares
1,*
1
Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
2
Laboratory for Relaibility Analysis of Offshore Structures (LACEO), COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-596, Brazil
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2022, 10(6), 739; https://doi.org/10.3390/jmse10060739
Submission received: 17 April 2022 / Revised: 20 May 2022 / Accepted: 23 May 2022 / Published: 27 May 2022
(This article belongs to the Special Issue Subsea Pipelines)
Browse Figures
Versions Notes

Abstract

:
A vision of the digital twins of the subsea pipelines is provided in this paper, with a coverage of the current applications and the challenges of the digital twins in the design, construction, service life, and assessments of life extension. Digital twins are described as a paradigm combining multi-physics modelling with data-driven analytics, which are used to mirror the life of its corresponding twin. Realistic virtual models of structural systems are shown to bridge the gap between design and construction and to mirror the real and virtual worlds. Challenges in properly using the new tools and how to create accurate digital twins considering data acquired during the construction phase are discussed. The key opportunities for improved integrity management using the digital twin are data contextualization, standardization, automated anomaly detection, and learning through sharing. The collection, interpretation and sharing of data, and cyber-security are identified as some of the main challenges.

1. Introduction

Subsea pipelines are the flowlines connecting a subsea wellhead to a manifold or a platform, or the export lines (trunk lines) used as a long-distance transportation system for the oil and gas [1,2]. Important considerations of the pipeline system are the efficient operation and the development of the system for future needs. Due to the low price of oil in recent years, oil and gas field operators are looking beyond the traditional operational maintenance strategy for the sake of reducing the downtime caused by the planned or sometimes unplanned preventive maintenance in the production field, thus reducing the operational cost (OPEX) [3,4].
In the digital solutions to subsea integrity management (SIM), the use and management of data bring benefits to the daily operations, e.g., the increased efficiency, optimization, reduction in cost, and safety [3]. New technologies and associated processes are required for increased safety and better monitoring of the subsea assets.
Recent advancements in information and communication technologies, including cloud computing, high-performance processors, high dimensional visualization capabilities, internet of things (IoT), wearable technologies, additive manufacturing (AM), big data analytics (BDA), artificial intelligence (AI), autonomous robotic systems, submarine drones, and blockchain technology, have catalysed digital adoption across industries. These new technologies have facilitated the cyber-physical integration by which data can be collected, analysed, and visualized to make informed decisions and to serve as a basis for simulations to optimize operations [5]. The concept of cyber-physical interaction and associated simulation is referred to as a Digital Twin.
The digital twin draws considerable interest as it can provide a cost-effective, reliable and intelligent maintenance strategy based on the machine learning (ML) algorithm [4] and assess extension life projects. The digital twin concept consists of three components: (1) the physical asset, (2) the virtual representation of the asset, and (3) the connection between the previous two components. The connection includes the information transferred from the physical asset to the digital twin and from the digital twin to the asset. A clear business purpose is a key principle in the development of digital twins to provide value [3].
The concept of the digital twin has also been used in offshore structures as platforms and subsea pipelines throughout the stages of construction and operation life. In this paper, the vision of the digital twin and its evolution, and the applications of the digital twin in subsea pipelines in design, construction and service life are reviewed. Further discussions and comments are also addressed.

2. Literature Review

2.1. Digital Twin

A digital twin is defined as one virtual representation of a system (or an asset) that can be used to calculate the states of the system and to make the information available. The integrated models and data of the digital twin can provide decision support over the life cycle of the system or asset. The idea to use a twin model can be dated back to the 1970s when two identical space vehicles were built in NASA’s Apollo program to allow for mirroring of the conditions of the space vehicle [6].
Although the concept of digital twins was first put forward in 1991 [7], the model of the digital twin was first introduced in 2002 as a concept for product lifecycle management (PLM). After its initial names of mirrored spaces model (MSM) and information mirroring model (IMM), the model was finally referred to as the Digital Twin in 2011 [8].
The digital twin has been applied to a wide range of industries including aerospace [9,10], automotive [11,12], healthcare [13,14,15,16], manufacturing [17,18,19], and smart city [20,21]. Some early applications of the digital twin can be found in NASA’s spacecraft [22,23,24] and in the US Air Force’s jet fighters [25]. More recently, world-class vendors such as Dassault Systèmes, PTC, and Siemens use the digital twin concept in their PLM. The digital twin model was also proposed for the robust deployment of IoT [26]. Aiming at developing digital twins for all built cars, TESLA enabled synchronous data transmission between their cars and the factory [27]. From 2017 to 2019, the Gartner listed the digital twin as being among the top 10 strategic technology trends and indicated that billions of things would have their digital twins within 3–5 years [28,29,30].
Although the concept of the digital twin is not new, it was more a descriptive one and lacked auxiliary technologies in the first few years [31,32,33]. Figure 1 shows the number of results obtained by searching “digital twin” as a “topic” in the Web of Science database, indicating a tremendous increase of research interest in the digital twin both in industry and academia, especially since 2017/2018. In terms of the country of study, China is the leading country in publications on the digital twin, followed by Germany and the USA.
Even though the digital twin technologies are of interest to companies in the industry, a major part of the studies (about 79%) are conducted by researchers in academia, according to a survey [34]. Most academic research focuses on improving modelling techniques rather than optimizing data and implementing digital twins. Only 6% of the articles are initiated in the industry; the remaining are collaborative studies between academia and industry. There are few connections between industry and academia, particularly due to commercial secrecy.
To meet the new requirement from applications, Tao et al. [35] presented an extended five-dimension digital twin model, adding data and services to the initial three-dimensional digital twin concept. Figure 2 shows the key technologies for modelling each dimension of the digital twin. Qi et al. [36] classified the tools for the service applications of digital twins into platform service tools, simulation service tools, optimization service tools, and diagnostic and prognosis service tools. A list of the tools for each category is shown in Figure 3.
ANSYS Twin Builder is one of the multi-physics simulation packages used to add physical realism to the digital twins [27]. It contains extensive application-specific libraries and features third-party tool integration and can enable engineers to quickly build, validate and deploy the digital models of physical assets at an appropriate level of detail.
As with all new concepts, there are also obstacles to the further application of the digital twin. For instance, it is sometimes difficult to collect the most important data from thousands of sensors that track vibration, temperature, environmental conditions, force, speed, or power. In addition, the data can be spread among different owners in various formats. Consequently, the digital twin may fail to echo what is going on in the real world, leading to poor decisions made by the managers accordingly [37]. In this regard, further research is needed for improved data collection and processing methods to implement the communication interface between the digital and the physical twins. In terms of standardization, the development of universal platforms and tools are also required for further applications of digital twins.

2.2. Subsea Pipelines and Application of Digital Twin

The first oil pipeline is widely believed to have been installed in the 1860s in the USA to transport crude oil [38]. Since then, subsea pipelines have become the most economical means of efficiently transporting crude oil, natural gas, and other products from offshore installations for the exploitation of subsea reservoirs. Figure 4 illustrates different uses of subsea pipelines associated with platforms and wells, including the infield flowlines and export pipelines.
In a harsh sea environment, sufficient structural strength is necessary for designing, analysing, and operating subsea pipelines to guarantee the safety and integrity of subsea pipelines [38,39] throughout their life span.
Pipeline design is affected by multiple factors identified during the early stages (i.e., conceptualization, front end engineering and design stage, and detailed engineering phase). These factors include the site selection, route survey, local environmental conditions, material selection, wall-thickness design, pipeline protections, and the budget of the project [40,41]. During the pipelay process, the pipelines are deposited from installation vessels, with new pipe segments welded to form the pipelines. A proper pipe-lay is required to avoid excessive bending stresses that may cause fractures and buckling. The low temperature on the seafloor can sometimes cause a global contraction of the pipeline, while heat coming up from the reservoir fluid may induce local thermal expansion of the pipeline [42,43]. Temperature or pressure changes in the operation process can also cause “pipeline walking” [44,45] in the case of improper restraints of the pipelines. Corrosion and erosion, which occur due to chemical attacks and abrasion from the internal fluids, are one of the major limiting factors in the continued operation of subsea pipelines [46,47,48,49,50]. These factors necessitate the routine inspection of subsea pipelines and the use of monitoring technologies.
According to the B31G code [51] of the American Society of Mechanical Engineers (ASME), the geometry of the corrosion pits can be idealized as elliptical shapes, and a bulging factor can be applied when considering the defect geometry. The class society Det Norske Veritas (DNV) headquartered in Norway published the standard ST-F101 [52] and also one document of recommended practice RP-F101 [53] for pipeline applications. The American Petroleum Institute (API) Specification 5L [54] includes the requirements for manufacturing seamless and welded steel pipes in the transportation of oil and gas. One current challenge for subsea pipeline inspection is the often-deep waters.
More recently, sandwich pipes (SPs) were proposed as an effective alternative to the pipe-in-pipe (PIP) system for ultra-deepwater applications [55,56,57]. In the SPs, a polyester foam material with low density and heat-conducting ability is filled between two metal pipes, providing high structural resistance with thermal insulation capability [58,59,60,61].
As reported by DNV [3], the recent digitalization requires the reduced cost of sensor technology and computational power, as well as cloud storage and computing. Figure 5 illustrates the main technologies and enablers in digitalization for SIM: digital worker + support, inspection + data collection, and the analysis.
DNV define in the RP-A204 [62] six levels of capability (stages of the evolution) of digital twins, as illustrated in Figure 6. Real-time data streams are not required in Level 0 (standalone) but are in Level 1 (descriptive capability). Level 2 (diagnostic capability) provides support to monitor the conditions and detect the fault, together with troubleshooting. Health and condition indicators are further enriched in Level 3 (predictive) to support prognostic capabilities. Level 4 (prescriptive capability) can be used to provide recommended actions based on the predictions. In Level 5 (autonomous capability), the users can determine the functional element to perform actions or make decisions regarding the system.
The digitalization of the data exchanges of the subsea pipelines plays an important role in avoiding the errors of the data during manual transcriptions. In order to simplify the data exchange between parties at the different stages of projects, the pipeline data exchange format (PDEF) collaborative initiative was proposed. It is an open-source joint industry project (JIP) involving many companies in the pipeline industry, aiming at developing a standard format for the exchange of the data and describing the data used in the design of subsea pipelines [3].

3. Automated Creation of the Digital Twin during Construction

The oil and gas industry has been adopting digital twins of asset life cycle management (ALCM) in recent years. Subsea pipelines are unique when compared to other major infrastructure assets due to the fact that they are buried immediately during construction and are used to transport hazardous and explosive contents at high pressures. These facts result in an enormous responsibility for all phases of a pipeline asset’s life cycle, including construction, operation, and long-term integrity management.
To accurately trace the condition of a pipeline asset at any time in its lifecycle to previous environmental conditions, operational conditions or events during its life, a single source of truth representing the entire meter-by-meter condition of the asset (i.e., a digital twin), must be created. The creation of the pipeline digital twin during pipeline construction can lead to a full representation of the asset from creation to decommissioning, which construction management, quality control, engineering and integrity can all refer to [63]. The goal is to create the data required by inspectors during construction and capture it in such a manner that it can also be easily or instantly accessed and used by the operator during construction for quality management and operations and maintenance post-commissioning.
Field trials were completed to test and evaluate workflows and sensor platforms for the creation of digital twins for pipelines in 2017 [63]. After pipeline stringing, welding, and lowering, the open ditch and lowered pipe were scanned in the final resting position prior to backfilling. The laser imaging, detection, and ranging (LiDAR) system and downward-facing camera were suspended above the open ditch on the side-mounted boom. The system has a measurement rate of up to 700,000 measurements per second, high accuracy fibre optic gyro (FOG) inertial measurement units (IMU), and a high-end survey-grade global positioning system (GPS). The trials resulted in highly accurate pipeline centrelines, weld locations, depth to cover (DoC), and ditch geometry capture in digital formats. The resulting point clouds contain about 6.2 million highly-accurate points over a scan distance of 370 m. The weld location was added to the geographic information system (GIS) based on the imagery interpretation.
In addition to the remote sensing method, techniques of magnetic flux leakage and acoustic detection are commonly used to detect the cracks of subsea pipelines. One of the limitations of the LiDAR inspection is the assumption used in the algorithm of the software. Since visualization is one key feature of digital transformation, methods to obtain an accurate and useable result from the massive amount of measurement data need to be developed and improved in the further application of digital twins.
A pipeline design automation was introduced based on the cloud-based digital twin McDermott SubseaXD [64]. SubseaXD is collaborating with Dassault Systemes/Simulia to leverage their 3D Experience platform with a smart, collaborative PLM platform. The web-based graphical user interface (GUI) worked as an integrated system producing a 3D digital field diagram, together with all pipeline design calculations in one digital platform. Various calculations, including wall thickness calculations based on API/DNV/ASME code check, on-bottom stability analysis, pipeline span analysis, pipeline end expansion analysis, out of straightness analysis, and pipeline buckling analysis are performed sequentially and systematically in the cloud using the metadata information (i.e., pipe data, soil, environment) available through Python-based API from the digital field data. Abaqus and Orcaflex are integrated with the SubseaXD, which can be used for detailed finite element analysis (FEA). It was stated that the automated pipeline design can save hours with fewer errors, thus saving on cost.
Based on the study in [63], the following recommendations for further digital twin creations were provided:
  • Refine the hardware used, and thus the field execution and subsequent data processing workflow.
  • Compare the digital twin centreline to the in-line inspection and weld location surveys, and seek to explain variances.
  • Make the results more readily available using cloud services and mobile communications to better expose the data to decision-makers.
  • Investigate the schedule and cost savings in greater detail.
  • Garner more operator feedback on the potential value from a construction quality control or quality assurance perspective.
  • Examine long-term impacts on the integrity and general operations and maintenance, as well as on failure investigations.

4. Update of the Digital Twin with Information Acquired in Inspections

Notable offshore production regions include the Gulf of Mexico (GoM), the North Sea, Brazil, West Africa, the Persian Gulf, Atlantic Canada, the Gulf of Thailand, the East and the South China Sea, the Caspian Sea, and the Southern and Western Australia [65]. The GoM has the greatest number of offshore pipelines installed, followed by the North Sea. From 1952 to 2017, more than 72,000 km of pipelines have been installed in the GoM, and about 42,000 km of them are still active. The North Sea has the second-largest pipeline network with approximately 45,000 km of pipeline installed since 1966 [66].
However, the issues and obstacles make the inspection and monitoring of pipelines a challenging task. Problems may occur throughout the life of the pipe, and the environment of service is full of potential dangers [65]. In the use of digital twin technology in offshore structures, the twin model must be updated during the service life with the data of corrosion and other structural degradation gathered during service life inspections and could be updatable according to the accidental damages.
A pipe segment is often coated during the manufacturing process to protect it against corrosion or abrasion. Additional layers of coating can be added, depending on the requirements. Some subsea pipelines have outer concrete coatings for protection against corrosion and impacts and weight stabilization [67,68]. PIP designs may also be implemented with additional layers for protection and thermal insulation [69,70,71]. In addition to the inspection of the primary metallic body, the coating layers may also require inspections for damage and debonding from the pipelines. Depending on the task and the technology used in the inspection, part of the pipelines may need to be stripped of coatings to be fully inspected.
Based on an investigation by The US Department of Transportation, the major failures of subsea pipelines were categorized into five categories: mechanical, operational, corrosion, natural hazards, and third party. All of these possible failures necessitate the routine inspection of the pipeline and the use of permanent monitoring technologies. Nowadays, one of the challenges for subsea pipeline inspection is the extreme water depths. Many well proved inspection technologies cannot be delivered to the pipeline without costly equipment and procedures. In the cases of multi-layered pipelines, the cost of stripping away the coatings for routine inspections in deepwater is impractical. In-line inspection (ILI) may be used to inspect the pipeline and the inner from the inside. Pigging is one of the ILI techniques in which devices referred to as “pigs” or scrapers are inserted into pipelines to perform inspection activities. Pigging can be conducted on a variety of pipeline sizes without having to stop the flow of material through the line.
Given the increasing capacity of satellite links, the potential of cloud computing and deep learning (DL) algorithms, the digital twin models with millimetre precision move the subsea asset management into a new era, enabling engineers to incorporate less margin into their remediation advice which translates into more targeted maintenance. A solution to processing high-resolution data collected from pipeline inspections was presented by the geo-data specialist company Fugro using in-house Remote Observation, Automatic Modelling, Economic Simulations (Roames) technology [71]. The resultant product is a web-based service that enables pipeline inspection data to be uploaded to the secure cloud environment in near real-time, processed using ML, verified by experts onshore and visualized in an intuitive 4D web viewer. This approach can greatly reduce the cost of infrastructure management practices, lower the risk exposure, and contribute to the extended life of the pipeline.
The project was executed which employed a Kongsberg Hugin autonomous underwater vehicle (AUV) operated at an average of 3 m altitude and 4.5 kt [72]. The point cloud was acquired with a Reson 7125 multi-beam echosounder (MBES). The pipeline position was automatically detected using a convolution neural network (CNN), which was trained to detect pipeline profiles from a cross-section of the point cloud. Ground truth data was generated from historical surveys, where the pipeline position was placed manually by a qualified data processor. The ground truth contains a pipe diameter marker, representing the position. Using the pipe and terrain model, the free-span and burial events can then be computed automatically. The web-based Roames Pipe Inspection tool can be used to inspect and adjust machine-learning-based point classification and pipeline positioning. The neural network-based pipe modelling positions the pipe within the correct location relative to the pipe points. The results of the neural network quantitative analysis showed a 97.2% accuracy in detecting pipe burials. The observed mean error in pipeline position was 0.46 cm.
While these new developments provide the operators with the desired deliverable, the large volume of information presents new challenges for inspection contractors and their onboard data processors working in a remote environment. Ongoing technological advancements in satellite communications present new opportunities for data to be transferred off the vessel in near real-time. This allows data processing to be performed securely in the cloud environment and validated by a global team of experts in offices around the world.
It is noted that the construction of the digital twins shall be started at the design phase of the offshore structure. The digital twin model must be updated with as-built and as-installed conditions and could be updatable in a fast way for accidental damages, permitting a quick evaluation of the asset safety and providing information.

5. Maintenance Planning Based on the Digital Twin

In recent years, the digital twins have been implemented in different industrial sectors, and in the design, production, manufacturing, and maintenance of the subsea assets. Among these application areas, maintenance is one of the most researched applications, as the execution of maintenance tasks may have a great influence on the business.
Different maintenance strategies might be used in the decision making, namely reactive maintenance, preventive maintenance, condition-based maintenance, predictive maintenance, and prescriptive maintenance [73,74,75,76,77,78,79,80,81,82,83,84]. Predictive maintenance combines conditioning monitoring integrating with an ML-based decision support system and can enhance economic efficiency and availability [4]. It has the capability to determine when to perform the maintenance based on the real conditions of the subsea pipelines. Once in place, these capabilities could reduce the unplanned maintenance downtime events and thus optimize the OPEX.
Figure 7 shows a digital twin system for predictive maintenance. One of the components is a computational model of the asset which is normally a finite element (FE) model. The computational model of the subsea pipeline is updated based on different field sensor data such as the motion sensor/accelerometer, the subsea strain gauge, the acoustic doppler current profiler (ADCP), the wave radar, the subsea pressure sensor, and the subsea temperature sensor (see Figure 8). Both the data-driven and FE-based models can be used to predict the remaining fatigue life (RFL) of the pipelines based on the stress range data, and are further used in the decision-making process. Knowledge of the RFL can enable efficient maintenance planning and avoid unpredicted shutdowns.
Another component of the digital twin is the IoT/sensor system installed in the physical asset. IoT brings together low-cost sensors, cloud computing, and BDA in subsea pipeline systems where robustness, reliability, and security are highly desired. The third component is the data analytics to find the insight between the measured sensor data and apply a machine-learning algorithm to find the RFL based on the measured strain gauge data. The hydrodynamic load can be measured in real-time using the field sensor system and fed into the digital model. Wireless data loggers are connected to the IoT-based systems which transmit the data to store in the cloud for online analytics, visualization, and reporting. The data stored in the cloud is accessible onshore or onboard for data analytics.
Figure 9 illustrates the predictive maintenance model based on sensor data analytics and ML algorithms. The predictive maintenance schedule is estimated by using a system of artificial neural network (ANN). The memory blocks of a long short-term memory (LSTM) are used for the layers of a recurrent neural network (RNN).

6. Fast Assessment of Failures and Inspection Planning

In the technically demanding submarine environment, subsea pipelines are susceptible to various damage threats, which may lead to catastrophic failures of the disastrous economic and environmental variety. For instance, the waves and currents can lead to a scouring of the soil underneath the pipelines and free span problems where a segment of the pipeline becomes unsupported except at the two ends of the free span length. Free span lengths above the allowable design limit can result in fatigue damage through vortex-induced vibrations (VIV) [65,85,86,87]. Corrosion and erosion can occur from chemical attacks and abrasion due to the internal fluids containing abrasive sand particles travelling at high velocities. Corrosions, when combined with tensile stresses, can lead to stress cracking and leakage in subsea pipelines [46,47,48,49,50,51,52,65,88].
To detect and correct malfunctions of the physical assent, the digital twin applies ML, DL, and AI algorithms. It was revealed in a survey that asset monitoring and maintenance is the most anticipated application area for the digital twin [5]. The fitness-for-service (FFS) of the physical asset is continuously monitored by the digital twin to identify potential failures. The big-data analytics capabilities of the digital twin can monitor the asset and send warnings to the responsible parties.
Digital twins provide high-fidelity accurate models and keep updating through the lifecycle of the pipelines with gathered data from sensors and inspections. Thus, they can reproduce the current state of the pipelines in the virtual space. Comparisons between digital twin simulated data and collected data can help determine the failure mode. One advantage of the state monitoring by the digital twin is that users can monitor the product state from any remote location through the unique identifier incorporated by the digital twin [31].
A computer vision-based digital twin model for real-time corrosion inspection was proposed in [89]. The CNN algorithm was used for the automated corrosion identification and classification from the remotely operated vehicles (ROV) images and ILI data. Based on the corrosion assessment by the digital twin, predictive and prescriptive maintenance strategies are recommended.
During the service life of subsea pipelines, failure mechanisms such as external/internal sheath damage, fatigue damage, or corrosion may arise. Therefore, high OPEX is consumed to confirm the fitness of the system [90].
Based on the reliability assessments [91,92,93], the risk-based inspection planning methodology has been used for the integrity management of subsea pipelines. The steps of the methodology include (1) data gathering, (2) development of risk criteria, (3) probability of failure (PoF) and consequence of failure (CoF), and (4) risk evaluation and enhanced inspection strategy [94,95,96,97,98,99]. Bayesian network (BN) and genetic algorithm (GA) were used to develop a framework for the inspection decision-making for subsea pipelines [100]. Using digital twins, risk target data analysis and risk estimation by prognostic and ML techniques were performed [101,102].

7. Life Extension Assessments

With the increasing maturity of the oil and gas industry, the requirement to operate a subsea pipeline beyond the design life is becoming commonplace [103]. The life extension of the subsea pipelines opens up many development opportunities. If the pipelines can be reused for future developments, significant capital expenditure reduction can be achieved [104,105].
The process of a life extension assessment considering consolidated guidelines [106,107,108,109] is summarized as follows:
(1)
Definition of the operational context and premises for an extended operational period, and identification of new threats.
(2)
Assessment of current condition, functionality and integrity of the system (Diagnostic).
(3)
Reassessment of the technical lifetime (Prognostic).
(4)
Identification of Life Extension measures (Prescriptive).
(5)
Development of a Life Extension program.
The basic premise of the life-extension process is similar regardless of which guideline is applied. Figure 10 displays a typical life extension process. The detailed descriptions of the key steps in the process can be found in [104].
A digital twin can be of great value to support ALCM and hence support optimal management of the asset lifetime [110]. The life extension assessment process clearly maps into the classification and implementation levels of the digital twin.
A digital twin supported by the system of systems concepts was proposed in [110] to represent an aid for model-based condition assessment, and the estimation of RUL contributes with prescriptive capabilities to the identification of measures related to condition-based or predictive maintenance policies, optimal operation, and control to extend equipment lifetime. In this regard, the life cycle losses and costs can be reduced. Following the standards and guidelines for life extension evaluations, a risk picture can be automated and integrated within levels 2 and 3 of the digital twin to guarantee the visualization of current and future risks. This can then serve as inputs into level 4 to identify the risk-based extension measures and to visualize the mitigated risk picture.
Another digital twin concept was proposed to provide an accurate estimation of the true fatigue life of assets to unlock potential fatigue life and ultimately extend the life of assets in the oil and gas industry [111]. The digital twin concept was divided into four tiers that allow for unlocking the RFL of the subsea asset:
(1)
High-resolution modelling of the asset (RB-FEA).
(2)
Update the model to reflect real-world conditions (Digital Twin).
(3)
Fatigue calculations based on continuous monitoring.
(4)
Statistical correlation between sea states and fatigue damage. Retrospective fatigue calculation.
The reduced basis finite element analysis (RB-FEA) technology by Akselos is faster than conventional FEA in which higher accuracy is ensured by using posterior accuracy indicators and automated model enrichment [112]. Among the existing case studies [111,113], the Akselos digital twin is used for the life extension of subsea assets.
Fluid flow assurance issues including hydrates, wax deposition, asphaltenes and naphthenates, slugging, scales, corrosion, and erosion, bring a critical operational challenge to the production and transportation of pipelines [114]. Computational fluid dynamics (CFD) can be applied, combined with different models (e.g., CSMHyK rheological model, Eulerian–Eulerian CFD-model, population balance model), to provide accurate analysis in terms of flow assurance [115]. The models can be integrated into commercial CFD packages such as FLUENT, STAR-CD, TransAT, and STAR-CCM+ for such analyses. DNV developed hydraulic modelling software Synergi Gas for the optimization and simulation of gas distribution and transmission networks. In the further development of digital twins in subsea pipelines, flow assurance and fluid composition tracking need to be taken into consideration.
Table 1 lists the reviewed papers on the application of digital twins on different areas of subsea pipelines. It reveals that the maintenance and manufacturing of the pipelines are two top-ranked applications of digital twins. It can also be seen that half of the publications are journal papers. Given that the investigations and discussions on digital twins were mainly published in international conferences in the early 2010s, as reported in [5,34], more researchers intend to publish their research in journals in the coming years.

8. Conclusions

With the recent wave of digitalization, the digital twin has been discussed as a powerful technology in a variety of industries including the oil and gas industry. The emergence of the digital twin provides an efficient way to realize remote monitoring and control, downtime prediction, and risk reduction of oil and gas subsea pipeline systems. This paper provides detailed coverage of recent publications on various applications of digital twins for subsea pipelines in terms of design, construction, service life, and assessments of life extension.
The key opportunities identified for improved integrity management enabled by digital twin applications are data contextualization, standardization, automated anomaly detection, and learning through sharing. The information of RFL provided by twin models will be valuable for the assessment of the extension of the service life of the subsea assets. With the calibrated twin model by considering actual environmental conditions, fatigue damage can be evaluated in real-time during the service life of the pipeline systems. Thus, the owners and authorities will be able to know the RFL and issue actions to optimize fatigue life or provide improvements or reinforcements for the lifetime extension of the assets.
On the other hand, the following main challenges of the use of digital twins have also been identified. Data related to the information on the conditions and risks are, in most cases, stored in different systems. Limited access to the data on servers is another issue in the use of digital twins. The physical and virtual facilities need to be protected against cyber-attacks by advanced cyber-security protocols [3,5,34,110]. Regarding the social impact, it was revealed that digital twin technologies can result in the redistribution of the workplace without much impact on employment [112].
The influence of digital twins also relies on the quantitative changes in new technologies. The following steps are suggested to make the research and development of digital twins more coherent: unify data and model standards; create a public database for sharing data and models; develop products and services to help digital twins become easier to build and use; develop universal platforms and tools for digital twin applications; and establish forums for practitioners and researchers.

Author Contributions

Conceptualization, B.-Q.C. and P.M.V.; writing—original draft preparation, B.-Q.C.; writing—review and editing, P.M.V. and C.G.S.; visualization, B.-Q.C.; supervision, C.G.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was developed in the scope of the project “Cementitious cork composites for improved thermal performance of pipelines for ultradeep waters—SUBSEAPIPE”, with reference no. POCI-01-0145-FEDER-031011 funded by European Regional Development Fund (FEDER) through COMPETE2020—Operational Program Competitive-ness and Internationalization (POCI) and with financial support from the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia—FCT), under contract 02/SAICT/032108/2017. This study contributes to the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineering, which is financed by FCT, under contract UIDB/UIDP/00134/2020.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Silva, L.M.R.; Guedes Soares, C. An integrated optimization of the floating and subsea layouts. Ocean Eng. 2019, 191, 106557. [Google Scholar] [CrossRef]
  2. Silva, L.M.R.; Guedes Soares, C. Oilfield development system optimization under reservoir production uncertainty. Ocean Eng. 2021, 225, 108758. [Google Scholar] [CrossRef]
  3. DNV GL. 2020/1022: How Digital Tools and Solutions Can Improve Subsea Integrity Management; DNV GL: Oslo, Norway, 2020. [Google Scholar]
  4. Bhowmik, S. Digital twin of subsea pipelines: Conceptual design integrating IoT, machine learning and data analytics. In Proceedings of the Annual Offshore Technology Conference 2019, Houston, TX, USA, 6–9 May 2019. [Google Scholar] [CrossRef]
  5. Wanasinghe, T.R.; Wroblewski, L.; Petersen, B.K.; Gosine, R.G.; James, L.A.; de Silva, O.; Mann, G.K.I.; Warrian, P.J. Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges. IEEE Access 2020, 8, 104175–104197. [Google Scholar] [CrossRef]
  6. Rosen, R.; von Wichert, G.; Lo, G.; Bettenhausen, K.D. About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 2015, 48, 567–572. [Google Scholar] [CrossRef]
  7. Gelernter, D.H. Mirror Worlds: Or the Day Software Puts the Universe in a Shoebox—How It Will Happen and What It Will Mean; Oxford University Press: New York, NY, USA, 1993; OCLC 23868481; ISBN 978-0195079067. [Google Scholar] [CrossRef]
  8. Grieves, M. Virtually intelligent product systems: Digital and physical twins. In Complex Systems Engineering: Theory and Practice; Flumerfelt, S., Schwartz, K.G., Mavris, D., Briceno, S., Eds.; American Institute of Aeronautics and Astronautics: Reston, VA, USA, 2019; pp. 175–200. [Google Scholar] [CrossRef]
  9. Li, C.; Mahadevan, S.; Ling, Y.; Choze, S.; Wang, L. Dynamic Bayesian network for aircraft wing health. AIAA J. 2017, 55, 930–941. [Google Scholar] [CrossRef]
  10. Mandolla, C.; Messeni, A.; Percoco, G.; Urbinati, A. Computers in Industry Building a digital twin for additive manufacturing through the exploitation of blockchain: A case analysis of the aircraft industry. Comput. Ind. 2019, 109, 134–152. [Google Scholar] [CrossRef]
  11. Magargle, R.; Johnson, L.; Mandloi, P.; Davoudabadi, P.; Kesarkar, O.; Krishnaswamy, S.; Batteh, J.; Pitchaikani, A. A simulation based digital twin for model-driven health monitoring and predictive maintenance of an automotive braking system. In Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, 15–17 May 2017; pp. 35–46. [Google Scholar] [CrossRef] [Green Version]
  12. Damjanovic-Behrendt, V. A digital twin-based privacy enhancement mechanism for the automotive industry. In Proceedings of the International Conference on Intelligent Systems (IS), Madeira, Portugal, 25–27 September 2018; pp. 272–279. [Google Scholar] [CrossRef]
  13. Torkamani, A.; Andersen, K.G.; Steinhubl, S.R.; Topol, E.J. High-definition medicine. Cell 2017, 170, 828–843. [Google Scholar] [CrossRef]
  14. Liu, Y.; Zhang, L.; Yang, Y.; Zhou, L.; Ren, L.; Wang, F.; Liu, R.; Pang, Z.; Deen, M.J. A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access 2019, 7, 49088–49101. [Google Scholar] [CrossRef]
  15. Laaki, H.; Miche, Y.; Tammi, K. Prototyping a digital twin for real time remote control over mobile networks: Application of remote surgery. IEEE Access 2019, 7, 20325–20336. [Google Scholar] [CrossRef]
  16. Jimenez, J.I.; Jahankhani, H.; Kendzierskyj, S. Health care in the cyberspace: Medical cyber-physical system and digital twin challenges. In Digital Twin Technologies and Smart Cities; Springer: Cham, Switzerland, 2020; pp. 79–92. [Google Scholar] [CrossRef]
  17. Post, J.; Groen, M.; Klaseboer, G. Physical model based digital twins in manufacturing processes. In Proceedings of the 10th Forming Technology Forum, Enschede, The Netherlands, 12–13 October 2017. [Google Scholar]
  18. Howard, D. The digital twin: Virtual validation in electronics development and design. In Proceedings of the 2019 Pan Pacific Microelectronics Symposium, Kauai, HI, USA, 11–14 February 2019; pp. 1–9. [Google Scholar] [CrossRef]
  19. Bilberg, A.; Malik, A.A. Digital twin driven human-robot collaborative assembly. CIRP Ann. 2019, 68, 499–502. [Google Scholar] [CrossRef]
  20. Mohammadi, N.; Taylor, J.E. Smart city digital twins. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 27 November–1 December 2017; pp. 1–5. [Google Scholar] [CrossRef]
  21. Ruohomäki, T.; Airaksinen, E.; Huuska, P.; Kesäniemi, O.; Martikka, M.; Suomisto, J. Smart city platform enabling digital twin. In Proceedings of the International Conference on Intelligent Systems (IS), Madeira, Portugal, 25–27 September 2018; pp. 155–161. [Google Scholar] [CrossRef]
  22. Piascik, R.; Vickers, J.; Lowry, D.; Scotti, S.; Stewart, J.; Calomino, A. Technology Area 12: Materials, Structures, Mechanical Systems, and Manufacturing Road Map; NASA Office of Chief Technologist: Washington, DC, USA, 2010.
  23. Caruso, P.; Dumbacher, D.; Grieves, M. Product lifecycle management and the quest for sustainable space exploration. In Proceedings of the AIAA SPACE 2010 Conference & Exposition, Anaheim, CA, USA, 30 August–2 September 2010. [Google Scholar] [CrossRef]
  24. Glaessgen, E.; Stargel, D. The digital twin paradigm for future NASA and US Air Force vehicles. In Proceedings of the 53rd AI—AA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Honolulu, HI, USA, 23–26 April 2012. [Google Scholar] [CrossRef] [Green Version]
  25. Tuegel, E. The airframe digital twin: Some challenges to realization. In Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Honolulu, HI, USA, 23–26 April 2012. [Google Scholar] [CrossRef]
  26. Maher, D.P. On software standards and solutions for a trusted Internet of Things. In Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS-51), Waikoloa Village, HI, USA, 3–6 January 2018. [Google Scholar] [CrossRef] [Green Version]
  27. Chen, B.Q.; Videiro, P.M.; Guedes Soares, C. Review of digital twin of ships and offshore structures. In Developments in Maritime Technology and Engineering; Guedes Soares, C., Santos, T.A., Eds.; Taylor and Francis: London, UK, 2021; pp. 445–451. [Google Scholar] [CrossRef]
  28. Panetta, K. Gartners Top 10 Technology Trends for 2017. Available online: https://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/ (accessed on 27 January 2022).
  29. Cearley, D.W.; Walker, M.J.; Blosch, M. Top 10 Strategic Technology Trends for 2018; Gartner: Stamford, CT, USA, 2017. [Google Scholar]
  30. Panetta, K. Gartner Top 10 Strategic Technology Trends for 2019. Available online: https://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2019/ (accessed on 27 January 2022).
  31. Liu, M.; Fang, S.; Dong, H.; Xu, C. Review of digital twin about concepts, technologies, and industrial applications. J. Manuf. Syst. 2021, 58, 346–361. [Google Scholar] [CrossRef]
  32. Son, Y.H.; Kim, G.-Y.; Kim, H.C.; Jun, C.; Noh, S. Do Past, present, and future research of digital twin for smart manufacturing. J. Comput. Des. Eng. 2021, 9, 1–23. [Google Scholar] [CrossRef]
  33. Wang, J.; Li, X.; Wang, P.; Liu, Q. Bibliometric analysis of digital twin literature: A review of influencing factors and conceptual structure. Technol. Anal. Strateg. Manag. 2022, 1–15. [Google Scholar] [CrossRef]
  34. Atalay, M.; Murat, U.; Oksuz, B.; Parlaktuna, A.M.; Pisirir, E.; Testik, M.C. Digital twins in manufacturing: Systematic literature review for physical-digital layer categorization and future research directions. Int. J. Comput. Integr. Manuf. 2022, 1–27. [Google Scholar] [CrossRef]
  35. Tao, F.; Zhang, M.; Nee, A.Y.C. Five-dimension digital twin modeling and its key technologies. Digit. Twin Driven Smart Manuf. 2019, 63–81. [Google Scholar] [CrossRef]
  36. Qi, Q.; Tao, F.; Hu, T.; Anwer, N.; Liu, A.; Wei, Y.; Wang, L.; Nee, A.Y.C. Enabling technologies and tools for digital twin. J. Manuf. Syst. 2021, 58, 3–21. [Google Scholar] [CrossRef]
  37. Tao, F.; Qi, Q. Make more digital twins. Nature 2019, 573, 490–491. [Google Scholar] [CrossRef] [Green Version]
  38. Guo, B.; Song, S.; Ghalambor, A.; Lin, T.R. Offshore Pipelines: Design, Installation, and Maintenance; Gulf Professional Publishing: Houston, TX, USA, 2013. [Google Scholar] [CrossRef]
  39. Cai, J.; Jiang, X.; Lodewijks, G. Residual ultimate strength of offshore metallic pipelines with structural damage–a literature review. Ships Offshore Struct. 2017, 12, 1037–1055. [Google Scholar] [CrossRef] [Green Version]
  40. Bai, Q.; Bai, Y. Subsea Pipeline Design, Analysis, and Installation; Gulf Professional Publishing: Waltham, MA, USA, 2014. [Google Scholar]
  41. Yazdi, M.; Khan, F.; Abbassi, R. Operational subsea pipeline assessment affected by multiple defects of microbiologically influenced corrosion. Process Saf. Environ. Prot. 2022, 158, 159–171. [Google Scholar] [CrossRef]
  42. Wang, Z.; Tang, Y.; Guedes Soares, C. Imperfection study on lateral thermal buckling of subsea pipeline triggered by a distributed buoyancy section. Mar. Struct. 2021, 76, 102916. [Google Scholar] [CrossRef]
  43. Wang, Z.; Tang, Y.; Yang, J.; Guedes Soares, C. Analytical study of thermal upheaval buckling for free spanning pipelines. Ocean Eng. 2020, 218, 108220. [Google Scholar] [CrossRef]
  44. Bruton, D.A.; Bolton, M.; Carr, M.; White, D. Pipe-soil interaction with flowlines during lateral buckling and pipeline walking —The SAFEBUCK JIP. In Proceedings of the Offshore Technology Conference, Houston, TX, USA, 5–8 May 2008. [Google Scholar] [CrossRef]
  45. Bruton, D.A.; Carr, M.; Sinclair, F.; MacRae, I. Lessons learned from observing walking of pipelines with lateral buckles, including new driving mechanisms and updated analysis models. In Proceedings of the Offshore Technology Conference, Houston, TX, USA, 3–6 May 2010. [Google Scholar] [CrossRef]
  46. Chen, B.; Zhang, X.; Guedes Soares, C. The effect of general and localized corrosions on the collapse pressure of subsea pipelines. Ocean Eng. 2022, 247, 110719. [Google Scholar] [CrossRef]
  47. Zhang, X.; Chen, B.; Guedes Soares, C. Effect of non-symmetrical corrosion imperfection on the collapse pressure of subsea pipelines. Mar. Struct. 2020, 73, 102806. [Google Scholar] [CrossRef]
  48. Teixeira, A.P.; Guedes Soares, C.; Netto, T.A.; Estefen, S.F. Reliability of pipelines with corrosion defects. Int. J. Press. Vessel. Pip. 2008, 85, 228–237. [Google Scholar] [CrossRef]
  49. Teixeira, A.P.; Palencia, O.G.; Guedes Soares, C. Reliability analysis of pipelines with local corrosion defects under external pressure. J. Offshore Mech. Arct. Eng. 2019, 141, 1–10. [Google Scholar] [CrossRef]
  50. Netto, T.A. On the effect of narrow and long corrosion defects on the collapse pressure of pipelines. Appl. Ocean Res. 2009, 31, 75–81. [Google Scholar] [CrossRef]
  51. ASME. B31G Manual for Determining the Remaining Strength of Corroded Pipelines (Supplement to ASME B31 Code for Pressure Piping); The American Society of Mechanical Engineers: New York, NY, USA, 2012. [Google Scholar]
  52. DNV. Recommended Practice DNV-RP-F101: Corroded Pipelines; DNV: Oslo, Norway, 2019. [Google Scholar]
  53. DNV. Standard DNV-ST-F101: Submarine Pipeline Systems; DNV: Oslo, Norway, 2021. [Google Scholar]
  54. American Petroleum Institute. API Specification 5L—Line Pipe, 46th ed.; American Petroleum Institute: Washington, DC, USA, 2018. [Google Scholar]
  55. Li, R.; Chen, B.; Guedes Soares, C. Design equation for the effect of ovality on the collapse strength of sandwich pipes. Ocean Eng. 2021, 235, 109367. [Google Scholar] [CrossRef]
  56. An, C.; Duan, M.; Estefen, S.F.; Su, J. Structural and Thermal Analyses of Deepwater Pipes; Springer: Cham, Switzerland, 2021; ISBN 9783030535391. [Google Scholar] [CrossRef]
  57. Xia, M.; Kemmochi, K.; Takayanagi, H. Analysis of filament-wound fiber-reinforced sandwich pipe under combined internal pressure and thermomechanical loading. Compos. Struct. 2001, 51, 273–283. [Google Scholar] [CrossRef]
  58. Estefen, S.F.; Netto, T.A.; Pasqualino, I.P. Strength analyses of sandwich pipes for ultra deepwaters. Trans. ASME J. Appl. Mech. 2005, 72, 599–608. [Google Scholar] [CrossRef]
  59. Yang, J.; Paz, C.M.; Estefen, S.F.; Fu, G.; Lourenço, M.I. Collapse pressure of sandwich pipes with strain-hardening cementitious composite—Part 1: Experiments and parametric study. Thin-Walled Struct. 2020, 148, 106605. [Google Scholar] [CrossRef]
  60. Li, R.; Chen, B.Q.; Guedes Soares, C. Effect of ovality length on collapse strength of imperfect sandwich pipes due to local buckling. J. Marit. Sci. Eng. 2022, 10, 12. [Google Scholar] [CrossRef]
  61. Bhardwaj, U.; Teixeira, A.P.; Guedes Soares, C. Uncertainty in collapse strength prediction of sandwich pipelines. J. Offshore Mech. Arct. Eng. 2022, 144, 041702. [Google Scholar] [CrossRef]
  62. DNV. Standard DNV-RP-A204: Qualification and Assurance of Digital Twins; DNV: Oslo, Norway, 2021. [Google Scholar]
  63. Hlady, J.; Glanzer, M.; Fugate, L. Automated creation of the pipeline digital twin during construction—Improvement to construction quality and pipeline integrity. In Proceedings of the 2018 12th International Pipeline Conference, Calgary, AB, Canada, 24–28 September 2018; Volume 2, pp. 1–12. [Google Scholar] [CrossRef]
  64. Bhowmik, S.; Noiray, G.; Naik, H. Subsea pipeline design automation using digital field twin. In Proceedings of the Abu Dhabi International Petroleum Exhibition & Conference ADIP 2019, Abu Dhabi, United Arab Emirates, 11–14 November 2019. [Google Scholar] [CrossRef]
  65. Ho, M.; El-Borgi, S.; Patil, D.; Song, G. Inspection and monitoring systems subsea pipelines: A review paper. Struct. Health Monit. 2020, 19, 606–645. [Google Scholar] [CrossRef] [Green Version]
  66. Kaiser, M.J. The global offshore pipeline construction service market 2017–Part I. Ships Offshore Struct. 2018, 13, 65–95. [Google Scholar] [CrossRef]
  67. Karuks, E.; Rohn, M. Mechanical Protection Coating for Coated Metal Substrate. U.S. Patent No. 4,395,159, 5 February 1985. [Google Scholar]
  68. Sumner, G.R. Offshore Pipeline Insulated with a Cementitious Coating. U.S. Patent No. 5,476,343, 19 December 1995. [Google Scholar]
  69. Bass, R.M.; Newberry, B.L.; Langner, C.G. Annulus for Electrically Heated Pipe-in-Pipe Subsea Pipeline. U.S. Patent No. 6,814,146, 9 November 2004. [Google Scholar]
  70. Zhang, X.; Duan, M.; Guedes Soares, C. Lateral buckling critical force for submarine pipe-in-pipe pipelines. Appl. Ocean. Res. 2018, 78, 99–109. [Google Scholar] [CrossRef]
  71. Bhardwaj, U.; Teixeira, A.P.; Guedes Soares, C. Reliability assessment of a subsea pipe-in-pipe system for major failure modes. Int. J. Press. Vessel. Pip. 2020, 188, 104177. [Google Scholar] [CrossRef]
  72. Bertram, S.J.; Fan, Y.; Raffelt, D.; Michalak, P. An applied machine learning approach to subsea asset inspection. In Proceedings of the Abu Dhabi International Petroleum Exhibition & Conference, ADIPEC 2018, Abu Dhabi, United Arab Emirates, 12–15 November 2018. [Google Scholar] [CrossRef]
  73. Daily, J.; Peterson, J. Predictive maintenance: How big data analysis can improve maintenance. In Supply Chain Integration Challenges in Commercial Aerospace; Springer: Cham, Switzerland, 2017; pp. 267–278. [Google Scholar] [CrossRef]
  74. Errandonea, I.; Beltrán, S.; Arrizabalaga, S. Digital Twin for maintenance: A literature review. Comput. Ind. 2020, 123, 103316. [Google Scholar] [CrossRef]
  75. Swanson, L. Linking maintenance strategies to performance. Int. J. Prod. Econ. 2001, 70, 237–244. [Google Scholar] [CrossRef]
  76. Shafiee, M. Maintenance strategy selection problem: An MCDM overview. J. Qual. Maint. Eng. 2015, 21, 378–402. [Google Scholar] [CrossRef]
  77. Ansari, F.; Glawar, R.; Nemeth, T. PriMa: A prescriptive maintenance model for cyber-physical production systems. Int. J. Comput. Integr. Manuf. 2019, 32, 482–503. [Google Scholar] [CrossRef] [Green Version]
  78. Werner, A.; Zimmermann, N.; Lentes, J. Approach for a holistic predictive maintenance strategy by incorporating a digital twin. Procedia Manuf. 2019, 39, 1743–1751. [Google Scholar] [CrossRef]
  79. Vathoopan, M.; Johny, M.; Zoitl, A.; Knoll, A. Modular fault ascription and corrective maintenance using a digital twin. IFAC-PapersOnLine 2018, 51, 1041–1046. [Google Scholar] [CrossRef]
  80. Rajesh, P.K.; Manikandan, N.; Ramshankar, C.S.; Vishwanathan, T.; Sathishkumar, C. Digital twin of an automotive brake pad for predictive maintenance. Procedia Comput. Sci. 2019, 165, 18–24. [Google Scholar] [CrossRef]
  81. Strohmeier, F.; Schranz, C.; Guentner, G. i-maintenance: A digital twin for smart maintenance. ERCIM News 2018, 115, 12–14. [Google Scholar]
  82. Liu, Z.; Chen, W.; Zhang, C.; Yang, C.; Chu, H. Data super-network fault prediction model and maintenance strategy for mechanical product based on digital twin. IEEE Access 2019, 7, 177284–177296. [Google Scholar] [CrossRef]
  83. Bashiri, M.; Badri, H.; Hejazi, T.H. Selecting optimum maintenance strategy by fuzzy interactive linear assignment method. Appl. Math. Model. 2011, 35, 152–164. [Google Scholar] [CrossRef]
  84. Arzaghi, E.; Abaei, M.M.; Abbassi, R.; Garaniya, V.; Chin, C.; Khan, F. Risk-based maintenance planning of subsea pipelines through fatigue crack growth monitoring. Eng. Fail. Anal. 2017, 79, 928–939. [Google Scholar] [CrossRef]
  85. Nikoo, H.M.; Bi, K.; Hao, H. Effectiveness of using pipe-in-pipe (PIP) concept to reduce vortex-induced vibrations (VIV): Three-dimensional two-way FSI analysis. Ocean Eng. 2018, 148, 263–276. [Google Scholar] [CrossRef]
  86. Shabani, M.M.; Shabani, H.; Goudarzi, N.; Taravati, R. Probabilistic modelling of free spanning pipelines considering multiple failure modes. Eng. Fail. Anal. 2019, 106, 104169. [Google Scholar] [CrossRef]
  87. Li, X.; Zhang, Y.; Abbassi, R.; Khan, F.; Chen, G. Probabilistic fatigue failure assessment of free spanning subsea pipeline using dynamic Bayesian network. Ocean Eng. 2021, 234, 109323. [Google Scholar] [CrossRef]
  88. Ferreira, A.D.M.; Afonso, S.M.B.; Willmersdorf, R.B.; Lyra, P.R.M. Multiresolution analysis and deep learning for corroded pipeline failure assessment. Adv. Eng. Softw. 2021, 162–163, 103066. [Google Scholar] [CrossRef]
  89. Bhowmik, S. Digital twin for offshore pipeline corrosion monitoring: A deep learning approach. In Proceedings of the Offshore Technology Conference, Houston, TX, USA, 16–19 August 2021. OTC-31296-MS. [Google Scholar] [CrossRef]
  90. Hameed, H.; Bai, Y.; Ali, L. A risk-based inspection planning methodology for integrity management of subsea oil and gas pipelines. Ships Offshore Struct. 2021, 16, 687–699. [Google Scholar] [CrossRef]
  91. Bhardwaj, U.; Teixeira, A.P.; Guedes Soares, C. Reliability assessment of corroded pipelines with different burst strength models. In Developments in Maritime Technology and Engineering; Guedes Soares, C., Santos, T.A., Eds.; Taylor and Francis: London, UK, 2021; Volume 1, pp. 687–696. [Google Scholar] [CrossRef]
  92. Bhardwaj, U.; Teixeira, A.P.; Guedes Soares, C.; Samdani Azad, M.; Punurai, W.; Asavadorndeja, P. Reliability assessment of thick high strength pipelines with corrosion defects. Int. J. Press. Vessel. Pip. 2019, 177, 103982. [Google Scholar] [CrossRef]
  93. Bhardwaj, U.; Teixeira, A.P.; Guedes Soares, C. Uncertainty in reliability of thick high strength pipelines with corrosion defects subjected to internal pressure. Int. J. Press. Vessel. Pip. 2020, 188, 104170. [Google Scholar] [CrossRef]
  94. Seo, J.K.; Cui, Y.; Mohd, M.H.; Ha, Y.C.; Kim, B.J.; Paik, J.K. A risk-based inspection planning method for corroded subsea pipelines. Ocean Eng. 2015, 109, 539–552. [Google Scholar] [CrossRef]
  95. Stadie-Frohbös, G.; Lampe, J. Risk based inspection for aged offshore pipelines. In Proceedings of the ASME 32nd International Conference on Ocean, Offshore and Arctic Engineering, Nantes, France, 9–14 June 2013; American Society of Mechanical Engineers: New York, NY, USA, 2013; p. V02BT02A024. [Google Scholar] [CrossRef]
  96. ABS. Guide for Surveys Using Risk-Based Inspection for the Offshore Industry; American Bureau of Shipping: Houston, TX, USA, 2003.
  97. API. Risk-Based Inspection. API-RP-580, 3rd ed.; American Petroleum Institute: Washington, DC, USA, 2016. [Google Scholar]
  98. ASME. B31G—Manual for Determining the Remaining Strength of Corroded Pipelines; Technical Report; ASME: New York, NY, USA, 1991. [Google Scholar]
  99. Singh, M.; Markeset, T. A methodology for risk-based inspection planning of oil and gas pipes based on fuzzy logic framework. Eng. Fail. Anal. 2009, 16, 2098–2113. [Google Scholar] [CrossRef]
  100. Liu, X.; Zheng, J.; Fu, J.; Nie, Z.; Chen, G. Optimal inspection planning of corroded pipelines using BN and GA. J. Pet. Sci. Eng. 2018, 163, 546–555. [Google Scholar] [CrossRef]
  101. Priyanka, E.B.; Thangavel, S.; Gao, X.Z.; Sivakumar, N.S. Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques. J. Ind. Inf. Integr. 2022, 26, 100272. [Google Scholar] [CrossRef]
  102. Priyanka, E.B.; Thangavel, S.; Prabhakaran, P. Rank-based risk target data analysis using digital twin on oil pipeline network based on manifold learning. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 2022. [Google Scholar] [CrossRef]
  103. Franklin, J.G.; Stowell, B.E.; Jee, T.P.; Hawkins, M. Standardizing the approach for offshore pipeline lifetime extension. In Proceedings of the Abu Dhabi International Petroleum Exhibition and Conference ADIPEC 2008, Abu Dhabi, United Arab Emirates, 3–6 November 2008; Volume 3, pp. 1354–1360. [Google Scholar] [CrossRef]
  104. Selman, A.; Hubbard, R. How to age gracefully—Pipeline life extension. In Proceedings of the SPE Asia Pacific Oil & Gas Conference and Exhibition, Perth, Australia, 25–27 October 2016. [Google Scholar] [CrossRef]
  105. Ferreira, N.N.; Martins, M.R.; de Figueiredo, M.A.G.; Gagno, V.H. Guidelines for life extension process management in oil and gas facilities. J. Loss Prev. Process Ind. 2020, 68, 104290. [Google Scholar] [CrossRef]
  106. Norwegian Oil and Gas Association. Guideline 122—Norwegian Oil and Gas Recommended Guidelines for the Management of Life Extension; Norwegian Oil and Gas Association: Forus, Norway, 2017. [Google Scholar]
  107. ISO/TS 12747; Petroleum and Natural Gas Industries—Pipeline Transportation Systems—Recommended Practice for Pipeline Life Extension. ISO: Geneva, Switzerland, 2011.
  108. NORSOK Y-002; Life Extension for Transportation System. NORSOK: Oslo, Norway, 2010.
  109. NORSOK Y-009; Life Extension for Subsea Systems. NORSOK: Oslo, Norway, 2011.
  110. Altamiranda, E.; Colina, E. A system of systems digital twin to support life time management and life extension of subsea production systems. In Proceedings of the OCEANS 2019, Marseille, France, 17–20 June 2019. [Google Scholar] [CrossRef]
  111. Knezevic, D.; Fakas, E.; Riber, H.J. Predictive digital twins for structural integrity management and asset life extension—JIP concept and results. In Proceedings of the SPE Offshore Europe Conference and Exhibition, OE 2019, Aberdeen, UK, 3–6 September 2019; pp. 1–6. [Google Scholar] [CrossRef]
  112. Rasheed, A.; San, O.; Kvamsdal, T. Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access 2020, 8, 21980–22012. [Google Scholar] [CrossRef]
  113. Sharma, P.; Knezevic, D.; Huynh, P.; Malinowski, G. RB-FEA based digital twin for structural integrity assessment of Offshore structures. In Proceedings of the Annual Offshore Technology Conference, Houston, TX, USA, 30 April–3 May 2018; Volume 4, pp. 2942–2947. [Google Scholar] [CrossRef]
  114. Theyab, M.A. Fluid flow assurance issues: Literature review. SciFed J. Pet. 2018, 2, 1–11. [Google Scholar]
  115. Balakin, B.V.; Lo, S.; Kosinski, P.; Hoffmann, A.C. Modelling agglomeration and deposition of gas hydrates in industrial pipelines with combined CFD-PBM technique. Chem. Eng. Sci. 2016, 153, 45–57. [Google Scholar] [CrossRef]
Jmse 10 00739 g001 550
Figure 1. The number of published results about digital twins in the Web of Science.
Figure 1. The number of published results about digital twins in the Web of Science.
Jmse 10 00739 g001
Jmse 10 00739 g002 550
Figure 2. Five-dimensional digital twin model and the key technologies. PE—Physical Entity, VE—Virtual Entity, Ss—Services, DD—Digital twin data, CN—Connection. Reproduced from [35], with permission from Elsevier, 2022.
Figure 2. Five-dimensional digital twin model and the key technologies. PE—Physical Entity, VE—Virtual Entity, Ss—Services, DD—Digital twin data, CN—Connection. Reproduced from [35], with permission from Elsevier, 2022.
Jmse 10 00739 g002
Jmse 10 00739 g003 550
Figure 3. The tools for services applications of digital twins. Reproduced from [36], with permission from Elsevier, 2022.
Figure 3. The tools for services applications of digital twins. Reproduced from [36], with permission from Elsevier, 2022.
Jmse 10 00739 g003
Jmse 10 00739 g004 550
Figure 4. Uses of subsea pipelines. Reproduced from [38], with permission from Elsevier, 2022.
Figure 4. Uses of subsea pipelines. Reproduced from [38], with permission from Elsevier, 2022.
Jmse 10 00739 g004
Jmse 10 00739 g005 550
Figure 5. The key enablers for digitalization in subsea integrity management [3].
Figure 5. The key enablers for digitalization in subsea integrity management [3].
Jmse 10 00739 g005
Jmse 10 00739 g006 550
Figure 6. The capability levels of the digital twins at different stages of the evolution [62].
Figure 6. The capability levels of the digital twins at different stages of the evolution [62].
Jmse 10 00739 g006
Jmse 10 00739 g007 550
Figure 7. A digital twin system for maintenance [4].
Figure 7. A digital twin system for maintenance [4].
Jmse 10 00739 g007
Jmse 10 00739 g008 550
Figure 8. A field sensor system and the data-driven model [4].
Figure 8. A field sensor system and the data-driven model [4].
Jmse 10 00739 g008
Jmse 10 00739 g009 550
Figure 9. A predictive maintenance model based on sensor data analytics [4].
Figure 9. A predictive maintenance model based on sensor data analytics [4].
Jmse 10 00739 g009
Jmse 10 00739 g010 550
Figure 10. The life extension process in [107].
Figure 10. The life extension process in [107].
Jmse 10 00739 g010
Table
Table 1. A categorical review of the applications of digital twins on subsea pipelines.
Table 1. A categorical review of the applications of digital twins on subsea pipelines.
ApplicationReference NumberYear of PublicationType of Document
Construction[63]2018Conference
Design[4]2019Conference
Design[18]2019Conference
Life extension[110]2019Conference
Life extension[111]2019Conference
Maintenance[11]2017Conference
Maintenance[74]2020Journal
Maintenance[78]2019Journal
Maintenance[79]2018Journal
Maintenance[80]2019Journal
Maintenance[81]2018Magazine
Maintenance[82]2019Journal
Manufacturing[6]2015Journal
Manufacturing[10]2019Journal
Manufacturing[17]2017Conference
Manufacturing[32]2021Journal
Monitoring[89]2021Conference
Risk assessment[101]2022Journal
Risk assessment[102]2022Journal
Risk assessment[113]2018Conference
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Chen, B.-Q.; Videiro, P.M.; Guedes Soares, C. Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines. J. Mar. Sci. Eng. 2022, 10, 739. https://doi.org/10.3390/jmse10060739

AMA Style

Chen B-Q, Videiro PM, Guedes Soares C. Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines. Journal of Marine Science and Engineering. 2022; 10(6):739. https://doi.org/10.3390/jmse10060739

Chicago/Turabian Style

Chen, Bai-Qiao, Paulo M. Videiro, and C. Guedes Soares. 2022. "Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines" Journal of Marine Science and Engineering 10, no. 6: 739. https://doi.org/10.3390/jmse10060739

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop