In-line Inspection Techniques Research Articles

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

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis.

The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

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.

Blending hydrogen in existing natural gas pipelines: Integrity consequences from a fitness for service perspective

Blending hydrogen in existing natural gas pipelines compromises steel integrity because it increases fatigue crack growth, promotes subcritical cracking and decreases fracture toughness. In this regard, several laboratories reported that the fracture toughness measured in a hydrogen containing gaseous atmosphere, KIH, can be 50% or less than KIC, the fracture toughness measured in air. From a pipeline integrity perspective, fracture mechanics predicts that injecting hydrogen in a natural gas pipeline decreases the failure pressure and the size of the critical flaw at a given pressure level. For a pipeline with a given flaw size, as shown in this work, the effect of Hydrogen Embrittlement (HE) in the predicted failure pressure is largest when a failure occurs by a brittle fracture. The HE effect on failure pressure diminishes with a decreasing crack size or increasing fracture toughness. The safety margin after a successful hydrostatic test is reduced and therefore the time between hydrotests should be decreased. In this work, all those effects were quantified using a crack assessment methodology (level 2, API 579-ASME FFS) considering literature values for KIH and KIC reported for an API 5L X52 pipeline steel. To characterize different scenarios, various crack sizes were assumed, including a small crack with a size close to the detection limit of current in-line inspection techniques and a larger crack that represents the largest crack size that could survive a hydrotest to 100% of the steel Specified Minimum Yield Strength (SMYS). The implications of a smaller failure pressure and smaller critical crack size on pipeline integrity are discussed in this paper.

Top-of-line corrosion via physics-guided machine learning: A methodology integrating field data with theoretical models

An artificial learning method for predicting top-of-line corrosion (TOLC) in oil/gas production from an existing oil field production data was presented, along with verifications and a few real-world applications. The developed algorithm was aimed to boost fundamental understanding of the day-to-day unit operations and periodic maintenance and inspection of oil/gas pipelines via a particular inline inspection technique (magnetic-flux-leakage measurements) and corrosion inhibitor applications. A recurrent neural network was utilized to establish a model for oil field production while a series of small neural networks representing the fundamental physics of TOLCs were included. The relevant physical models are those relating to (i) electrochemistry of corrosion mechanisms, (ii) gas–liquid phase flows and atomizations, and (iii) the condensation rates at the top of the lines. By incorporating these networks into the operation data, the developed learning algorithm gives accurate TOLC prediction for the complex oil field structures. The results show that the estimation models based on the physics-guided artificial neural network present the root-mean-square error within 10%. The developed software also offers insights into the essential parameters for the better prevention of the TOLC in pipelines.

Automated Box Data Matching for Multi-Modal Magnetic Flux Leakage Inspection of Pipelines

Magnetic flux leakage (MFL) is one of the most popular in-line inspection techniques to detect pipeline corrosion defects. However, the measurement limitation and random error associated with the individual MFL could introduce observable variations to the inspection. One potential way to reduce such variation is to fuse the inspection data from axial MFL (aMFL) and circumferential MFL (cMFL) due to their complementary measurement nature. Data from the multi-modal MFL must be matched before the fusion operation. However, there is no literature reporting the multi-modal MFL data matching yet. In this article, we propose an automatic box data matching framework that consists of four major steps, i.e., pre-processing, grouping, alignment, and matching. The pre-processing step accomplishes the 3-D to 2-D data mapping and cleansing. The grouping of the box data from the cMFL based on their location information is given in the following. Then, the coordinate systems of two MFL data sets are aligned. Finally, in the aligned coordinate system, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is revised to perform many-to-many matching. The experimental results demonstrate that the proposed framework can accurately match the multi-modal MFL box data. Thus, this solution will enable the multi-modal MFL data fusion for a more reliable and accurate pipeline corrosion assessment.

Analysis of Magnetic-Flux Leakage (MFL) Data for Pipeline Corrosion Assessment

Oil and gas pipelines transport and distribute large quantities of oil products and natural gas to industrial and residential customers over a long distance. However, pipeline failures could lead to enormous hazards and safety issues. Metal loss, due to corrosion, is a significant cause for pipeline failures. To detect and quantify metal loss, in-line inspection (ILI) is carried out periodically to assess the integrity of the pipeline. Among all the ILI techniques, magnetic-flux leakage (MFL) is the most popular one due to its high efficiency, robustness, and good applicability to both oil and gas pipelines. This article provides a comprehensive review of the pipeline corrosion assessment with the MFL technique from the data analytic perspective. The analyses of the MFL signal and data contribute to both corrosion quantification and prediction. In this article, the state of the art for the MFL measurement technique is briefly reviewed first. For corrosion quantification, the signal processing methods together with the characterization models, which aim to enhance the measurement and characterize the corrosion, are described and discussed. For corrosion prediction, this article investigates multiple MFL data matching methods, which align defects from successive ILI runs. Subsequently, corrosion growth models, which aim to predict the future corrosion status, are presented. Besides, the reliability analysis of the corroded pipeline is reviewed. The potential of fusing MFL with other non-destructive testing (NDT) techniques are explored as well. At the end of this article, we summarize the existing issues and describe the trends for future research on pipeline corrosion assessment.

The cross-disciplinary approach to analysis and forecast of operational damage tolerance of the oil pipeline system – part 2

The paper presents the cross-disciplinary approach to the analysis of the oil pipeline system based on the methodologies of tribo-fatigue and mechano-thermodynamics. The pipeline section is analyzed as a complex system pipe-soil- flow of liquid subject to the set of mechanical, thermal and friction loads. It is shown that theses loads are mainly repeatedlyalternated, and the pipe metal works in the multi-cycle fatigue conditions. The procedure of resonance accelerated fatigue tests is proposed, and their results are presented. Also, the unorthodox method of integrated wear-fatigue tests of the pipeline steel was proposed with the model of simultaneous pressure and wall friction actions. The presented field test results of pipes subject to the long-term operation showed that their fracture may occur not only in the near-weld zone, but also in the vicinity of internal corrosion damages. New models of three-dimensional stress-strain state and volumetric damage tolerance for the system pipe-soil-liquid flow were developed. These models were applied with regard to the pipe internal corrosion damages, defined using the inline inspection technique. A new efficient method to describe static and cyclic elastic-plastic fracture of the pipe steel with crack using the transverse strain is proposed and tested. Results of the computer-simulated propagation of the crack-like damage are based upon the model of deformed solid with dangerous volume. The new model is proposed for risk and safety assessment with regard to the ultrasonic inspection data. The algorithm of the ‘oil line pipe’ problem solution is presented for drafting a short-term plan of particular R&D actions.

The cross-disciplinary approach to analysis and forecast of operational damage tolerance of the oil pipeline system – part 1

The paper presents the cross-disciplinary approach to the analysis of the oil pipeline system based on the methodologies of tribo-fatigue and mechanothermodynamics. The pipeline section is analyzed as a complex system pipe-soil- flow of liquid subject to the set of mechanical, thermal and friction loads. It is shown that theses loads are mainly repeatedly-alternated, and the pipe metal works in the multi-cycle fatigue conditions. The procedure of resonance accelerated fatigue tests is proposed, and their results are presented. Also, the unorthodox method of integrated wear-fatigue tests of the pipeline steel was proposed with the model of simultaneous pressure and wall friction actions. The presented field test results of pipes subject to the long-term operation showed that their fracture may occur not only in the near-weld zone, but also in the vicinity of internal corrosion damages. New models of three-dimensional stressstrain state and volumetric damage tolerance for the system pipe-soil-liquid flow were developed. These models were applied with regard to the pipe internal corrosion damages, defined using the inline inspection technique. A new efficient method to describe static and cyclic elastic-plastic fracture of the pipe steel with crack using the transverse strain is proposed and tested. Results of the computer-simulated propagation of the crack-like damage are based upon the model of deformed solid with dangerous volume. The new model is proposed for risk and safety assessment with regard to the ultrasonic inspection data. The algorithm of the ‘oil line pipe’ problem solution is presented for drafting a short-term plan of particular R&D actions.

Defect detection strategies and process partitioning for single-expose EUV patterning

The key challenge for enablement of a second node of single-expose EUV patterning is understanding and mitigating the patterning-related defects that narrow the process window. Typical in-line inspection techniques, such as broadband plasma and e-beam systems, find it difficult to detect the main yield-detracting defects postdevelop, and thus understanding the effects of process improvement strategies has become more challenging. New techniques and methodologies for detection of EUV lithography defects, along with judicious process partitioning, are required to develop process solutions that improve yield. This paper will first discuss alternative techniques and methodologies for detection of lithography-related defects, such as scumming and microbridging. These strategies will then be used to gain a better understanding of the effects of material property changes, process partitioning, and hardware improvements, ultimately correlating them directly with electrical yield detractors.

Comparative Analysis of In-line Inspection Equipments and Technologies

Considering the global aging of pipelines and demand for new ones in more and more hostile environments, in-line inspection (ILI) based on the Non-Destructive Testing(NDT) was essential for pipeline operating company to protect their asset efficiently. This paper reviewed kinds of state of the art of ILI techniques and equipments, including geometry pig (GP), magnetic flux leakage pig (MFL PIG), ultrasonic pig (UT PIG), electromagnetic acoustic pig (EMAT PIG), eddy current pig (EC PIG), integrated function pig and specific function pig. Through the review of kinds of techniques, different approaches were compared, challenges and problems were highlighted and future research and direction was suggested.

A cognitive approach for quality assessment in laser welding

Quality assessment in laser welding is of outmost importance. A plethora of in-line inspection techniques have been developed identifying melt pool geometry and weld defects for quality evaluation. This paper aims to introduce a cognitive assessment method for the prediction of weld quality and classification into different quality categories. The study corresponds to camera-based monitoring approaches utilizing thermal images obtained from process simulation models where artificial defects were inserted. A dimensionality reduction technique is deployed, and an image processing technique is afterwards implemented to identify weld defects based on specific melt pool features. A classification algorithm has also been developed and validated.

Signal processing of magnetic flux leakage surface flaw inspect in pipeline steel

The magnetic flux leakage (MFL) method has established itself as the most widely used inline inspection technique for the evaluation of pipelines. The MFL signal is usually contaminated by various noise sources This paper presents the empirical mode decomposition (EMD) for enhancing signal-to-noise ratio. Experiments are conducted on the pipeline steel samples with different depth artificial defects. The MFL signal is decomposed into several intrinsic mode functions by EMD. Some modes are selected to reconstruct a new signal considering their frequencies and energy. The reconstructed signal has a better signal-to-noise ration. The results show that EMD technology is efficient in eliminating noise in the MFL signal.

A modified wavelet transform domain adaptive FIR filtering algorithm for removing the SPN in the MFL data

With the widespread application and fast development of gas and oil pipeline network, the pipeline inspection technology has been used more extensively. The magnetic flux leakage (MFL) method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise (SPN). SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. This paper presents a modified wavelet transform domain adaptive FIR filtering algorithm for removing the SPN in the MFL data. The advantage of the proposed algorithm is that it converges faster than the time domain adaptive SPN filtering algorithm. Results from application of the modified algorithm to the MFL data from field tests show that the modified algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.

2D defect reconstruction from MFL signals by a genetic optimization algorithm

The magnetic flux leakage (MFL) method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. An important problem in MFL nondestructive evaluation (NDE) is signal inverse problem, wherein the defect profile and its parameters are determined using the information contained in the measured signals. This paper proposes a genetic-algorithm-based inverse algorithm for reconstructing 2-D defect from MFL signals. In the algorithm, a radial basis function neural network (RBFNN) is used as forward model, and genetic algorithm is used to solve the optimization problem in the inverse problem. Experimental results are presented to demonstrate the effectiveness of the proposed inverse algorithm.

Ultrasonic in-line inspection technique for contact materials: E. Jost and G. Fontaine IEEE Trans. Components, Hybrids Mfg Technology. CHMT-3(1), 79 (March 1980)

Ultrasonic in-line inspection technique for contact materials: E. Jost and G. Fontaine IEEE Trans. Components, Hybrids Mfg Technology. CHMT-3(1), 79 (March 1980)

Ultrasonic in-line inspection technique for contact materials : Ernest Jost and Gerard Fontaine. IEEE Trans. Components, Hybrids, Mfg. Technol., Chmt-3 (1) 79 (March 1980)

Ultrasonic in-line inspection technique for contact materials : Ernest Jost and Gerard Fontaine. IEEE Trans. Components, Hybrids, Mfg. Technol., Chmt-3 (1) 79 (March 1980)