Pipeline Integrity Management Research Articles

Leakage detection of an acoustic emission pipeline based on an improved transformer network

Pipeline leakage detection is an integral part of pipeline integrity management. Combining AE (Acoustic Emission) with deep learning is currently the most commonly used method for pipeline leakage detection. However, this approach is usually applicable only to specific situations and requires powerful signal analysis and computational capabilities. To address these issues, this paper proposes an improved Transformer network model for diagnosing faults associated with abnormal working conditions in acoustic emission pipelines. First, the method utilizes the temporal properties of the GRU and the positional coding of the Transformer to capture and feature extract the data point sequence position information to suppress redundant information, and introduces the largest pooling layer into the Transformer model to alleviate the overfitting phenomenon. Second, while retaining the original attention learning mechanism and identity path in the original DRSN, a new soft threshold function is introduced to replace the ReLU activation function with a new threshold function, and a new soft threshold module and adaptive slope module are designed to construct the improved residual shrinkage unit (ASB-STRSBU), which is used to adaptively set the optimal threshold. Finally, pipeline leakage is classified. The experimental results show that the NDRSN model is able to make full use of global and local information when considering leakage signals and can automatically learn and acquire the important parameters of the input features in the spatial and channel domains. By optimizing the GRU improved Transformer network recognition model, the method significantly reduces the model training time and computational resource consumption while maintaining high leakage recognition accuracy. The average accuracy reached 93.97%. This indicates that the method has good robustness in acoustic emission pipeline leakage detection.

Uplifting the study of the inline inspection technique on the buckling pipelines in pipeline integrity management strategy

This work reports the development of inline inspection (ILI) methodology to enhance the pigging activity for the dented pipeline, facilitate the pigging process to prevent Pipeline Inspection Gauge (PIG) from getting stuck and improve the safety passage for buckled pipelines. The recent report unveils the condition of the UNPIGGABLE pipelines, which reduce the inner diameter of pipelines to 257.51 mm, equivalent to 27.58 % of the initial diameter and restricts the pigging activity. In this report, the pull-through test coupled with the collapsibility test was conducted. The success of the test above allows the ILI equipment based on the magnetic flux leakage (MFL) technique to record the internal and external wall loss inwardly and geometric defect on diameter of the pipelines. The prepared artificial dented pipeline was made before it underwent several tests. Based on the pull-through test, the maximum force of 27000 N is more significant than the pipeline operating pressure to enable the MFL tool to pass through the pipelines despite exhibiting the geometry anomaly. Compressing the opposite magnetic yoke of the collapsibility test is critical, showing that the ILI MFL tool reaches its maximum compression of 242 mm. The value is lower than the minimum internal diameter of 257 mm. The ILI results show that the highest metal loss was achieved at 73 % at 15504 m at the bottom of the inspected pipelines. At the same time, the dented area reduces to more than 6 % of the pipelines’ nominal outer diameter and imposes the pipe’s integrity status to red. The distinctive result of the research can be used to model the future unprecedented pigging process when buckles appear in pipelines

In-Line Inspection (ILI) Techniques for Subsea Pipelines: State-of-the-Art

Offshore oil and gas resources play a crucial role in supplementing the energy needs of human society. The crisscrossing subsea pipeline network, which serves as vital infrastructure for the storage and transportation of offshore oil and gas, requires regular inspection and maintenance to ensure safe operation and prevent ecological pollution. In-line inspection (ILI) techniques have been widely used in the detection and inspection of potential hazards within the pipeline network. This paper offers an overview of ILI techniques used in subsea pipelines, examining their advantages, limitations, applicable scenarios, and performance. It aims to provide valuable insights for the selection of ILI technologies in engineering and may be beneficial for those involved in pipeline integrity management and planning.

Predicting failure pressure of corroded gas pipelines: A data-driven approach using machine learning

This paper presents a novel methodology utilizing machine learning (ML) techniques to accurately forecast the failure pressure of corroded pipelines, which are pivotal for oil, gas, and petroleum product transport. In contrast to prior approaches, this study overcomes their limitations by integrating physically significant factors tied to corroded pipeline failure mechanisms and delivering interpretable models. Robust ML models are constructed using dependable experimental data from pertinent literature sources, ensuring research rigor. To bolster model interpretability, the SHAP method is deployed, facilitating a comprehensive grasp of each feature's impact on predictions. The performance and efficacy of the developed ML models are rigorously assessed using real-world pipeline incidents reported by the PHMSA in the US, further validating their practical utility. The findings not only affirm the models' precision and dependability but also yield valuable insights, underscoring their importance. This study makes a substantial contribution to the enhancement of pipeline integrity management by enabling precise failure pressure predictions in corroded pipelines. The insights gleaned from this research hold practical implications, enabling well-informed choices in pipeline design, integrity management, and risk assessment. Thus, it stands as a valuable resource for pipeline operators aiming to optimize resource allocation and bolster overall pipeline safety and efficiency.

Development of a convolutional neural network model to predict the size and location of corrosion defects on pipelines based on magnetic flux leakage signals

This study employs the convolutional neural network (CNN) to predict the dimensions and locations of corrosion defects on steel pipelines based on magnetic flux leakage (MFL) signals. Extensive three-dimensional parametric finite element analyses are carried out to generate MFL signals corresponding to semi-ellipsoidal-shaped corrosion defects with different sizes and locations on a pipe model. The white noises characterized by different signal-to-noise ratios are considered in the analysis to represent the measurement errors in the real MFL inspection tool. The numerically generated MFL signals are used to train and validate the CNN model to predict the dimensions and locations of the corrosion defects. The results indicate that the developed CNN model achieves a high predictive accuracy. The study demonstrates the application of CNN model to improve the pipeline integrity management practice.

Multi-objective maintenance strategy for corroded pipelines considering the correlation of different failure modes

Corrosion is the leading cause of leakage and burst failures in long-distances gas pipelines. Periodic maintenance is often used to extend the service life of the pipeline. The presence of multiple performance indicators with competing objectives needs to be taken into account for pipeline maintenance strategy. To address these challenges, this paper proposes an approach based on multi-objective maintenance optimization. The copula function is applied to characterize the correlation of failure modes (leakage and burst). With the aims to optimize availability and cost rate, a multi-objective maintenance optimization model is established, and it is solved with the non-dominated sorting genetic algorithm II (NSGA II). The results show that the Pareto frontier of multi-objective optimization can provide more feasible maintenance schemes. Moreover, the effects of major variables on the availability and cost rate are investigated. It is found that compared with other parameters, downtime cost has the greater impact on performance indicators. The proposed method can provide theoretical reference for pipeline integrity management, thereby ensuring that the pipeline is in a safe state during operation.

Safety Analysis and Condition Assessment of Corroded Energy Pipelines under Landslide Disasters

Corrosion poses a significant risk to the safety of energy pipelines, while landslide disasters emerge as the primary threat responsible for triggering pipeline failures across mountainous areas. To date, there is limited research focused on the safety of energy pipelines considering the synergistic effect of corrosion and landslides. The present study proposes a finite element (FE)-based model to assess the condition of corroded pipelines under landslides. The effects of corrosion dimensions (length and depth) and location are determined. A novel equation is finally developed to predict the maximum stress and determine the most disadvantageous position for corroded pipelines under various landslide displacements. The results demonstrate that (1) as the landslide progresses, the pipeline’s stress significantly increases; (2) corrosion depth has a more significant impact on the pipeline condition than the corrosion length, and it is positively correlated with the pipe’s stress; (3) the maximum stress exhibits a nonlinear relationship with the landslide-facing position and the corrosion circumferential location; and (4) when the axial position of the corrosion is more than 6.5 m away from the center of the landslide, the location of maximum stress shifts from the corrosion region to the central section of the pipeline within the landslide. This work contributes to helping pipeline owners to understand the applicability of energy pipelines subjected to the combined effects of corrosion and landslides and provides support for future risk assessment efforts in pipeline integrity management.

Prediction of pipeline fatigue crack propagation under rockfall impact based on multilayer perceptron

Recently, the artificial intelligence technologies have been widely used in the field of pipeline integrity management. When crossing mountains, pipelines would inevitably encounter rockfall impact, which will potentially affect the growth of crack. However, previous research barely investigated the effect of sudden rockfall impact on health management of pipelines with fatigue cracks. To overcome this limitation, a novel crack propagation prediction algorithm based is proposed for pipelines subjected to rockfall impact. The stress intensity factor (SIF) rockfall impact ratio is introduced to describe the interaction effect of rockfall on the fatigue crack of pipelines. And the dynamic SIF values are acquired by finite element modeling (FEM) where 354 models with different parameters are analyzed. To more accurately forecast the crack growth under the rockfall impact, a method integrates multilayer perceptron (MLP) with Paris’ law is proposed based on the above reliable database. Two parameters impacting the performance of the network including the number of neurons in the hidden layer and the hidden layer's activation function are evaluated and network with the most precise prediction results is selected. Quantitative analyses are performed for key factors including rockfall mass, impact velocity, impact position and crack size. The prediction results using dynamic SIF values are compared with the static ones to indicate the effect of rockfall on the crack propagation. The proposed method is valuable to support decision-making in pipeline reliability assessment and integrity management.

Information Fusion and Data Augmentation for Risk-based Maintenance Optimization of Hydrogen Gas Pipelines

Demand for energy is increasing every year and hydrogen is being seen as a good alternative to conventional natural gas. The current focus is on the use of existing pipeline infrastructure for the transport of hydrogen gas, and it is necessary for us to ensure the safe and efficient operation of the pipeline infrastructure given the risks posed by hydrogen. Pipeline integrity management is critical for hydrogen transport and there are knowledge gaps for the impact of hydrogen on the pipeline integrity and operational considerations, thus hindering the pipeline operators from adopting hydrogen into their networks. To realize the concept of transporting hydrogen through existing pipeline systems, it is necessary to have reliable risk assessment and maintenance optimization frameworks in place. A Bayesian network methodology is proposed to fuse information from multiple sources obtained by multimodality diagnosis of pipe materials and Bayesian updating will be incorporated to reduce the uncertainty arising from different random variables. Risk assessment of the pipeline systems will be carried out based on the posterior distributions of the random variables. Given the predicted risk level, we then propose a risk-based maintenance optimization framework to minimize the maintenance costs while ensuring the safe operation of the pipeline systems.

A risk-based approach to inspection planning for pipelines considering the coupling effect of corrosion and dents

RBI, referring to a risk-based approach to inspection planning, is an established pipeline integrity management method. Both corrosion and dents are the primary threats to pipeline integrity. However, they are often treated separately in RBI without considering their interactions. This coupling may lead to a synergic effect on integrity degradation. The present study proposes an RBI planning framework for pipelines considering external corrosion and dents. Time-dependent pipeline deterioration by dents and corrosion is modeled probabilistically using a Dynamic Bayesian Network (DBN), in-line inspection (ILI) data, and corrosion propagation knowledge. Two failure scenarios (leakage and burst) are considered. The hybrid method, integrating Monte Carlo Simulation (MCS) and Latin Hypercube Sampling (LHS) technique, estimates the pipeline's Probability of Failure (PoF) over time. The pipeline failure risk is quantified by monetizing the Consequence of Failure (CoF). An optimization model of loss-maintenance total expected cost is introduced to determine the optimum inspection period using maximum acceptable risk (MAR) and the lowest total expected cost. A cost-benefit analysis (CBA) is finally implemented to choose appropriate risk reduction measures. The proposed framework is robust and well-validated by a case study on an in-service pipeline.

An intelligent feature recognition method of natural gas pipelines based on shapelet and blending fusion model

Despite the availability of pipeline bending strain detection technologies based on inertial measurement unit, there is a lack of intelligent and efficient methods for accurately identifying pipeline features by bending strain. Therefore, this paper proposes a novel method for identifying features in natural gas pipelines based on shapelet and blending fusion model. Specifically, the shape features of the bending strain data are extracted and transformed by shapelet. Then a blending fusion model with SVM, Decision Tree and Gradient Boosting as base learners and Random Forest as meta-learner is constructed. Finally, the extracted features are fed into the blending fusion model for pipeline feature recognition. The model is trained with bending strain data obtained from a real natural gas pipeline, the results indicate that the recognition accuracy of the proposed method is 97.17%. Compared with other models, the superiority of the proposed model is verified, and it is proved that the proposed method has better accuracy than the existing models (over 1.3%). Overall, the method proposed in this paper can be effectively combined with the in-line inspection system to provide a reference for pipeline companies to carry out pipeline integrity management.

A REVIEW ON DESIGN OF CRUDE OIL PIPELINE WALL OPTIMIZATION DUE TO CORROSION AND HYDROGEN EMBRITTLEMENT

Crude oil pipeline infrastructures represent a high capital investment, and pipelines must be free from the risk of degradation due to corrosion and hydrogen embrittlement, which could cause environmental hazards and potential threats to human life. Pipeline integrity design, monitoring, and management become crucial, especially in subsea pipelines. Corrosion risk assessment is necessary to track pipeline reliability and life prediction effectively. The pipeline inspections cost a lot of money since the pipeline are hundreds of thousands of kilometres, and the maintenance process interrupts the production rates. Moreover, this cost will triple for the subsea pipeline as it requires special personal qualifications and tools, causing the oil and gas company to pay millions for frequent checks. It is proposed to utilize Finite Element Analysis (FEA) modelling technique and Computational Fluid Dynamics (CFD) to optimize the design and preventive maintenance prediction. Hence this paper aims to review the current work on this pipeline design problem, especially in the Tropical region, and discuss the factors that cause the deterioration of structural properties due to the presence of hydrogen within the crude oil.

Acoustic Field Imaging of Pipeline Turbulence for Noninvasive and Distributed Gas Flow Measurement

Precise control and measurement of the flow are essential for pipeline safety monitoring and economic accounting. Nowadays, the measurement of pipeline flow relies on point measurement schemes such as mechanical flowmeters, which invade the pipeline and can irreversibly damage the fluid pressure, especially when multipoint measurements are required. To overcome these problems, we have explored a novel technique in which the acoustic field properties of pipeline turbulence are reflected by distributed backscattering light field, from which the flow can be measured via acoustic field imaging. The acoustic field information is recorded by a helical deployment scheme using backscattering-enhanced optical fiber (BEOF) encapsulated in the polyethylene anticorrosive layer, thereby achieving highly sensitive, noninvasive, and distributed online monitoring of gas flows. We obtained 24-h flow fluctuation distributions with only one sensing fiber for two operating gas pipelines, and the flow measurement accuracy reached higher than 98% within the error range of −15% to 15%. Moreover, the minimum detectable flow is evaluated and it is demonstrated that the proposed scheme is extremely sensitive to weak flows. We successfully demonstrated noninvasive and distributed online measurement of operational pipeline flow, which will exhibit great potential for pipeline integrity management in the future.

Robust CNN-based flow pattern identification for horizontal gas-liquid pipe flow using flow-induced vibration

Determination of flow patterns is crucial for the prediction of unsteady flow parameters, which is important for multiphase flow pipeline integrity management. Thus far, various methods have been put forward, however, their robustness has yet to be thoroughly tested across varying environments. Despite recent progress in flow-induced vibration (FIV) of multiphase flows, variability due to measurement approach and pipeline conditions remains an under-investigated area. This paper proposes a non-intrusive, robust convolutional neural network (CNN) flow pattern identification method based on FIV analysis. FIV for horizontal gas–liquid pipe flow under various flow patterns is investigated experimentally by simultaneously analyzing acquired FIV signals and high-speed video. A range of pipeline scenarios is thoroughly studied, encompassing variations in flow conditions, measurement axes, pipe material, and installation conditions. Both Hilbert-Huang Transform (HHT) and Short Time Fourier Transform (STFT) are used to investigate the characteristics of FIV. The results show distinct flow patterns with the FIV signal varying with measurement axes and pipe conditions. Three processes are applied to increase identification robustness: HHT images to eliminate variability due to structure signatures and flow conditions; Ostu’s threshold to eliminate image magnitude difference; and data augmentation, applied to both time signals and images, to increase database diversity. From these, the morphological features associated with flow patterns are extracted. Pattern identification results using a neural network architecture trained by GoogLeNet show over 95% accuracy. To test the generalizability of the proposed flow pattern identification method, the trained method is employed to make predictions in various pipeline scenarios. The results show that the trained model attains an accuracy of over 90% when predicting the flow pattern using data from different axes, and an accuracy above 80% when predicting with different pipe material and installation conditions. During the model training phase, the integration of multiple databases or the implementation of transfer learning can result in a substantial improvement in accuracy, reaching a respectable level of at least 90%.

Performance evaluation method of oil and gas pipeline integrity management

The performance evaluation of integrity management has become the focus of attention because integrity management has become widely accepted by pipeline operators. This paper proposes a three-index system of pipeline integrity management performance evaluation (TISPIMPE), which includes system construction, implementation process, and pipeline health status, comprehensively evaluating the status, adequacy, and effectiveness of integrity management. It helps the pipeline operators to determine the weakness in each step. Based on the improved analytic hierarchy process (AHP), the different backgrounds of experts are fully considered, and experts' opinions are revised. The adjustment coefficient of performance evaluation is set up simultaneously, and the evaluation results are adjusted to make the results more reasonable. Taking the performance evaluation of pipeline integrity management of an oil and gas company as an example, it is shown that TISPIMPE has reasonable practicability and can accurately reflect the shortcomings of pipeline integrity management. TISPIMPE can effectively help pipeline managers comprehensively and systematically evaluate the performance of pipeline integrity management and gain an in-depth understanding of pipeline operation status.

Analysis and prediction of pipeline corrosion defects based on data analytics of in-line inspection

In-line inspection (ILI) is important to pipeline integrity management since it can detect pipeline defects and identify potential failure locations through periodical examinations. However, effectively evaluating defects based on ILI data is challenging. Measurements of ILI are easily influenced by instrument performance and maintenance activities, leading to unmatched and imbalanced data. Poor ILI data make it difficult to establish defect growth models based on multiple inspections. This study conducted comprehensive analysis of ILI data for evaluating corrosion defects of a steel pipeline. First, statistical analysis was performed on raw data to visualize distributions of corrosion depths and number of corrosions. Second, hierarchical clustering method was used to classify corrosion severity levels based on features of corrosion depth and estimated repair factor. The interaction effect between adjacent corrosions was considered. Machine learning methods, including k-nearest neighbor, support vector machine, random forest, and light gradient boosting machine were used to explore the relationship between the location parameters of adjacent corrosions and severity levels. Then, maximum corrosion depths and corrosion density were filtered from raw ILI data of multiple inspections, which were critical for pipeline failure prediction. Finally, distribution parameters were fitted to establish stochastic growth models on maximum corrosion depth and corrosion number density. This study presents data analytics based approach to obtain valid information from ILI data in practice.

An inventory of greenhouse gas emissions due to natural gas pipeline incidents in the United States and Canada from 1980s to 2021

Natural gas is believed to be a critical transitional energy source. However, natural gas pipelines, once failed, will contribute to a large amount of greenhouse gas (GHG) emissions, including methane from uncontrolled natural gas venting and carbon dioxide from flared natural gas. However, the GHG emissions caused by pipeline incidents are not included in the regular inventories, making the counted GHG amount deviate from the reality. This study, for the first time, establishes an inventory framework for GHG emissions including all natural gas pipeline incidents in the two of the largest gas producers and consumers in North America (United States and Canada) from 1980s to 2021. The inventory comprises GHG emissions resulting from gathering and transmission pipeline incidents in a total of 24 states or regions in the United States between 1970 and 2021, local distribution pipeline incidents in 22 states or regions between 1970 and 2021, as well as natural gas pipeline incidents in a total of 7 provinces or regions in Canada between 1979 and 2021. These datasets can improve the accuracy of regular emission inventories by covering more emission sources in the United States and Canada and provide essential information for climate-oriented pipeline integrity management.

Integrated dynamic risk assessment of buried gas pipeline leakages in urban areas

In urban areas, buried gas pipeline leakages could potentially cause numerous casualties and massive damage. Traditional static analysis and dynamic probability-based quantitative risk assessment (QRA) methods have been widely used in various industries. However, dynamic QRA methods combined with probability and consequence are rarely used to evaluate gas pipelines buried in urban areas. Therefore, an integrated dynamic risk assessment approach was proposed. First, a failure rate calculation of buried gas pipelines was performed, where the corrosion failure rate dependent on time was calculated by integrating the subset simulation method. The relationship between failure probability and failure rate was considered, and a mechanical analysis model considering the corrosion growth model and multiple loads was used. The time-independent failure rates were calculated by the modification factor methods. Next, the overall evolution process from pipeline failures to accidents was proposed, with the accident rates subsequently updated. Then, the consequences of buried gas pipeline accidents corresponding to the accident types in the evolution process were modeled and analyzed. Finally, based on the above research, dynamic calculation and assessment methods for evaluating individual and social risks were established, and an overall application example was provided to demonstrate the capacity of the proposed approach. A reliable and practical theoretical basis and supporting information are provided for the integrity and emergency management of buried gas pipelines in urban areas, considering actual operational conditions.

A Review of Leak Detection Systems for Natural Gas Pipelines and Facilities

Pipelines facilities, used for the transportation of natural gas in large quantities to homes and industries, remain the best economic, most reliable and safest mode of transport of energy. Despite these numerous advantages, gas pipelines have been enmeshed in various accidents and thefts, nonetheless this could be reduced if properly maintained and pipelines can last indefinitely without leaks. Pipelines are susceptible to leakages and rupture accidents as a result of age, corrosion, material defects, operational errors or other reasons. Pipeline failures may be caused intentionally (e.g. vandalism) or unintentionally (e.g. device/material failure and corrosion), which may result into irreversible damages such as financial losses, human casualties, ecological disaster and extreme environmental pollution. Leakages in natural gas facilities and installations require three vital aspects, namely: Gas Leakage Prevention, Gas Leakage Detection and Gas Leakage Mitigation. Many Gas Leak Detection methods are used for pipeline integrity management and especially for minimizing gas leakage. The performance of these methods depends on the approaches, operational conditions and pipeline networks. Also, there are some essential requirements and guidelines which must be met before we can consider any leak detection system suitable for production solutions, including sensitivity, reliability, accuracy and robustness. The attempt of this study is to carry out a critical review of these models, to ascertain the best model(s) applicable to natural gas leak detection. Keywords: Gas Leak Detection System, Leak Location, Leak Size DOI: 10.7176/JETP/13-2-02 Publication date: April 30 th 2023

A method to determine the uncertainties of solid particle erosion measurements utilizing machine learning techniques

The ability to predict solid particle erosion is a vital task for the integrity management of pipelines carrying solid particles in many industries. Usually, the accuracy of predicted values is assessed by comparing them with the measured erosion of some cases where data is available. Thus, defining the uncertainties of the experimental data of erosion is crucial to evaluate the reliability of the predictions of solid particle erosion and consequently, the decisions made based on these predictions. The databases gathered by many investigators that include erosion data of various test cases have been considered for this uncertainty investigation. These cases include erosion rate data collected using the ultrasonic thickness measurement technique for gas-sand flows in 0.0508, 0.0762, and 0.1016-m elbow geometries with various particle sizes (75,150,300,550 μm) and air velocities vary in the range between 11 up to 202 m per seconds. This study aims to develop a framework for defining the uncertainties of erosion experiments. For this purpose, first, the uncertainties of repeated experiments were estimated and then analytical analysis was used to generalize these results to cases where repeated results were unavailable. Based on this idea, a correlation between erosion and the most effective parameters was developed. Then, machine learning algorithms were used to determine a correlation between the erosion rate and chosen parameters including material hardness, pipe size, particle size, and flow velocity. The framework of the current model/method is based on the combined results of analytical analysis and machine learning procedure, and the estimated uncertainties are presented by lower and upper bounds curves of erosion data.