Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning
Abstract
:1. Introduction
- (a)
- The effectiveness of modeling methodologies or techniques in accurately capturing the results of simulation attempts, either experimental or numerical, that are intended to simulate the field occurrence of SCC in pipeline steels;
- (b)
2. Methodology and Analysis
2.1. Research Framework
2.2. Research Definition
- RQ 1:
- What are the true values identified by the published work?
- RQ 2:
- What are the new ways identified to use machine learning techniques?
- RQ 3:
- What is machine learning’s application status in the oil and gas sector?
- RQ 4:
- What are the main challenges of adopting machine learning tools for energy pipeline condition assessment?
2.3. Search Strategy
(“Machine Learning” <OR> “Machine Learning Application”) |
[AND] |
(“Oil and Gas” <OR> “Energy Industry” <OR> “Energy”) |
[AND] |
(“Stress Corrosion Cracking” <OR> “SCC”) |
[AND] |
(“Prediction Techniques” <OR> “SCC Prediction” <OR> “Pipeline Condition Assessment”) |
[AND] |
(“Non-Destructive Testing” <OR> “Pipeline Integrity Management” <OR> “Integrity Management”) |
2.4. Analysis of Publications
3. Critical Research and Analysis
3.1. Stress Corrosion Cracking (SCC) Failure Events
3.2. Statistics of Corrosion Incidents and Factors Leading to SCC in Energy Pipelines
- Uniform corrosion;
- Pitting corrosion;
- Cavitation and erosion–corrosion;
- Stray current corrosion;
- Microbiologically influenced corrosion;
- Stress corrosion cracking (SCC);
- Selective seam corrosion (SSC).
- Metal metallurgy;
- Metal physical properties;
- Manufacturing process;
- Process and operating conditions;
- Protecting coating conditions;
- Soil conditions.
3.3. Stress Corrosion Cracking (SCC) Susceptibility Parameters
3.4. Stress Corrosion Cracking (SCC) Management Program
- (1)
- Evaluation of pipe segment susceptibility;
- (2)
- Investigating the presence of SCC;
- (3)
- Determining the time between SCC susceptibility assessments;
- (4)
- Determining the SCC’s severity level;
- (5)
- Establishing and using a safe operating pressure for each pipe segment;
- (6)
- Making plans and putting them into action to mitigate risk;
- (7)
- Examining and assessing mitigation measures;
- (8)
- Recording, education, and reporting;
- (9)
- Condition monitoring.
3.5. Stress Corrosion Cracking (SCC) Detection Techniques
Detection Method | Brief Details |
---|---|
Linear polarization resistance (LPR) | This method quantifies the electrochemical resistance of a corroding metal working electrode in close proximity to its open circuit potential. The process entails the polarization of a voltage range of ±10 mV relative to the corrosion potential [55,56]. |
In-line inspection (ILI) | ILI tools, commonly known as “smart pigs”, are devices that are inserted into the pipeline and travel with the product flow. They use various technologies such as magnetic flux leakage (MFL) or ultrasonic sensors to detect anomalies, including SCC, within the pipeline. These tools provide a comprehensive assessment of the pipeline’s condition [57]. Currently, there are two primary methods used for crack detection: ultrasonic testing and test using electromagnetic acoustic transducers (EMATs) [58]. They are readily accessible for the on-line inspection of SCC in commercial settings. Further ILI technologies can be seen in Figure 7, and their merits and demerits are in Table 3 [49]. |
Electrochemical noise (EN) | This technique continually monitors corrosion potential and variations in current. It is utilized to acquire corrosion current by measuring noise resistance [59]. |
Acoustic emission (AE) | AE monitoring involves detecting and analyzing the high-frequency acoustic signals emitted by crack growth or propagation. It can provide real-time information on the occurrence and progression of SCC [60]. AE sensors are placed on the pipeline, and any acoustic emissions resulting from crack activity are captured and analyzed [61]. |
Electromagnetic testing | Electromagnetic techniques, such as eddy current testing (ECT) and magnetic particle inspection (MPI), can be employed to detect SCC [62]. ECT utilizes electromagnetic induction to detect surface and near-surface cracks, while MPI uses magnetic fields and iron particles to locate cracks or defects that are magnetically visible. |
Ultrasonic testing (UT) | UT uses high-frequency sound waves to detect internal defects or cracks in the pipeline. It involves transmitting ultrasonic waves into the material and analyzing the reflected waves to identify any indications of SCC [63]. UT can be performed on both the external and internal surfaces of the pipeline. |
Cyclic potentiodynamic polarization | This process entails applying an over-potential greater than the corrosion potential toward the noble side until a current of 5 mA is reached. Then, the potential is reversed until the corrosion potential is achieved [64]. |
Radiographic testing (RT) | RT uses X-rays or gamma rays to detect internal defects in the pipeline. It involves passing the radiation through the material and capturing the transmitted radiation on a film or detector. Any cracks or indications of SCC can be identified by examining the resulting radiographic image [65]. |
Electrochemical impedance spectroscopy (EIS) | This process entails the use of an alternating current (AC) potential with a magnitude of ±10 mV around the corrosion potential. This is achieved throughout a broad range of frequencies, generally spanning 0.1 to 106 Hz. The purpose of this is to obtain the corrosion current [64]. |
Electromagnetic acoustic transducer (EMAT) | The electromagnetic acoustic transducer (EMAT) is a modern non-destructive testing (NDT) device employed in in-line inspection (ILI) equipment to detect SCC in gas pipelines [66]. EMATs operate by utilizing a magnetic field to create an ultrasonic compression wave on the inner surface of the pipe wall [67]. |
Hydrostatic testing | Hydrostatic testing is a method employed to detect SCC in pipelines. When conducted correctly, this approach ensures that any significant flaws present during the test are discovered. Hydrostatic testing is a frequently employed technique to ensure the preservation of pipeline integrity in the presence of developing flaws, such as pitting corrosion, fatigue, corrosion fatigue, or SCC [68,69]. |
Magnetic flux leakage | Magnetic flux leakage (MFL) is a non-destructive testing (NDT) method employed for the identification of SCC. A high-strength magnet is employed to magnetize the steel in areas prone to corrosion or potential metal degradation. This method has been employed to identify corrosion flaws, fractures, and mechanical impairments [44]. |
ILI Technologies Used to Detect SCC
Technology | Pros | Cons | Run in Operating | |
---|---|---|---|---|
Oil | Gas | |||
Shear Wave (Liquid-coupled) Ultrasound |
|
| No | Yes |
EMAT |
|
| Yes | Yes |
FMFL |
|
| Yes | Yes |
LWUT |
|
| Yes | Yes |
New Technologies |
|
| Yes | Yes |
SEEC (Self-Excited Eddy Current) |
|
| Yes | Yes |
3.6. Machine Learning (ML)
3.7. SCC Prediction through Machine Learning
- ▪
- A Decision Process: Often, machine learning algorithms are utilized to make predictions or classifications. An algorithm with a clear set of instructions will produce an approximation of a pattern using certain input data that may have labels or may not.
- ▪
- An Error Function: An error function measures the accuracy of the model’s prediction. If there are known examples, an error function can be used to evaluate the model’s accuracy by comparing the model’s output and the known result of the example.
- ▪
- A Model Optimization Process: Weights are adjusted to minimize the discrepancy between the known results from the given example and the model prediction if the model can more accurately represent the data points in the training set. The algorithm will repeatedly execute this “evaluate and optimize” operation, automatically updating weights until a preset level of accuracy is achieved.
3.8. Research Analysis
- i.
- Corrosion damage;
- ii.
- Misuse of factors;
- iii.
- Third-party damage;
- iv.
- Design defects.
3.9. Critical Review Analysis
3.10. Gaps and Challenges in Implementing Machine Learning
Gap/Challenge | Brief Details |
---|---|
Data Availability | ML models require large amounts of high-quality data to effectively learn and make accurate predictions. Obtaining sufficient labeled data related to SCC in pipelines to validate ML models is a major challenge. |
Data Quality | A lack of good quality data is one of the major problems that machine learning experts are facing in obtaining the required outcomes. Data quality is an issue in developing a good model to predict SCC in energy pipelines. As a result, we must make sure that data pre-processing is carried out to achieve the highest degree of accuracy possible, which involves eliminating outliers, the imputation of missing values, and eliminating undesired characteristics. |
Data Variability | The data collected for SCC detection can vary in terms of pipeline materials, environmental conditions, stress levels, and other factors. This variability makes it challenging to develop a machine learning model that can effectively handle different data types. Ensuring a diverse and representative dataset is crucial for training models that can handle the various conditions encountered in pipeline systems. |
Data Privacy | Data protection, data security, and privacy are some of the issues connected with the application of machine learning. For instance, the General Data Protection Regulation (GDPR) was developed in 2016 to provide people with more control over their data while also protecting the personal information of those living in the European Union and the European Economic Area. The California Consumer Privacy Act (CCPA) launched in 2018 mandates businesses to tell customers about the acquisition of their data. It is one example of a state policy being developed in the United States [166]. Process data, operation data, and other inspection and maintenance data are some of the most important information that pipeline companies need to secure to avoid any interruption in their businesses and operations. |
Required Skillset | To obtain the best results from the data collected over the years, the oil and gas sector is facing difficulty in obtaining the right skills. Machine learning techniques and approaches are relatively new to people working in the energy pipeline industry. There are not enough ML specialists in this field, which hinders the potential to develop successful models that will bring benefits to business or predict issues to control unwanted events. |
Affordability | To develop a significantly advanced data analytics system in order to use machine learning techniques, pipeline owners will require data engineers/scientists with sound technical knowledge of data analytics, modeling, and mathematics. Without these skills, companies are not able to start with a good digital transformation system. |
Understanding the Algorithms | Given the complexity of machine learning, data scientists are required to have expertise in this particular field and an in-depth understanding of science, technology, and mathematics to develop ML models to achieve the best results. Many businesses lack the internal expertise necessary to comprehend algorithms and how they operate, which can cause them to lose out on crucial insights. |
Class Imbalance | SCC occurrences in pipelines are typically rare events in the overall dataset. This class imbalance can lead to biased models that struggle to accurately detect SCC instances. Techniques such as oversampling, undersampling, or synthetic data generation can be employed to address the class imbalance issue and ensure that the model is trained on a balanced dataset. |
ML Model Generalization | Developing a generalized ML model that can be applied to the detection of unseen pipeline conditions is difficult. A model should be capable of detecting SCC across different pipeline sections, varying stress levels, and diverse corrosion environments. Adequate model evaluation and validation of unseen data are necessary to assess the generalization capability of a model. |
Obtaining the Right Data/Information | Obtaining the appropriate data to train ML models is one of the biggest challenges we are facing. ML models may not perform as well as they should since data are frequently siloed, erroneous, or incomplete. Therefore, this requires careful data gathering, processing, and curation for the purpose of model training. |
Lack of Training Data | The most crucial step in ML model development is training the model using enough data in order to let the model obtain reliable outputs. Less training data will result in model outputs that are biased or erroneous. |
Infrastructure Requirements | In some oil and gas companies, the data infrastructure is inadequate, which makes it difficult to find the required data in the data retrieval process. Therefore, it is an essential requirement to maintain an appropriate data management infrastructure in a company for the easy use of available data to dig out embedded values. This will make testing various tools easier and also make data transfers easier. |
Feature Selection | Identifying the most informative features or input parameters for predicting SCC in gas pipelines can be a challenge. Different factors, such as pipe material, temperature, pressure, pH, environmental variables, and pipe geometry, can influence SCC. |
Incorporating Time-Dependent Factors | SCC in gas pipelines is a complex phenomenon that can evolve over time because of various factors, including aging, environmental changes, and operational conditions. Capturing and incorporating the temporal aspect of SCC into ML models can be a research gap. |
Lack of Labeled Data | ML models typically require labeled data for training and validation. However, obtaining labeled data for SCC in oil and gas pipelines can be difficult given the complex and expensive nature of conducting inspections and assessments. |
4. Future Perspective
- ▪
- Scale of Data: The amount and variety of data collected by different sensors installed on pipeline systems is enormous, and these data need to be processed by pipeline owners. ML models can be programmed to analyze data independently, draw conclusions, and predict any damage or degradation to avoid unwanted downtime or safety concerns.
- ▪
- Finding Anomalies: A machine learning algorithm learns on its own from the datasets it examines, increasing analytical accuracy with each run. Because it happens automatically, this iterative learning process is special and useful; in other words, ML algorithms can find hidden insights without being deliberately trained to do so. They can detect any anomalies, analyze online trends, and tell pipeline operators to take action in advance in order to continue pipeline operations under safe operating parameters.
- Design data;
- Field data;
- Maintenance history;
- Experimental data;
- Simulated data.
- Available models need to be tested in the field, and a model’s accuracy needs to be verified in a controlled environment. This might be achieved through collaborations between model developers and plant/industry research teams in data collection and model testing.
- Data selection can be made better by including more details about the methods used for data collection, generation, and pre-processing. The literature lacks good-quality data, especially for machine learning where labeled data are required. This subject needs a good review in order to dig up more relevant data and clean that data.
- Both field testing and laboratory experiments must be used to evaluate SCC in terms of its severity and frequency of occurrence. Forecasting and management become critical for unveiling external corrosion-provoked deterioration events where machine learning might help to predict SCC and, hence, aid in the determination of remaining useful life based on identified SCC anomalies and their growth. To achieve this, it is of great interest to develop a framework applied to the detection of SCC, which is referred to in Figure 10.
Proposed Framework to Identify SCC in Energy Pipelines
5. Conclusions
- ▪
- There still needs to be an effort to develop the best technologies and modeling approaches to enhance SCC detection capabilities using machine learning.
- ▪
- Further effort is required to make use of information about environmental factors that influence corrosion, including ambient temperature and humidity; process factors like pH, stream temperature, and pressure; material factors like material type, coating type, and coating thickness; corrosion protection; and visual inspection data.
- ▪
- Efforts should be made to develop interpretable ML models that incorporate domain knowledge and expert input.
- ▪
- It is worth developing new data-processing and data management methods to ensure the data availability and quality required for energy pipeline SCC prediction and integrity management.
- ▪
- A new framework to guide the use and development of ML models for SCC detection/prediction is required.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Author Names | Reference No. | Year | Brief Details | Comments |
---|---|---|---|---|
Al-Sabaeei et al. | [167] | 2023 | A systematic review highlights the complexity and effectiveness of ML methods in predicting pipeline failures, emphasizing factors such as dataset variations, data sources, and model complexity. It underscores the success of ANNs, SVMs, and HML in detecting defects, focusing on corrosion while also identifying a need for more diverse research on other failure types. |
|
Ma et al. | [168] | 2023 | A novel hybrid approach is presented to effectively estimate the burst pressure of corroded pipelines. It incorporates a feature space with physical importance and a fusion mechanism that combines empirical formula and collective learning. The suggested model, which uses the light gradient-boosting machine, exhibits better interpretability through feature importance analysis. |
|
Alamri A. H. | [13] | 2022 | This review summarizes the current state of ML applications in SCC for risk assessment. It identifies existing knowledge gaps, discusses challenges, and outlines future perspectives on utilizing ML and AI in corrosion risk assessment. |
|
Liu and Bao | [169] | 2022 | Explores the application of ML in automated pipeline condition assessment, leveraging advanced sensing technologies to analyze routine operations, NDT, and computer vision data. |
|
Soomro et al. | [170] | 2022 | Emphasizes the limitations of existing probabilistic models; this research advocates for Bayesian network approaches, offering insights, methodologies, and dataset considerations for risk analysis in evaluating corroded hydrocarbon pipelines. |
|
Soomro et al. | [16] | 2022 | Emphasizes the emerging role of machine learning in predicting pipeline corrosion, mainly through hybrid models like ANNs and SVMs, while also addressing current research gaps and proposing future directions for enhancing accuracy and validation in this evolving field. |
|
Coelho et al. | [10] | 2022 | This study emphasizes that localized corrosion and inhibition efficiency prediction is recommended, requiring large, high-quality training data and collaboration for systematic ML integration into the corrosion community. |
|
Wasim and Djukic | [144] | 2022 | This review includes an analysis of monitoring tools; models for corrosion prevention, prediction, failure occurrence, and remaining life; and insights into external corrosion management, reliability-based and risk-based models, and integrity assessment using machine learning and fuzzy logic approaches. |
|
Khakzad et al. | [160] | 2022 | Using a Bayesian network and an empirical corrosion simulation model, this research estimates corrosion rates based on factors like pipe diameter and flow conditions, subsequently converting these predictions into a distribution of failure probabilities. |
|
Seghier et al. | [108] | 2022 | Presents a robust ensemble learning approach for accurate internal corrosion rate prediction in oil and gas pipelines, utilizing four models: random forest, adaptive boosting, gradient boosting regression tree, and extreme gradient boosting. |
|
Soomro et al. | [81] | 2021 | This study proposes ML-based algorithms to estimate the probability of failure, leveraging extensive simulations to generate a rich dataset for comprehensive validation and providing insights into improved reliability assessment in the industry. |
|
Sheikh et al. | [155] | 2021 | Employing a hybrid approach integrating machine learning techniques, this research successfully predicts corrosion severity levels with high accuracy based on distinct features extracted from acquired acoustic emission data. |
|
Rachman et al. | [29] | 2021 | Explores the integration of ML in pipeline integrity management (PIM). This review covers ML applications across PIM elements such as inspection, monitoring, maintenance, and analysis techniques and addresses current challenges while also highlighting future research opportunities. |
|
Reddy et al. | [109] | 2021 | Emphasizing the importance of early detection and prevention, this review explores sensor technologies employing physical and electrochemical techniques, discussing their recent developments, sensitivity, selectivity, and standard inspection methods for corrosion monitoring. |
|
Ossai, C. I. | [171] | 2020 | This study uses a data-driven methodology to estimate increased corrosion defect depth (CDD) in oil and gas pipelines using a subspace clustering neural network (SSCN) and particle swarm optimization (PSO). |
|
Jiang, P. | [14] | 2018 | This thesis addresses the growing global demand for risk analysis in corrosion- and SCC-related failure events. It introduces an innovative method utilizing machine learning, including ensemble methods and support vector machines (SVMs) for automatic risk analysis. |
|
Ratnayake and Antosz | [140] | 2017 | Presents a novel approach to risk-based maintenance (RBM) analysis using fuzzy logic. The proposed approach extends the traditional RBM framework by incorporating fuzzy sets to represent the uncertainty associated with risk factors. |
|
Aljaroudi et al. | [123] | 2016 | Addresses the critical issue of offshore pipeline leak-detection system failures, emphasizing their potential operational and environmental consequences. Introduces a risk-based assessment methodology to evaluate system integrity, quantify associated risks, and guide decision-makers in determining appropriate preventive measures based on an acceptable risk threshold. |
|
Hasan, A. | [124] | 2016 | Introduces a risk-based security management method utilizing an analytic hierarchy process (AHP) model to assess the likelihood of pilferage in different pipeline sections, aiding in prioritizing security measures for effective prevention. |
|
Guo et al. | [114] | 2016 | Introduces a robust risk evaluation method utilizing a fuzzy Petri net (FPN) model to assess potential hazards in long-distance oil and gas transportation pipelines. |
|
Parvizsedghy and Zayed | [127] | 2016 | This work employs a neuro-fuzzy technique; the study develops a model utilizing historical data to predict and assess the financial consequences of potential failures, offering an 80% accurate tool for practitioners and academics involved in the risk assessment of gas pipelines. |
|
Zhou et al. | [128] | 2016 | Provides an analytical model based on fuzzy logic to determine the probability of corrosion-related issues in energy pipelines, considering corrosion cracking and thinning to be important variables. This model offers important insights into corrosion failure likelihood by considering variables like inspection efficacy and timing. |
|
Lu et al. | [125] | 2015 | This study offers a new method of assessing the possible risks related to natural gas pipeline leaks. The approach makes use of a risk matrix in addition to a bowtie model. |
|
Zhou et al. | [131] | 2015 | Provides a novel strategy for estimating the service time of subterranean gas pipelines before corroding under the cyclically loading condition. The methodology employs cumulative damage rates, models corrosion defect depths as an exponential function of elapsed time, and computes remaining life by using an iterative approach. |
|
De Masi et al. | [156] | 2015 | Addresses the growing challenge of maintaining the integrity of hydrocarbon pipelines over long distances because of aging plants and components in the oil and gas industry. By leveraging an ensemble of artificial neural networks (ANNs), the proposed ML approach demonstrates promising results in predicting the complex evolution of corrosion, outperforming traditional deterministic models and single-ANN models. |
|
El-Abbasy et al. | [145] | 2015 | Proposes a condition assessment model and uses both an analytic network process and a Monte Carlo simulation to consider the uncertainty of factors affecting pipeline conditions and the interdependency relationships between them. |
|
De Masi et al. | [92] | 2014 | Highlights the role of reliable corrosion predictions in pipeline integrity management, reducing economic impact, and preventing environmental damage. |
|
Ismail et al. | [152] | 2011 | Explores SCC in austenitic stainless steel in high-temperature aquatic surroundings, employing fact-based techniques such as classical statistics, machine learning, and fuzzy logic. The decision tree approach was found to be highly effective, demonstrating superior performance and intelligibility in addressing the investigated problem. |
|
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Searching Index | Specific Content |
---|---|
Article Type | Publications in books, journals, and conferences |
Database | Web of Science, IEEE Xplore, Elsevier, Springer |
Classification | By the type of publication (i.e., concept, case study, and review), nationalities, application segments, enabling technologies, and affiliations (i.e., universities and industries) |
Focus | Determine opportunities and challenges related to SCC detection and prediction in the context of oil and gas |
Types of Learning | Data | Goal |
---|---|---|
Supervised | Labeled | Learn a mapping function |
Unsupervised | Unlabeled | Find patterns |
Semi-supervised | Labeled and unlabeled | Define a mapping function |
Reinforcement | Trial and error | Maximize rewards |
Corrosion Sensor Detection Technique | Type of Corrosion | Corrosion Phenomena/Parameter Assessed | Sensitivity | Field Monitoring Use |
---|---|---|---|---|
Acoustic emission (AE) | Stress corrosion cracks, pitting corrosion | Acoustic energy (impingement, leaks, and cracks) | Medium | Yes |
Image processing techniques (IPT) | General, localized corrosion, SCC, erosion–corrosion | Morphology of the corroded surface (image color, texture, and shape characteristics) | High | Yes |
Electrochemical noise (EN) | Uniform corrosion, localized corrosion (pitting, crevice), SCC | Electrical noise on the corrosion potential or current | High | Yes |
Hydrogen monitoring (HM) | Erosion–corrosion, stress corrosion cracking | Hydrogen diffusion through metal | High | Yes |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hussain, M.; Zhang, T.; Chaudhry, M.; Jamil, I.; Kausar, S.; Hussain, I. Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning. Machines 2024, 12, 42. https://doi.org/10.3390/machines12010042
Hussain M, Zhang T, Chaudhry M, Jamil I, Kausar S, Hussain I. Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning. Machines. 2024; 12(1):42. https://doi.org/10.3390/machines12010042
Chicago/Turabian StyleHussain, Muhammad, Tieling Zhang, Muzaffar Chaudhry, Ishrat Jamil, Shazia Kausar, and Intizar Hussain. 2024. "Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning" Machines 12, no. 1: 42. https://doi.org/10.3390/machines12010042