Offshore Pipelines Research Articles (Page 1)

Abstract In underwater applications, the mechanical noise has become one of the key limiting factors affecting the acoustic stealth performance of underwater vehicles. Among them, the main power unit noise and propeller noise have been effectively controlled. However, the hydraulic pipeline system noise still needs to be solved. In this paper, a piezoelectric actuated attenuator for underwater pipeline pulsation suppression is developed. The attenuator changes the local liquid volume in the secondary channel through the output displacement of the piezoelectric stack, causing regular changes in the pressure in the area, thereby generating secondary pressure pulsations to offset the pressure pulsations in the main path. The system identification is conducted based on a customized fabricated prototype and the built experimental set. Then an active noise control (ANC) technology based on the developed attenuator using enhanced variable step-size filtered-x least mean square (VSS-FxLMS) algorithm is proposed to suppress low frequency vibration and underwater noise in the pipelines. The ANC is tested and compared with existing algorithms to show the effectiveness. Results show that the proposed VSS-FxLMS can suppress the initial pulsation fluctuations fast by converging within 1 second and maintaining a low residual error in the steady state stage. In terms of quantitative comparison of the noise attenuation, PID and phase shift controllers achieved attenuation effects of –7.62 dB and –8.04 dB respectively, while the FxLMS algorithm improved to –10.34 dB. As for the proposed VSS-FxLMS, it shows better control effects in each stage, and finally achieves a noise attenuation of –11.83 dB. The developed piezo-actuated attenuator as well as its ANC algorithm can provide a new structure and control scheme for noise attenuation of underwater vehicle pipeline systems.

The diamond wire cutter (DWC) is a crucial tool for subsea pipeline repairs. However, in the event of a saw wire failure, replacing and connecting the diamond wire on the seabed is unfeasible due to constraints in underwater construction conditions. To extend the service life of diamond wires, this study examines the micro-wear morphology of diamond beads through low-temperature grinding experiments. The wear mechanisms of diamond grains are revealed, and the wear process of the diamond bead is deduced. Utilizing wear experimental data and a BP neural network, the wear rate prediction model of electroplated diamond wire during subsea pipe continuously cutting is constructed. Additionally, the effects of cutting parameters, wire lengths, and pipe diameters on bead wear rates are analyzed. Findings indicate that the wear behavior of diamond grains includes fracture, thermal wear, and grain detachment. Compared to the (111) crystal surface, the (100) crystal surface exhibits superior wear resistance. During electroplated diamond wire continuously cutting subsea pipes, the saw wire achieves optimal service life at a cutting speed of 22 m/s and a feed speed of 1.1 mm/min. This study offers theoretical guidance for diamond wire underwater construction programs.

Finite element analysis on the snap-through buckling of subsea pipelines initiated by the imposed residual curvature with nonlinear lateral soil resistance

Experiment investigation of scour and self-burial of sagging subsea pipelines in steady current

Flow pattern transition in N-shaped subsea pipeline: Experimental insights into gas hold up and gravity effects on gas-liquid transportation

End-to-End graph neural network framework for precise localization of internal leakage valves in marine pipelines based on Intelligent graphs

Impact of the Number of Imperfections and Spacing on Subsea Pipeline Buckling Behavior

Abstract The linear explosively formed projectile (LEFP) is a special type of linear shaped charge (LSC) and is developed on the basis of the explosively formed projectile. Known for their excellent penetration performance and directional destruction capacity, LEFPs are widely utilized in explosion dismantlement, petroleum exploration, concrete crushing, metal cutting, and penetrator interception. LEFPs can also induce multimode damage—combining penetrator impact, shock waves, and bubble effects—during underwater detonations. Thus, they are employed in applications including subsea pipeline disruption and effective neutralization of underwater vehicles. Adequate formation effect is a criterion for a desirable penetration performance of shaped charges. Therefore, investigating the formation characteristics of LEFPs in different media and optimizing the structural parameters are important tasks. In this study, the arbitrary Lagrangian—Eulerian (ALE) method is adopted for numerical simulation, and the efficacy of the ALE method is verified by the consistency between numerical and experimental results. Then, the formation effects of LEFPs in pure air medium, water medium, and water with air cavity are investigated separately. The results show that the stabilized velocity of the penetrator in air is 1 355 m/s, while the velocity is only 423.6 m/s in water. Furthermore, the liner cannot be completely closed in water and cannot form a slender penetrator as in air. After the addition of the air cavity, the stabilized velocity of the penetrator increases to 966.6 m/s, and its morphology is significantly enhanced. Moreover, explosive material, liner material, and liner thickness are chosen as the primary factors for orthogonal optimization. The optimized factor combination is RDX—Copper—1 mm. The peak velocity of the penetrator, the velocity before penetration, the velocity after penetration, and the length of penetration increase by 50.8%, 44.9%, 26%, and 10.3%, respectively.

External impact represents a significant hazard for subsea pipelines, where wedge-shaped indenters can induce dents through localized stress concentration. This study investigates the denting process, explicitly accounting for the pressure difference between internal hydrocarbon fluid and external seawater. A theoretical model based on a string-on-plastic foundation approach was developed to analyze the problem, deriving analytical solutions for the indentation force-depth relationship. This model considered two distinct pipeline end boundary conditions. An empirical formula predicting the load-deflection curve was established. The accuracy of the analytical model and the derived formula was assessed through comparisons with existing models from the literature and experimental data. Furthermore, the influence of the pressure difference on the denting process was systematically investigated, quantifying its effects on indentation depth across pipelines with varying diameter-to-thickness ratios and for distinct end boundary conditions, rotationally restrained and free ends. This research provides foundational insights relevant to the design and failure analysis of pressurized subsea pipelines.

ABSTRACT Internal valve leakage in offshore pipeline systems poses serious risks to energy transportation safety, particularly under high environmental noise, complex structural interactions, and long-distance signal attenuation. Acoustic emission (AE), as a vital non-destructive testing (NDT) technique, has shown promise for pipeline structural health monitoring (SHM) but faces limitations in such harsh marine conditions. To address these challenges, this paper proposes a Multiscale Acoustic Graph Network (MAGNet), a novel framework combining AE sensing with graph neural networks (GNNs) for high-precision leakage localisation. MAGNet comprises three synergistic modules: an Anti-Noise Encoder (ANE) for robust multiscale feature extraction, an Attentive Graph Layer (AGL) for dynamic inter-signal dependency learning, and a Spectral Graph Learner (SGL) for capturing long-range signal attenuation patterns. Additionally, a dedicated dataset of internal valve leakage signals collected from offshore platform pipelines is constructed to support comprehensive evaluation. Experimental results show that MAGNet achieves localisation accuracies between 0.942 and 0.976 under 2–5 MPa pressure conditions, and maintains 85.2% to 94.8% accuracy across 0–20 dB signal-to-noise ratios. Compared with existing methods, MAGNet exhibits superior accuracy, robustness, and generalisation performance on both in-situ and benchmark datasets. These findings highlight the potential of MAGNet as a practical and reliable solution for offshore SHM.

The Nam Rong - Doi Moi oilfield is located between Block 09 - 3 and Block 09 - 1 in the Cuu Long Basin, on Vietnam's continental shelf. It is operated by the Vietnam - Russia Joint Venture (Vietsovpetro) and has been performing efficiently. Production from the Nam Rong - Doi Moi oilfield is transported via subsea pipelines RC - DM → RC - 4, RC - 4 → RC - 5, and RC - 5 → RP - 1 to the floating production storage and offloading vessel FSO - 6. Crude oil exploited from offshore oilfields in Vietnam generally has a high paraffin (wax) content. Transporting this type of oil through subsea pipelines often leads to undesirable issues that can affect the pipeline’s transport capacity. Specifically, the crude oil from the Nam Rong - Doi Moi field is known to contain high wax content, which poses several risks due to the formation of paraffin deposits inside the pipeline. To ensure safe transportation, various techniques have been employed, including pipe insulation, mechanical removal, and the use of wax inhibitors. Among these, wax inhibitors have recently emerged as highly effective solutions for mitigating gelation and improving crude oil flow. This work introduces a solution using wax inhibitors to reduce the pour point of crude oil transported from the Nam Rong - Doi Moi oilfield. In addition, simulations of the probability of paraffin deposition in the pipeline were conducted using the OLGA software. The results indicate a high likelihood of wax formation and recommend the use of the TPD - 1210 depressant as an effective treatment. This research makes a significant contribution to the transportation of high - paraffin - content crude oil through subsea pipelines and is applicable to other oilfields in Vietnam with similar conditions.

Welding is essential for the manufacture of complex products due to its efficiency and characteristics. However, thermal input in the welded joints in joints can cause embrittlement due microstructural defects with low fracture toughness, especially in heat-affected areas in heat-affected zones. Recent studies have sought to understand the behavior of cracks produced in dissimilar steel joints, widely used in the oil and gas industry, but there are still gaps in knowledge regarding the effects of heterogeneity on these joints. This study analyzed welded joints of dissimilar steels, ASTM A182 and ASTM A36, with INCONEL 625 amalgamation. Different welding conditions were analyzed from the perspective of fracture mechanics in offshore pipeline flanges. Based on a previous experimental study, numerical simulations using the finite element method were performed to confirm some of these experimental results, which showed that plastic flow is directed toward the material with the lowest yield stress.

The submarine topography in the canyon area of the Qiongdongnan Basin is complex, with severe risks of shallow gas hazards threatening marine engineering safety. To accurately characterize seabed morphology and assess shallow gas risks, this study employed multi-source data fusion technology, integrating 3D seismic data, shipborne multibeam bathymetry data, and high-precision AUV topographic data from key areas to construct a refined seabed terrain inversion model. For the first time, the spatial distribution characteristics of complex geomorphological features such as scarps, mounds, fissures, faults, and mass transport deposits (MTDs) were systematically delineated. Based on attribute analysis of 3D seismic data and geostatistical methods, the enrichment intensity of shallow gas was quantified, its distribution patterns were systematically identified, and risk level evaluations were conducted. The results indicate: (1) multi-source data fusion significantly improved the resolution and accuracy of terrain inversion, revealing intricate geomorphological details in deep-water regions; and (2) seismic attribute analysis effectively delineated shallow gas enrichment zones, clarifying their spatial distribution patterns and risk levels. This study provides critical technical support for deep-water drilling platform site selection, submarine pipeline route optimization, and engineering geohazard prevention, offering significant practical implications for ensuring the safety of deep-water energy development in the South China Sea.

Deep learning-based intelligent path planning system design for subsea pipeline laying

Novel approach of collapse pressure prediction for subsea pipelines with realistic corrosion defects

The understanding and prediction of slurry flow behavior in pipeline systems is critical for successful operation and management of various industrial applications due to the inherent complexity of such solid–liquid mixtures. Thus, fundamental experimental investigations and an easy-to-use, efficient computational fluid dynamics model meeting engineering requirements are essential. This study first preliminarily explores the physical process of slurry flow, composed of uniformly graded glass beads at different concentrations, via laboratory tests. These tests use electrical impedance tomography (EIT) and sampling probe techniques to measure particle volume fraction distribution in a 100 mm diameter pipe. A key contribution is a novel, efficient two-fluid model requiring calibration of one parameter, turbulent Schmidt number σ: it offers fast predictions and reasonable estimates of concentration profiles and hydraulic gradients in horizontal pipes post-calibration, assuming full particle suspension (in situ concentration ideally <30% v/v) and neglecting particle–particle interactions. The study finds σ primarily depends on pipe diameter and establishes potential correlations between σ, pipe diameter, and Reynolds number, simplifying calibration across flow conditions and geometries. While not a fundamental theoretical advance, the proposed model enhances predictive capability by minimizing empirical uncertainty and enabling reliable extrapolation beyond validation cases. Moreover, experimental results confirm that EIT provides a qualitative yet robust visualization of particle distribution, demonstrating its promise when integrated with advanced reconstruction algorithms. These findings provide both theoretical insight and practical tools for slurry transport analysis, with particular value in engineering contexts, such as dredging, mining, and offshore pipeline operations.

Probabilistic model for state assessment of offshore pipeline with anti-corrosion coating based on Wiener process and Bayesian network

A novel method for underwater pipeline crack detection via active electric field and frequency inflection point analysis

This study constructed an integrated underwater pipeline monitoring system, which combines pipeline posture sensing modules and pipeline leakage detection modules. The proposed system can achieve the real-time monitoring of pipeline posture and the comprehensive assessment of pipeline damage. By deploying pipeline posture sensing and leakage detection modules in array configurations along an underwater pipeline, information related to pipeline posture and flow variations is continuously collected. An array of inertial sensor nodes that form the pipeline posture sensing system is used for real-time pipeline posture monitoring. The system measures underwater motion signals and obtains bending and buckling postures using posture algorithms. Pipeline leakage is evaluated using flow and water temperature data from Hall sensors deployed at each node, assessing pipeline health while estimating the location and area of pipeline damage based on the flow values along the nodes. The human–machine interface designed in this study for underwater pipelines supports automated monitoring and alert functions, so as to provide early warnings for pipeline postures and the analysis of damage locations before water supply abnormalities occur in the pipelines. Underwater experiments validated that this system can precisely capture real-time postures and damage locations of pipelines using sensing modules. By taking flow changes at these locations into consideration, the damage area with an error margin was estimated. In the experiments, the damage areas were 8.04 cm2 to 25.96 cm2, the estimated results were close to the actual area trends (R2 = 0.9425), and the area error was within 5.16 cm2 (with an error percentage ranging from −20% to 26%). The findings of this study contribute to the management efficiency of underwater pipelines, enabling more timely maintenance while effectively reducing the risk of water supply interruption due to pipeline damage.

ABSTRACTLeak detection (LD) in gas pipelines (GPs) is critical for ensuring operational safety and environmental protection. This study presents a novel digital/visual twin for detecting single‐ and multiple leaks in GPs under both single‐ and multiphase flow conditions. The framework of the digital twin leverages experimental data from a multiphase flow‐testing loop and synthetic data generated using OLGA software to validate and optimize machine learning (ML) models for leak detection and localization. Several ML models, including random forest (RF), support vector machine (SVM), k‐nearest neighbors (k‐NNs), decision tree regression (DTR), and eXtreme gradient boosting (XGBoost), were tested individually for their ability to classify leak conditions and localize leaks. Initial results showed moderate performance for individual models, with accuracies ranging from 42% to 57%. However, a significant improvement was observed through the use of advanced techniques such as stacking models, feature engineering, and data averaging. The final stacking regressor model, which combined the strengths of RF, k‐NN, and SVM, outperformed the individual models, achieving R2 values exceeding 0.96 with an accuracy of 90% in complex multiple leak scenarios. The digital twin system integrates this ML framework with real‐time data visualization, allowing operators to visualize offshore pipeline conditions, detect leaks, and localize leak positions using a virtual twin representation of the physical pipeline. The virtual twin provides an interactive, high‐fidelity interface that enables users to monitor and analyze leak events as they occur, enhancing situational awareness and decision‐making capabilities. The combination of advanced ML techniques and digital twin technology provides a robust and accurate solution for real‐time LD in offshore pipelines. It significantly improves detection performance in multiphase flow conditions. This innovative approach sets a new benchmark for offshore pipeline monitoring systems, offering superior LD capabilities under a range of operational conditions. The system is readily adaptable for integration with SCADA platforms and pipeline monitoring infrastructures, supporting deployment in offshore oil and gas operations, industrial gas distribution networks, and critical energy corridors where early LD is essential.