aKey Laboratory of Ocean Energy Utilization and Energy Conservation of the Ministry of Education, Dalian University of Technology, Dalian 116024, China
bNingbo Institute of Dalian University of Technology, Ningbo 315016, China
cCNOOC Research Institute Ltd., Beijing 100028, China
dState Key Laboratory of Natural Gas Hydrates, Beijing 100028, China
With the development of offshore oil and gas resources, hydrates pose a significant challenge to flow assurance. Hydrates can form, accumulate, and settle in pipelines, causing blockages, reducing transport capacity, and leading to significant economic losses and fatalities. As oil and gas exploration moves deeper into the ocean, the issue of hydrate blockages has become more severe. It is essential to take adequate measures promptly to mitigate the hazards of hydrate blockages after they form. However, a prerequisite for effective mitigation is accurately detecting the location and amount of hydrate formation. This article summarizes the temperature–pressure, acoustic, electrical, instrumental–response, and flow characteristics of hydrate formation and blocking under various conditions. It also analyzes the principles, limitations, and applicability of various blockage detection methods, including acoustic, transient, and fiber-optic-based methods. Finally, it lists the results of field experiments and commercially used products. Given their advantages of accuracy and a wide detection range, acoustic pulse reflectometry and transient-based methods are considered effective for detecting hydrate blockages in future underwater pipelines. Using strict backpressure warnings combined with accurate detection via acoustic pulse reflectometry or transient-based methods, efficient and timely diagnosis of hydrate blockages can be achieved. The use of a hydrate model combined with fiber optics could prove to be an effective method for detecting blockages in newly laid pipelines in the future.
Natural gas hydrates (NGHs) are solid substances that typically form in low-temperature and high-pressure environments. They have been studied widely for their practical applications in seawater desalination, CO2 capture and storage, gas separation, energy storage, gas storage, and transportation. However, hydrates can hinder normal operations in oil and gas pipelines. With the gradual development of oil and gas exploration in deep-water areas, the depth and length of oil and gas gathering systems have increased, posing additional challenges [1], [2]. As the transportation distance increases, the temperatures of the oil and gas in the pipeline decrease, making them prone to hydrate formation [3], [4]. Once hydrates form in a pipeline, they increase flow resistance and reduce transport capacity, complicating the operational efficiency of the pipelines [5], [6], [7]. Deep-water oil and gas pipelines—particularly those associated with large-scale engineering projects in harsh environments—are exceedingly difficult to maintain and overhaul. This complexity makes the timely and effective detection of pipeline abnormalities a significant challenge. If hydrate blockages are not detected and removed promptly, they expand and eventually block the pipeline completely, resulting in enormous economic losses [8], [9], [10]. Compared to wax, asphalt, and scale problems, hydrate blockages are more severe and are one of the major challenges in flow assurance, attracting widespread attention [4], [11], [12].
Inhibitors are commonly used to mitigate hydrate formation during industrial production [13], [14], [15]. However, thermodynamic inhibitors have issues such as high injection volume, salt precipitation, and high viscosity, which hamper the flow [16], [17]. Anti-agglomerants also have problems, such as high cost, failure under high water content, and toxicity [18], [19]. Currently, kinetic inhibitors are primarily effective only for hydrates formed from single gases and tend to fail under conditions of high supercooling [20], [21]. Another approach is to remove water from the oil and gas in advance to prevent hydrate formation. However, if the dehydration equipment malfunctions or unexpected weather conditions occur, the equipment may stop working and fail to achieve the desired results [22], [23]. Therefore, hydrate blockage risks cannot be eliminated in engineering practice, and hydrates always form in pipelines, particularly during shutdown periods [24]. Once hydrates accumulate and form blockages in the pipeline, the pressure inside the pipeline increases significantly, which can result in shutdowns and substantial economic losses [25], [26]. Improper handling can lead to accidents and even casualties [27], [28], [29], [30], [31].
As pipeline blockages are difficult to avoid, they must be removed promptly to minimize losses [32]. Before doing so, accurate blockage information must be obtained to design targeted blockage-removal plans [24], [29]. Knowing the blockage locations allows precise measures to be taken and enables early resolution of blockages, thereby reducing economic losses. Timely determination of the number of blockages is also crucial for taking the correct measures to address hydrate blockages. Usually, in the early stages of blockage removal, depressurization is used to promote hydrate decomposition. However, when multiple blockages exist, even if depressurization is conducted simultaneously from both sides, the high pressure between the blockages can cause them to move at high speed, possibly leading to pipeline ruptures and accidents [29], [33]. Therefore, accurately determining the number of blockages is critical for developing targeted solutions, improving blockage removal efficiency, and preventing accidents and casualties. In conclusion, for safe and efficient blockage removal, obtaining specific pipeline blockage information is crucial, making research on blockage detection imperative.
At present, pipeline fault detection focuses mainly on leakage and corrosion, with less attention on blockage detection. The main studies on pipeline blockage detection are as follows. Yu et al. [34] analyzed the application of acoustic methods for detecting underground pipeline leakages, blockages, and other faults. Che et al. [35] and Brunone et al. [36] investigated the application of transient methods to detect blockages, particularly partial blockages. However, their research focused primarily on specific detection methods. Datta and Sarkar [37], and Wong and McCann [38] provided comprehensive analyses of various pipeline fault-detection methods, pointing out the development direction of pipeline detection. However, all these works lacked targeted research on hydrate blockage detection.
This paper summarizes the characteristics of hydrate formation and blockage, considering various detection methods—including acoustic, transient, and fiber-optic-based methods—and analyzing their principles and application scope. We also summarize some field detection experimental results and existing commercial products. Section 2 introduces the characteristics of hydrate formation and blockage. Section 3 describes different hydrate blockage detection methods, Section 4 presents the field applications of these methods, and Section 5 provides suggestions for the future development of hydrate blockage detection methods. Fig. 1 illustrates the structure of this paper, highlighting the correspondence between 2 Hydrates’ physicochemical characterizations, 3 Detection methods of hydrate blockage.
2. Hydrates’ physicochemical characterizations
As shown in Fig. 2, hydrates are easily generated at low temperatures and high pressures in the presence of water—usually at pipeline valves, inclines, and low points—and can travel along the pipeline [39], [40]. Hydrate particles will build up in the pipe continuously, similar to wax/asphalt deposits, eventually causing blockages in the pipe [24], [41]. The pipeline environment becomes particularly conducive to hydrate formation during shutdowns or restarts, when hydrates can form rapidly, potentially causing blockages within just a few hours to tens of hours [29], [42].
Oil and gas pipelines are generally classified into oil-, gas-, and water-dominated systems, based on the transport medium’s liquid-holding rate, void ratio, and water content [43], [44]. As shown in Fig. 3[45], the hydrate-blocking mechanism and characteristics of the blockages differ in different systems.
In oil-based systems, most water exists as emulsified droplets, and hydrates grow at the interface between the droplets and the oil phase, continuously growing inside the droplets [46]. The hydrate particles gather under the action of the liquid bridge, the flow resistance increases, and the pressure drop of the pipe increases as the hydrate particles continue to gather, eventually forming blockages [47]. In the early blockage stage, the blockage contains free water and is loose; subsequently, annealing occurs, and the hydrate blockage becomes dense [29], [48].
In water-dominated systems, most oil exists as emulsified oil droplets [49]. After hydrate generation, the flow rate decreases, leading to stratification of the oil–water mixture, and the hydrates become concentrated in the lower fluid layer, sharply reducing the fluid fluidity and causing blockages [46]. In the early stages of hydrate growth, oil droplets may be encapsulated by hydrates; subsequently, the droplets are extruded as the hydrates grow [50], [51].
Hydrates in gas-dominated systems typically grow on pipe walls; thus, the pipe’s cross-sectional area is gradually reduced by the hydrate film, which further promotes downstream hydrate growth owing to the Joule–Thomson effect [52]. The hydrates in the pipeline flake off from the pipe wall because of the progressively increasing flow shear and subsequently accumulate, blocking the pipeline [52]. Hydrates also form in the low-lying water pools of a pipeline and are transported with the pipeline, eventually blocking it [29].
In the early stages of a blockage, the inside of the blockage may not be entirely converted to hydrate because of the adhesion of the hydrate blocks to each other; thus, a free water or oil phase exists, and the porosity and permeability of the hydrate blockage is high [53]. If the location of the blockage is accurately obtained at this point and effective treatment measures are taken quickly, the hydrate blockage may be speedily removed. As the blocking time increases, the hydrate blockage may anneal; the porosity and permeability of the blockage then decrease and the blockage becomes more solid, making the subsequent removal process more complicated [29], [46]. Many researchers have explored the laws and characterization of hydrate formation and blockage processes with the aim of being able to rapidly identify hydrate blockages. These characterizations include temperature, pressure, acoustic signals, electrical signals, and specific instrument response signals during hydrate formation and their impact on the pipeline flow.
2.1. Thermodynamic characterization
During hydrate formation, gas is consumed and heat is released, leading to a decrease in pressure and an increase in temperature in the system. Extensive research has been conducted on CH4 hydrates [11]; however, in oil and gas pipelines, hydrocarbons (e.g., C2H6 and C3H8) are usually present in addition to CH4. Pure CH4 or pure C2H6 gas generates structure I (sI) hydrates. However, the cage shape of the hydrates generated from a mixture of hydrocarbons may change, resulting in more relaxed phase equilibrium conditions and easier hydrate generation in the pipeline, as shown in Fig. 4.
The generation of CH4 hydrates is an exothermic process that can lead to changes in the local temperature. Therefore, accurately calculating heat changes correctly during hydrate generation and decomposition is essential for calculating the system temperature. The values of the enthalpy change of decomposition and the generation of hydrates are generally considered to be opposite [54], [55]. The enthalpy change of hydrate decomposition can be measured using direct or indirect methods: Direct methods include calorimetry, pressure drop, and differential scanning calorimetry (DSC), while indirect methods mainly utilize the Clapeyron equation or the Clausius–Clapeyron equation. Gupta et al. [56] used DSC to measure the heat of hydrate decomposition directly and noted that sI CH4 hydrates were insensitive to temperature changes under typical temperature and pressure conditions. Many researchers have measured the enthalpy of decomposition of sI CH4 hydrate into water and CH4 gas to be in the range of about 51.6–57.7 kJ·mol−1[57], [58], [59], [60]. However, when only 1% C2H6 and 99% CH4 were used to generate structure II (sII) hydrates, the enthalpy of decomposition increased by approximately 30% [60]. Different hydrate cage types lead to great differences in hydrate decomposition enthalpies, producing large variations in temperature during hydrate generation and decomposition. In addition, the friction of the hydrate slurry against the pipe wall in a flow system may cause an increase in temperature.
Thermal conductivity is a key parameter in the study of heat transfer characteristics, and changes in the thermal conductivity of a system after hydrate generation may prevent heat transfer. Lingelem et al. [61] were the first to suggest that hydrates deposited in pipes would reduce the heat flux at the wall. In subsequent studies, Singh et al. [62], Na and Webb [63], and Nicholas et al. [64] also proposed that hydrate deposits exhibit a thermally insulating effect similar to that of wax deposits. Chong et al. [65] reported the thermal conductivities of the sI and sII hydrates. The thermal conductivity of hydrate deposits in oil and gas transportation pipelines is much lower than that of the steel pipelines, so such deposits have an insulating effect [66].
Accurate measurements of hydrate thermal conductivity are required to study the insulating effect of hydrates. Transient hot-wire and transient plane source techniques are commonly used to measure the thermal conductivity of hydrates. However, the porous nature of hydrate samples and their contact thermal resistance may affect the results [67]. Zhang et al. [68], [69] investigated the effect of hydrate deposits in dead legs (areas of a pipeline with little or no flow) and found that the hydrate thermal insulation effect influenced the temperature distribution and increased the temperature inside the pipes. Song et al. [70] analyzed the thermal properties of hydrates in dead legs, calculated the effective thermal conductivity of hydrate deposits, and determined the thermal conductivity of pure hydrates. Kumar et al. [71] concluded that the thermal diffusivity of a porous media system increased and then decreased with CH4 hydrate saturation. Li and Liang [72] considered that the effective thermal conductivity in a porous media system is primarily dependent on the morphology of the sediments. They found that hydrates bind sediment particles in environments with low water saturation, causing the thermal conductivity to increase with hydrate saturation, whereas the thermal conductivity changes little with hydrate saturation in an environment with high water saturation.
2.2. Acoustical characterization
Gas hydrates are solid substances with acoustic properties similar to those of ice. When water and gas form hydrates, the acoustic velocity in the system typically increases, as indicated by the two red curves in Fig. 5. Extensive research has been conducted on the velocity of gas hydrates in sedimentary materials. Many researchers believe that NGHs in sediments can cement sediment particles. As the hydrate saturation increases, the medium’s compressibility decreases, increasing the velocity and amplitude of acoustic waves in the system [73].
Hu et al. [74] measured the acoustic velocities of NGHs during their formation and decomposition in porous media and found that the p- and s-wave velocities of the decomposition process were higher than those of the formation process at the same saturation level. Furthermore, when the saturation was lower than 10%, the p- and s-wave velocities were insensitive to saturation changes. However, both velocities increased rapidly when the saturation exceeded 10%, particularly in the range below 30%. Bu et al. [75] also found that the wave velocity in sediments increased with hydrate saturation, noting that the p-wave velocity increased more slowly than the s-wave velocity. Moreover, Bu et al. [76] investigated the acoustic responses during hydrate decomposition in sediments and found that both the p- and s-wave velocities decreased during the decomposition process as the hydrate saturation decreased.
Prasad et al. [77] reported that the acoustic wave velocity increased with increasing tetrahydrofuran (THF) hydrate saturation in sandy porous media. Specifically, when the hydrate saturation increased 15%–40%, the velocity increased by approximately 80%. Duchkov et al. [78] measured p- and s-wave propagation in sandy porous media and concluded that the velocity was linearly correlated with hydrate saturation. In contrast, Xing et al. [79] argued that the velocity in porous media did not increase linearly with gas hydrate saturation. During the initial stages of hydrate formation, the bonding between the hydrates and sand grains increases the contact area between the particles, enhancing the elastic modulus and leading to a rapid increase in velocity. However, hydrates begin to form in the pore fluid as the hydrate saturation increases; they are suspended in the pore space and are not directly in contact with the sand grains as the increase in acoustic velocity slows down. Bu et al. [76] concluded that the velocity of an acoustic wave with saturation varied unevenly during decomposition. In the early stage of decomposition, the hydrate in direct contact with the sediment particles decomposed first, resulting in a rapid decrease in velocity. In the late stage of decomposition, the hydrate suspended in the pore space decomposed, and the decrease in velocity was smaller than that in the early stage of decomposition.
Gimaltdinov et al. [80] studied the acoustic response during the formation of CH4 hydrates from gas bubbles in liquids. They found that the formation of hydrates strongly affected wave propagation at frequencies below 1 kHz, leading to a significant increase in wave attenuation by two orders of magnitude. They also observed that an increase in the initial gas content resulted in a decrease in the velocity and an increase in the reflection coefficient. Hu et al. [81] concluded that hydrates in the sediments of the South China Sea were initially suspended in the pore space during the initial stage of hydrate generation and then began to come into contact with the porous media framework as the saturation increased. Therefore, acoustic attenuation increased with saturation in the early stage of hydrate formation and decreased with saturation when the saturation was higher than 14%. Gubaidullin et al. [82] investigated wave propagation in a porous medium containing hydrate and found that increased hydrate saturation led to higher acoustic impedance in the medium. As a result, the wave propagated without distortion, and the attenuation was reduced. In porous media, an increase in the hydrate saturation usually increases the acoustic impedance of the medium, resulting in a more pronounced difference between the acoustic impedance of the medium and that of the gas phase, which is often used to detect the distribution of hydrates [83], [84].
In various research systems, the formation of hydrates can affect the propagation characteristics of acoustic waves. Specifically, in pipeline transportation systems, the accumulation of hydrates significantly impacts acoustic wave propagation. Due to the difference in impedance between hydrate blockages and the surrounding oil and gas media, distinct reflection and refraction waves are generated at this interface. In addition, hydrate blockages deposited in the pipeline act as damping agents, weakening the energy of the acoustic waves and altering their propagation path, thereby affecting the acoustic characteristics of the system and leading to changes in the frequency response. The acoustic impedance mismatch and frequency response changes caused by hydrates are often utilized to detect hydrate blockages in pipelines.
2.3. Electrical characterization
Du Frane et al. [85] conducted the first electrical conductivity measurements of pure CH4 hydrates. The electrical conductivity of NGHs is typically approximately five orders of magnitude lower than that of seawater [86]; thus, NGHs are commonly regarded as electrical insulators. Sediments containing NGHs exhibit high resistivity, making them suitable for the geological exploration of hydrates [87], [88]. Pearson et al. [89] investigated the impact of THF hydrates in sediments on the resistivity and found that the resistivity increased with increasing hydrate saturation. During hydrate formation, the resistivity increases by nearly two orders of magnitude. Lee et al. [90] suggested that the formation of THF hydrates affects the volume fraction of free water in sediments, thereby influencing the resistivity of the system. Buffett and Zatsepina [91], [92] studied the electrical resistivity of sediments containing CO2 hydrates and found that the resistivity increased with saturation. Li et al. [93] conducted in situ measurements of resistivity variations in sedimentary systems, forming CH4 hydrates with free gas and varying saline water concentrations. They found that the resistivity consistently increased with hydrate saturation. Spangenberg and Kulenkampff [94] measured changes in the resistivity of fully water-saturated glass bead samples as the saturation of CH4 hydrates increased. They found that, as the hydrate saturation increased, the resistivity increased significantly. When the saturation reached 95%, the resistivity was approximately 52 times higher than the initial value. Ren et al. [73] suggested that an increase in hydrate saturation depletes free water, leading to higher solution concentration and hence lower resistivity. However, hydrates acting as insulators may block the pore spaces of porous media, isolating the conductive solution and resulting in increased resistivity.
The exothermic heat released during hydrate formation causes temperature fluctuations, affecting the migration of ions in the solution and consequently affecting resistivity. As the temperature increases, the migration rate of the ions also increases, resulting in a decrease in the resistivity [89], [91], [92]. As shown by the yellow curve in Fig. 5, the resistivity decreases as the temperature increases at initial hydrate generation; it then continues to increase with subsequent hydrate generation. Zhou et al. [95] measured temperature changes during CH4 hydrate formation and decomposition processes. They found that a higher hydrate saturation corresponded to higher resistivity. Moreover, they emphasized that the impact of hydrate saturation variation on resistivity is greater than that of temperature and pressure changes. Li et al. [96] measured changes in electrical resistivity during hydrates’ nucleation, growth, and decomposition processes in porous media. During the nucleation of the NGHs, water was consumed, resulting in an increase in the salt concentration in the solution and a slight decrease in resistivity. As the hydrates grew, they isolated the salt solution in the system, leading to an increase in the resistivity with increasing saturation. During hydrate decomposition, the resistivity decreased and was slightly higher than the preformation value. These findings confirmed the potential use of resistivity measurements to detect the nucleation and growth of NGHs.
As shown in Fig. 6[97], the dielectric constant of NGH differs significantly from those of water, natural gas, and crude oil. As a result, there is a significant change in the dielectric constant during NGH formation. Jakobsen et al. [98] and Jakobsen and Folgero [99] studied gas hydrate formation in oil–water emulsions using dielectric constants. They found that the static dielectric constant and dielectric increment revealed information about the geometric structure and evolution of the hydrates, while the relaxation time provided insights into the kinetics. Jakobsen et al. [98] and Jakobsen and Folgero [99] used dielectric spectroscopy to investigate the formation kinetics of gas hydrates in water in oil emulsions. They also measured the formation of NGHs in the emulsion and observed a decrease in the relaxation time, static dielectric constant, and high-frequency dielectric constant. Haukalid [100] suggested reducing the measurement frequency to achieve better results because the dielectric constant of hydrates exhibits significant changes within the kilohertz–megahertz range.
2.4. Characterization of the instrument’s response
X-ray computed tomography (CT), powder X-ray diffraction (PXRD), Raman spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy are widely used tools in microscopic research and are employed in the field of NGHs. X-ray CT generates a series of grayscale images, where the grayscale values reflect the absorption of X-rays by the material and are typically closely related to the density. Hydrates usually have lower densities than water; therefore, the grayscale values of the CT images in hydrate studies change with hydrate formation. Segmentation techniques can then be used to obtain a 3D distribution of materials such as water, gas, oil, hydrate, and sand. Distinguishing gas hydrates from water in CT images is challenging because of their similar densities. Increasing the density difference between the hydrates and solution can achieve a higher image resolution. Therefore, substances such as potassium iodide are often used to increase the density of the solution, and guest molecules such as argon or krypton are used to generate denser hydrates [101], [102], [103]. Recent advancements include the application of algorithms such as random forest and convolutional neural networks for CT images [104], [105]. In addition, synchrotron X-ray CT can provide more accurate images [106], [107], and such internal structure observations can not be obtained by two dimensional images [108].
PXRD is a widely used technique in microscopic research. When X-rays are directed into a crystal in a specific direction, the crystal acts as a diffraction grating. By analyzing the direction and intensity of the diffracted X-rays, structural information can be obtained regarding the interior of the crystal. Raman spectroscopy has been used extensively to study the chemical bonding of guest and water molecules. It provides information on hydrate structure, composition, phase transitions, hydrate number, and occupancy rate [109], [110], [111], [112]. NMR spectroscopy detects the absorption of electromagnetic waves by atomic nuclei in a magnetic field and offers insights into hydrate structure, composition, phase transitions, hydrate number, and occupancy rate in multicomponent NGHs [113], [114], [115].
Lu et al. [116] used 13C NMR to characterize NGH samples recovered from the periphery of Cascadia and identified the presence of structure H (sH) hydrates, thus confirming their existence in nature. Zhang et al. [117] conducted an in situ investigation of NGHs in the South China Sea using Raman spectroscopy and found that the hydrate sediment exhibited more than a single structure. Meng et al. [118] studied the influence of different guest molecules on the microstructure of hydrates using PXRD. They found that the lattice constants of hydrates formed with single-component light alkane guest molecules correlated positively with the diameter of the guest molecules. In contrast, although hydrates formed with guest molecules containing oxygen atoms (e.g., CO2 and THF) also showed a correlation with molecular diameter, the relationship with lattice parameters was less pronounced. Due to the different environments provided by hydrate cages, the Raman and NMR signals shift when guest molecules enter the cages [119]. Generally, compared with the gas phase, CH4 molecules in hydrates exhibit shifts toward lower frequencies in Raman spectra [120] and toward lower fields in 13C NMR spectra [121], [122]. Although Raman spectroscopy is primarily utilized for the qualitative study of hydrates, it can also provide quantitative measurements of hydrate composition if the intensity is calibrated and all peaks are accurately assigned [123]. The CH4 chemical shift in 13C NMR spectra has structural specificity, and its intensity can be quantitatively measured, making it an effective tool for determining the occupancy rate of cages during the formation of mixed gas hydrates [119].
Truong-Lam et al. [124] observed the formation and decomposition processes of CH4 hydrates using Raman spectroscopy and found that the formation rate of small cages was faster than that of large cages in the initial stage of hydrate formation. In contrast, the collapse rate of large cages was higher than that of small cages in the late stage of hydrate decomposition. They also observed significant changes in the Raman intensity of the OH-stretching band during hydrate formation and decomposition. Using Raman and 13C NMR spectroscopy, numerous studies have verified that CH4 and C2H6 gas mixtures produce sII hydrates. The specific structure of the generated hydrates depends on the proportion of the mixed gases [121], [125]. Uchida et al. [123] studied the crystal structures of hydrates formed from CH4 and C2H6 gas mixtures using Raman spectroscopy. They calculated the volume ratio of sI and sII hydrates based on the intensity ratio of the C–C stretching peaks, determining the occupancy rate of guest molecules in the hydrate cages using relative intensity ratios. In the structures of the sI and sII hydrates, C2H6 molecules solely occupied the large cages, while CH4 molecules occupied the remaining hydrate cages. The researchers also analyzed the structures of the hydrates formed from CH4, C2H6, C3H8, and i-C4H10 mixed gases using Raman spectroscopy and PXRD, and the occupancy rates of different guest molecules in the cages. Dec [126] studied the formation process of hydrates from CH4 and C2H6 gas mixtures using 13C NMR spectroscopy and identified four distinct stages: simultaneous formation of sI and sII hydrates; cessation of sI formation, accelerated generation of sII hydrates; commencement of sI decomposition, further acceleration of sII formation; and ultimate steady state. Truong-Lam et al. [127] characterized the hydrates formed from CH4 and C3H8 gas mixtures using Raman spectroscopy. Their results showed that small cages of sII hydrates formed more quickly than lager cages and that CH4 could occupy both large and small cages, whereas C3H8 could only occupy large cages. During hydrate decomposition, the CH4 molecules in the small cages decomposed faster than those in the large cages, whereas the C3H8 molecules in the large cages decomposed more slowly than the CH4 in large cages. Kumar et al. [119] characterized the hydrates produced by CH4, C2H6, and C3H8 gas mixtures using PXRD, NMR, and Raman spectroscopy; the images are shown in Fig. 7. The researchers determined the composition of the hydrates and the occupancy rate of the cages using PXRD and Raman spectroscopy, and validated the results by comparing them with NMR data.
2.5. Characterization of flow
In flow systems, the generation and aggregation of hydrate particles can affect the flow. In particular, slurries with high hydrate volume fractions may exhibit complex rheological properties [128]. Understanding the flow characteristics of hydrate slurries is crucial for assuring flow in oil and gas pipelines.
Shen et al. [66] reported that, in high water-cut systems, slurries with small hydrate volume fractions exhibit properties similar to water and can be considered Newtonian fluids. However, numerous studies have shown that, in oil-dominated systems, slurry viscosity decreases with increasing shear rate, indicating that the slurry exhibits shear-thinning behavior [128], [129], [130], [131], [132]. This implies that restarting operations in pipelines containing hydrates can be challenging once the production is halted [129]. Furthermore, this behavior intensifies with an increase in the volume fraction of the hydrates [128], [133]. It is generally believed that, in the early stages of hydrate formation, the slurry viscosity increases as the hydrate particles form and aggregate. However, as larger hydrate masses decompose or rearrange, the slurry viscosity decreases [130]. Hydrates have lower densities than water at the same temperature. Therefore, it is generally believed that the slurry density decreases as hydrates form. However, Shen et al. [66] reported that many bubbles were generated during the initial formation of hydrates. The hydrates formed from these bubbles either broke or continued to grow, resulting in considerable fluctuations in the slurry density. Furthermore, the researchers observed that the higher the flow rate, the higher the density of the hydrate slurry.
Wang et al. [134] studied the flow behavior of CH4 hydrate slurries and observed a significant change in the flow pressure drop when the hydrate volume fraction reached 30%–40%. Joshi et al. [135] conducted a study on the formation of hydrates in high-water-content systems and similarly found that the pressure drop increased rapidly only when the hydrate reached a certain concentration, as indicated by the yellow curve in Fig. 8. They divided the hydrate-formation process into three stages. In the first stage, the hydrates were evenly dispersed primarily in the water, and there was no significant change in the pressure drop; this corresponds to regions I and II in the figure. In the second stage, when the hydrate concentration reached a certain value, the system transitioned from a homogeneous suspension flow to a heterogeneous suspension flow, sharply increasing the pressure drop; this corresponds to region III in the figure. In the third stage, gas primarily dominated the flow with low water content, resulting in a significant pressure drop; this corresponds to region IV in the figure. Shen et al. [66] suggested that the flow was turbulent during the early stage of hydrate formation and the pressure drop remained relatively constant. When the volume fraction reached 15%, the flow transitioned from turbulent to laminar, decreasing the pressure drop. Subsequently, the flow remained laminar, and the pressure drop rapidly increased with increasing volume fraction. In addition, the researchers observed that the pressure drop was greater at higher flow velocities. However, Yan et al. [131] and Tang et al. [136] argued that the pressure drop increased with the formation of hydrates during the early stage, as indicated by the green curve in Fig. 8. In addition, Yan et al. [131] observed a decrease in the flow velocity, followed by fluctuation and eventual stabilization of the pressure drop.
Hydrate particles greatly affect the slurry friction coefficient, Sinquin et al. [137] analyzed the effect of hydrate particle size on the slurry friction coefficient. For oil–water emulsion systems, the slurry’s friction coefficient increased with water content [138]. Shi et al. [139] investigated the generation of hydrates from oil–water emulsions and found that the formation of hydrates led to a sudden increase in pressure drop and that the coefficient of friction was proportional to the volume fraction of the hydrates. Rao et al. [41] noted that it is necessary to study the aggregation and distribution of hydrates to determine the pressure drop. Shi et al. [139] pointed out that high flow rates increase the particle suspension and reduce the collision of particles with the pipe wall, reducing the friction coefficient and pressure drop. Ding et al. [140] suggested that the agglomeration of hydrates leads to a dramatic increase in the pressure drop after a period of steady hydrate growth; when large hydrates are sheared into smaller pieces, the pressure drop decreases slightly, and the subsequent continuous deposition of hydrates leads to a smaller circulation area and an increase in the pressure drop. Aman et al. [141] stated that, in gas-dominant systems at low velocities, hydrates tend to be distributed unevenly in the pipeline, leading to localized accumulation and potentially causing a significant friction pressure drop. Fu et al. [142] pointed out that, under turbulent flow conditions, the collision between the hydrate particles in the slurry and the pipe wall is one of the main causes of the pressure drop. Moreover, the flow rate changes caused by the presence of hydrates—as well as their collisions and friction with the pipe walls—may lead to the generation of flow noise, which could potentially be used to detect hydrate blockages.
The formation of hydrate particles in a pipeline can lead to changes in the flow patterns. Lv et al. [138] discovered that the Mandhane flow pattern map was ineffective in predicting the flow patterns of hydrate slurries, which confirmed that the formation of hydrates does affect the flow patterns. Joshi [143] was the first to observe that even small amounts of hydrates could induce flow regime transitions, promoting slug flow formation. Zerpa et al. [144] developed a flow model for hydrate-containing systems in pure water and found that hydrates could induce a transition from stratified to slug flow, agreeing with the experimental result of Joshi [143]. Ding et al. [145] highlighted that the presence of hydrates does indeed lead to changes in the flow regimes of hydrate slurries. They found that hydrates could cause a transition from a stratified smooth flow to a slug flow or stratified wave flow at lower velocities. Hydrates can also induce a transition from slug and stratified wave flows to annular flow at lower gas velocities. In addition, hydrates make the transition from a slug flow to a bubble flow more likely. Liu et al. [146] pointed out that the presence of hydrates makes the flow regime in inclined pipelines more prone to transitioning into a slug flow, and that hydrates tend to accumulate at the transition sections between inclined and horizontal segments.
3. Detection methods of hydrate blockage
This section provides an overview of several commonly used methods for detecting hydrate blockages, based on the characteristics of hydrate formation and blockages mentioned in the previous section, and lists in Table 1 the relationship between hydrate blockage detection methods and hydrate physicochemical characterizations. These methods can assist professionals in promptly identifying and addressing hydrate blockage issues to prevent economic losses.
3.1. Acoustic pulse reflectometry method
A schematic of a typical acoustic pulse reflectometry method is shown in Fig. 9. When a small acoustic pulse encounters an impedance mismatch, the signal is reflected, with part of the pulse propagating through the mismatch and the other part reflecting back to the acoustic source [147], [148]. The reflection coefficient caused by the impedance change is represented as follows [149]:
where R represents the reflection coefficient, represents the acoustic impedance (Pa·s·m−1), represents the density (kg·m−3), and represents the velocity of the acoustic waves (m·s−1), with i taken as either 1 or 2, representing the media on two sides of the reflective interface, respectively.
When there is a significant difference in the acoustic impedance between the two sides, a larger acoustic reflection coefficient is generated, which is advantageous for accurate detection. In natural gas pipelines, the substantial density difference between the flowing medium and the hydrate blockage creates a significant impedance difference. However, in petroleum pipelines, the smaller density difference results in a lower reflection coefficient, potentially affecting the detection distance and accuracy. Based on the principles of acoustic pulse reflectometry, Morgan and Crosse [150] successfully detected blockages in short pipes. Amir et al. [151] studied blockages in pipelines using this method and found that constriction of a flow cross-section results in positive reflection, whereas expansion results in negative reflection. By combining the propagation velocity of acoustic waves in the pipeline and the time of flight (TOF), information regarding the location of the blockage can be obtained.
where x represents the distance from the measurement point to the blockage (m), c represents the velocity of the acoustic wave in transport medium (m·s−1), and t represents the TOF between the measurement point and the front edge of the blockage (s). The length of the blockage can also be determined using the equation above, where x represents the length of the blockage, and t represents the TOF between the front and back edges of the blockage. However, the latter only applies to cases of incomplete blockage.
The key to determining the position is the velocity of the acoustic wave and the TOF, where c can be calculated based on the ideal gas equation for the acoustic velocity for natural gas pipelines. For petroleum pipelines, c is determined based on the bulk modulus and density. To achieve more accurate positioning, a precise estimation of the acoustic velocity in the fluid is essential. Wang et al. [152] suggested calibrating the acoustic velocity by measuring the TOF between two known points. An et al. [153] used resampling algorithms to compensate for the detection signal drift caused by changes in the acoustic velocity. Duan et al. [154] highlighted the importance of separating incident and reflected pulses in the time domain to accurately determine the value of t, which ensures sufficient distance between the acoustic wave transmitter, acquisition device, and obstacles.
However, due to the complex structure of actual oil and gas pipelines, the transmission path of acoustic wave signals is intricate, and the amplitude of acoustic waves decreases exponentially during transmission, rendering the signal vulnerable to external environmental noise interference. An et al. [155] discovered that blockages can be detected by adjusting the frequency content of acoustic waves and emitting linear frequency-modulated pulses into noise-polluted pipelines. Duan et al. [154] used a cross-correlation method to measure the TOF and found that the TOF varies slightly with the pulse frequency, which is related to the interaction between pulse waves and blockages, as well as the thermal-viscous losses and wave frequency. When the number of acquired acoustic detection signal samples is limited, selecting effective signal denoising methods to obtain accurate characteristic information from the acoustic signal is crucial for pipeline fault diagnosis [156]. During actual detection processes, the measured signals of pipeline blockages exhibit obvious nonlinearity and nonstationarity, owing to the coupling effect between the incident and reflected waves [157], [158].
3.2. Eigenfrequency shift method
Hydrate blockages generate additional acoustic impedance, resulting in changes in the characteristic frequency of the pipeline [159]. Domis [160] studied the relationship between the characteristic frequency and the location of the blockage. Wu and Fricke [161] utilized characteristic frequency shifts to detect blockages at the end of a pipeline and determined the presence of blockages by measuring the characteristic frequencies between any two adjacent points. In addition, the amplitude of the characteristic frequency shift was used to determine the blockage ratio.
Wu and Fricke [162] proposed using the Fourier transform to represent the characteristic frequency shift; this made it possible to determine the position and size of multiple blockages within a pipeline, with particular accuracy for small blockages. The researchers indicated that, because of the cutoff frequency of the pipeline in practical testing, only a limited number of characteristic frequencies could be determined. Moreover, low-order characteristic frequencies are particularly important for blockage detection, and the absence of the first five characteristic frequencies made it impossible to reconstruct a blockage area. De Salis and Oldham [163] utilized broadband maximum-length sequence measurements to obtain the characteristic frequency shift of a pipeline. This method was found to be less time-consuming than frequency sweep tests, and the researchers proposed a new formula for calculating the blockage area without knowing the length of the pipeline. They confirmed Wu and Fricke’s [162] finding that it was impossible to reconstruct blockages with a blockage ratio exceeding or approaching half, due to the limitations of the acquisition method. However, De Salis and Oldham [163] argued that low-order characteristic frequency shifts are unnecessary and that the minimum order frequency shift required depends on the blockage size.
3.3. Vibration analysis method
According to Bernoulli’s principle, when a hydrate blockage forms in a pipeline, the flow area decreases, causing the velocity to increase and the pressure to decrease during the initial blockage stage. Similarly, in the late blockage stage, the fluid velocity decreases, and the pressure increases. The pipeline vibrates owing to changes in the flow velocity and pressure; thus, the blockage condition of the pipeline can be understood by measuring its vibrations. Lile et al. [164], [165] conducted simulations and experimental studies on the influence of blockages on circular pipe vibrations and found that the vibration intensity correlated with the size of the blockage.
3.4. Ultrasonic guided wave method
In the ultrasonic guided wave method, ultrasonic guided waves are excited by ultrasonic transducers and propagate along the pipe wall, carrying information about the pipeline. When the guided waves encounter blockages, reflected and transmitted signals are generated. In an unobstructed pipe, the basic torsional mode T(0,1) is the only torsional mode at low frequencies. However, when blockages are present, this mode becomes dispersed.
Ma et al. [166] assumed that the pipe segment containing a blockage was a dual-layer pipe and located the blockage area by measuring the reflection in the T(0,1) mode. Simonetti and Cawley [167] pointed out that the cutoff frequency of a dual-layer pipe is related to the shear rate and thickness of the blockage. The blockage ratio was calculated by measuring the peak of the reflection coefficient spectrum to determine the cutoff frequency and combining it with the shear rate of the hydrates. In addition, it was found that the transmitted signal could be used for measurements. The blockage ratio could be obtained by calculating the dispersion curve using the hydrates’ shear rate and blockage length and then fitting the dispersion curve to the redistributed spectrum [166], [168]. Ma et al. [168] found that incomplete bonding between the blockage and pipe affected the reflection coefficient spectrum, thus affecting the calculation of the blockage ratio. When the thickness of the blockage varied along the axial direction, the intensity of the reflected signal decreased, whereas that of the transmitted signal increased. When the blockage was irregular, the signal became complex and could not accurately represent the blocked area.
3.5. Transient wave reflectometry method
The method based on transient waves introduces rapid flow disturbances into the pipeline by opening or closing valves and utilizes transient waves to identify anomalies. The transient wave method is also known as the transient pressure, water hammer wave, hydraulic transient, fluid transient, and fast transient method [35], [169]. Initially, this method was mainly applied in water pipe inspection. However, because of the similarities between certain fluid properties of water and oil/gas, the transient wave method was extended to the detection of oil/gas pipelines [170], [171], [172], [173]. It is commonly believed that generating large pressure waves is necessary to detect faults, but this may cause damage to pipelines [169], [174]. Recently, a method that uses small amplitude pressure test waves was proposed to address this issue [175].
When transient waves propagate in both directions inside a pipeline, they encounter blockages and generate reflected and transmitted signals. By collecting the pipeline’s pressure and wave velocity information and considering the pipeline structure, the position and length of the blockage can be calculated, enabling the rapid detection of pipeline obstacles. The monitoring range of the transient wave method is typically several kilometers, owing to the limited propagation distance of the characteristic waveforms [176], [177]. The emitted waveform of the transient wave method has a significant impact on the accuracy of the blockage detection. Lee et al. [178] studied the influence of the transient signal bandwidth on the spatial resolution, accuracy, and range of fault detection. They found that different excitation methods generated transient waves with different effective bandwidths, and the selection of suitable excitation methods must be based on the practical requirements.
According to the processing methods used for the blockage signals, the transient wave method can be divided into the transient wave reflectometry and transient wave frequency response methods. The principle of the transient wave reflectometry method is similar to that of the acoustic pulse reflectometry method. Both methods introduce waves into the pipeline and analyze them using time-domain methods to obtain information related to blockages. Similar to the equation used for the acoustic pulse reflectometry method, the blockage position can be obtained using the following formula:
where a is the velocity of the transient wave in transport medium (m·s−1).
As shown in Fig. 10[178], the reflected waves can be identified by comparing the actual measured signal with the signal without a blockage. Without the pipeline’s complete structural data, it may be difficult to determine the reflected waves or the resulting blockage position and severity, leading to potential ambiguity. In addition, in practical applications, measurement signals are often subject to noise interference, which makes it difficult to determine the arrival time of transient waves [35]. To mitigate noise, multiple repeat experiments can be conducted, or techniques such as wavelet analysis, cross-correlation, and pulse response functions can be used to assist in measuring the TOF [179], [180], [181], [182].
For a single-blockage pipeline system, the larger the reflected wave amplitude, the larger the blockage area. Therefore, the blockage area can be measured based on the relationship between the amplitudes of the reflected and incident waves. Tian et al. [183] directly used the ratio of the reflected wave to the incident wave to characterize the blockage area; however, their result had a large error. Meniconi et al. [175], [184] and Adeleke et al. [185] proposed blockage area detection methods based on the water hammer theory and acoustic principles, respectively. Chu et al. [186] proposed a transient wave attenuation model and accurately detected blockages using the dynamic pressure at three positions. Yu et al. [187] proposed an approximate scattering technique based on the Born approximation to reconstruct the blockage profile in pipelines; this method worked well for mild blockages.
Transient waves in pipelines are affected by factors such as the steady-state friction, unsteady friction, viscoelasticity of the pipe wall, nonlinear effects, amplitude attenuation, and waveform distortion [35], [188]. The transient wave attenuation model was usually considered for gas pipelines by considering nonlinear effects and viscous dissipation [188]. Adeleke et al. [185] pointed out that viscous dissipation does not affect the accuracy of blockage length and position prediction, but it significantly affects the accuracy of blockage severity prediction.
3.6. Transient frequency response method
Transient signals in pipelines exhibit hyperbolic curve characteristics with periodic repetition, making them highly suitable for frequency domain analysis [189]. Stephens et al. [190] and Duan et al. [191] classified blockages into discrete and extended blockages based on the size of the blockage length relative to the total length of the pipeline. As shown in Fig. 11[178], discrete blockages cause changes in the resonant frequency amplitude of a system but do not alter the resonant frequency itself [192], [193]. In contrast, extended blockages simultaneously affect both the resonant frequency and amplitude of the system [194], [195], [196].
Sattar et al. [193] reported that partial blockages led to decreased amplitudes at odd harmonic frequencies and increased amplitudes at even harmonic frequencies in the frequency response. Mohapatra et al. [192] studied single blockages in pipeline systems with branches and determined the approximate position of the blockage by analyzing the distribution of the peak pressure frequency spectra. The number of peaks or valleys was used to determine the blockage location, and the average value of the peak pressure represented the size of the blockage. Mohapatra and Chaudhry [197] further extended this approach to detect multiple blockages but could not determine the size of each blockage. Lee et al. [198] proposed an expression that utilized frequency domain information to locate and determine the size of blockages in simple pipeline systems, which was then extended to detect multiple blockages.
Brunone et al. [199] reported that extended and discrete blockages have significantly different effects on a system’s frequency response, and the methods used for discrete blockages may not apply to extended blockages. Duan et al. [191] analyzed the influence of extended blockages on the resonant frequency of the system and used a genetic algorithm to fit the frequency response to an analytical equation, thereby accurately determining the location, length, and area of the extended blockage. Subsequently, Duan et al. [194] confirmed that this method could accurately describe the frequency shift caused by extended blockages and that the obtained blockage location and length provided more accurate information than the blockage area. Duan et al. [200] explained the frequency shift caused by extended blockages through wave perturbation analysis and simplified their proposed method using a first-order approximation, significantly improving the efficiency of blockage detection.
Louati and Ghidaoui [201], [202] and Louati et al. [203] pointed out that blockages in pipelines interact strongly with waves that satisfy the Bragg resonance condition. Duan et al. [204] stated that current transient-based frequency domain methods do not consider the scattering effects of rough blockages on frequency shifts, resulting in significant errors in determining the length and area of rough or irregular blockages. Che et al. [205] investigated the transient response of nonuniform blockages and found that such blockages weakened the frequency shift of higher-harmonic frequencies. Che et al. [206] further analyzed the influence of exponentially nonuniform blockages on the resonant frequency shift of pipeline systems from an energy perspective. Meniconi et al. [195] proposed a combined method that integrated transient wave reflectometry and frequency response, in which time-domain methods were first used to detect and locate blockages, followed by frequency domain methods to confirm the length and area of the blockages. This method improves detection accuracy and computational efficiency compared with a single method.
3.7. Closed-circuit television (CCTV) method
The CCTV method initially involved using CCTV cameras to record the internal condition of pipelines, followed by analysis to identify pipeline defects. Owing to the high level of expertise required for manual image recognition and its relatively low efficiency, various defect-identification algorithms have been proposed to improve the accuracy and efficiency of this method [207], [208], [209], [210]. However, probes are often tethered or cabled for power and data transmission [211], [212], which restricts their operational range. To overcome this limitation, battery-powered pipeline robots with wireless data transmission and positioning capabilities have been developed. In the oil and gas industry, the integration of CCTV with pigging tools has led to the development of “smart pigs” [213]—robots equipped with various detection devices, such as cameras and ultrasonic sensors, that can navigate pipelines of different shapes, covering distances of several miles [214]. In particular, Zhou et al. [215] designed an intelligent robot equipped with a brush to detect hydrate accumulation on the pipe wall and control the release of the plugging remover, enabling the accurate identification and removal of hydrate blockages.
3.8. Transmission-based method
In transmission-based method, penetrating projection sources (typically gamma rays, X-rays, and ultrasonic waves) are emitted radially along the exterior of the pipeline, and blockage information is obtained by measuring the relative attenuation [216], [217] or arrival time of the radiation [218]. Gouveia et al. [219] successfully employed gamma rays to characterize substances in oil pipelines. Benson and Robins [220] utilized tomographic scanning techniques to perform multiple scans around oil pipelines, generated density images of the interior to determine hydrate blockage situations, and successfully located obstructions between floating production units and subsea manifolds in the ocean. Rao et al. [221] proposed the use of gamma rays for quick scanning to identify suspicious areas before employing photographic techniques to determine blockage situations. Cheng et al. [222] and Sharma et al. [223] obtained information on pipeline blockages by measuring the transmission intensity of rays. Salgado et al. [224], Alkabaa et al. [225], and Askari et al. [226] combined ray detection with artificial neural networks to successfully detect pipeline blockages and demonstrated the feasibility of this technique in multiphase transportation systems for oil, gas, and water. X-rays can also be used to scan blockages in oil pipelines, although they have relatively weaker penetration capabilities in comparison with gamma rays [227], [228]. Harara [229] noted that this method can be applied to almost all types of blockages. However, due to the high radiation intensity, radiographic techniques require the evacuation of all personnel in the detection area to ensure their protection from radiation exposure [230]. Fig. 12[230] shows the comparison of blockages in the pipe cross-section detected by simulated gamma radiography and the actual results.
Ultrasonic testing is a widely used form of non-destructive testing, though research on ultrasonic testing specifically focused on pipeline blockages remains relatively limited. Roslee et al. [231] demonstrated that interference from Lamb waves can be avoided by selecting the appropriate frequency, making ultrasonic waves effective for transmission detection in steel pipes. Piao et al. [232], [233] successfully detected paraffin blockages in pipelines using a pair of ultrasonic sensors placed on opposite radial sides of the pipe; they asserted that this method was highly versatile and effective for various types of blockages. Li et al. [218] developed a portable ultrasonic profiling device for pipeline blockages. This device employs a focused ultrasonic transducer to measure the profile of hydrate blockages inside the pipeline and includes a rail system for continuous measurements. It was used to successfully reconstruct the cross-sectional area of hydrate blockages in a laboratory flow loop.
3.9. Electrical method
Electrical resistance tomography (ERT) and electrical capacitance tomography (ECT) are two types of electrical tomography technologies that measure different electrical properties. Both technologies use electrode sensor arrays to assess electrical characteristics inside pipelines, and both employ physical models and image-reconstruction algorithms to infer material distribution. ERT reconstructs images based on conductivity differences, using sensors that typically consist of metal probes that penetrate to the inner wall of the pipeline. In contrast, ECT utilizes differences in dielectric constants, with sensors generally consisting of metal plates attached to the pipeline’s outer wall. Due to their fast response times and minimal interference with normal pipeline operations, both ERT and ECT are widely used for measuring multiphase flow patterns in the oil and gas sector [234], [235], [236], [237], [238]. Recently, ERT has been employed to study hydrate deposits, offering insights into their distribution and temporal evolution [239], [240], [241]. Although research on hydrate detection using ECT is limited, the significant dielectric constant differences between hydrates and oil or gas indicate that ECT holds considerable potential for detecting hydrate blockages.
Utilizing the significant differences in dielectric constant spectra between hydrates, oil, gas, and water, researchers have used open-ended coaxial probes to monitor the dielectric constant near the pipeline wall [97]. Hydrate deposits as small as less than 1 mm were successfully detected using this method. Research has also been conducted on the use of this method to identify hydrate deposition in vertical dead-leg pipelines; the researchers successfully identified the initial stages of liquid water condensation and hydrate formation in water-saturated gas systems and estimated the porosity and humidity of the deposits [242]. It should be noted that this method is currently in the laboratory stage and requires further development and improvement.
3.10. Backpressure method
The backpressure method, also known as the friction loss method, involves conducting multi-rate tests under steady-state conditions to obtain a baseline curve of the pressure drop versus the flow rate. If the pipeline deviates from the baseline during operation, it is assumed there is a blockage or leakage in the pipeline. Scott and Satterwhite [243] and Scott and Yi [244] used the backpressure method to detect blockages in natural gas and oil pipelines and quantified the measurement errors in blockage severity. However, the backpressure method can only detect the severity of a blockage and cannot determine its specific location. To address this issue, Liu and Scott [245] combined the backpressure method with the transient method. First, they used the backpressure method to detect the blocked area; then, they simultaneously closed the valves on both sides of the pipeline to generate transient disturbances and calculated the position of the blockage by measuring the average pressure difference along the flow stream. Regarding the impact of blockages, Yang et al. [246] studied the effect of blockages on the pressure drop in pipelines using computational fluid dynamics (CFD) and found that the blockage’s length and area significantly impacted the pressure drop, while the position of the blockage had a lesser effect. Ling et al. [247] developed a multi-rate testing method that can determine the location and size of blockages without requiring pressure data at the pipeline inlet and outlet.
3.11. Fiber optics method
Fibers can function as sensors by detecting external physical factors that affect the light transmitted through them [248]. Researchers are currently developing a range of fiber sensors based on fiber Bragg gratings (FBGs), Brillouin scattering, Raman scattering, Rayleigh scattering, the Sagnac effect, and Mach–Zehnder interferometry to measure pressure, temperature, heat flux, strain, and acoustic properties [249], [250]. Optical fibers can provide information on the variations in the temperature, pressure, and flow characteristics inside pipelines at different locations, thus aiding in the analysis of blockage situations.
Fiber sensors have been widely applied in the oil and gas industry [251]. Distributed fiber sensing technology enables pipeline detection along the entire length of a fiber, reaching detection distances of tens of kilometers and up to 300 km using fiber amplifiers [248]. In addition, quasi-distributed FBG fiber sensors can be installed at reserved intervals along the fiber, thereby catering to precise positioning requirements for hydrate blockages and allowing multiple FBG sensors to monitor pipelines spanning tens of kilometers on a single fiber [249], [252].
4. Engineering applications of hydrate blockage detection methods
Several teams have conducted onsite experiments to detect hydrate blockages and have introduced numerous commercial products for pipeline detection. For example, a team led by Papadopoulou from the University of Manchester in the United Kingdom used acoustic pulse reflectometry to detect defects in straight pipelines with a length exceeding 0.5 km under atmospheric pressure [157]. Subsequently, they conducted multiple onsite experiments by injecting high-pressure pulse compression waves into pipelines [152]. Successful blockage detection was achieved under static conditions with lengths exceeding 1.5 km; industrial test pipelines including valves, bends, and pigging devices; small-diameter natural gas pipelines under flowing conditions; and a 24 km-long offshore natural gas pipeline. The researchers found that the detection distance increased as the maximum operating pressure of the pipeline increased. Moreover, with an increase in the pipe diameter, the attenuation of the pulse compression waves decreased, resulting in a further expansion of the detection range.
A team led by Meniconi from the University of Perugia in Italy used transient wave reflectometry to detect the status of a water-supply pipeline in Milan, Italy [253]. The main pipeline had a length of over 6 km and a diameter of 800 mm, including multiple branches. The researchers detected possible anomalies in the pipeline by generating transient waves through pump trips and comparing the results of wavelet transforms and numerical pulse response functions. They emphasized the need to increase the number of measurement sections to improve the accuracy of the diagnostic results. In addition, they used a laboratory-improved portable pressure-wave generator to examine a water-supply pipeline with a diameter of approximately 500 mm and a length of over 1.3 km, successfully locating a shorter branch pipeline [184]. Furthermore, a team led by Ferrante from the University of Perugia connected the detection device to a pipeline through a fire hydrant, accurately introducing transient pressure waves using brushless motors to control valves [254]. The arrival times and amplitudes of the transient waves were precisely identified using wavelet transform and cross-correlation analyses.
A team led by Jiafei Zhao from Dalian University of Technology in China also conducted multiple onsite experiments. The researchers used electromagnetic valves to rapidly open and close a pipeline, releasing fluid to generate negative pressure waves. Blockage detection experiments were conducted in a 2.5 km, 254 mm pipeline in Tianjin, with average positioning errors of 0.29% and 0.65% for two blockages.
Fiber optics are widely used in oil and gas production and transportation. Blockage conditions can be determined by combining the characteristics of hydrate formation with temperature, pressure, strain, flow, and acoustic signals obtained from optical fibers. Shell Global successfully identified wax blockages in oil wells by relating the noise from blockages to distributed acoustic sensing (DAS) data provided by oil producers [255]. The group indicated that this method was also applicable for detecting hydrate blockages.
Recently, the Board of Radiation and Isotope Technology in India conducted onsite inspections of pipelines in a refinery using gamma scanners [221]. The researchers initially determined approximate positions using multiple scans and identified a suspicious range of 3 m. After opening the pipe, they successfully located a stuck (PIG).
Many companies offer mature commercial products and services for detecting pipeline blockages. For example, the method developed by Papadopoulou et al. [157] was commercialized by iNPIPE PRODUCTS. It can be used to monitor multiple blockage points in a 10 km pipeline, either for single blockage detection or long-term online monitoring, with a positioning accuracy of ±2.5 m. Find-Block, developed by Paradigm Flow Services Ltd., locates full-bore blockages using the transient method and has successfully identified a stuck PIG in a 92 km pipeline. The Tracerco Explorer is an external pipeline detection device deployed underwater using remotely operated vehicles (ROVs). It can move along a pipeline and obtain density profiles without removing the pipeline coating, thereby enabling quick screening of blockage positions. A full-bore blockage was detected in a 150 mm diameter pipeline in the North Sea. The Tracerco Discovery is a pipeline CT scanner that provides detailed distributions of hydrate blockages via external scanning. It has been successfully applied to a series of pipelines in the Arabian Gulf and the Gulf of Mexico. TSC Subsea’s ART vPush utilizes resonance for external pipeline detection. Deployed by ROVs, it can run along the top of a pipeline and detect hydrates. It was used to conduct a 33-h scan of 12 km of a 20 mm diameter underwater pipeline at a depth of 1.3 km near Angola, West Africa.
Many pipeline robots have been widely used for pipeline defect detection. These robots can navigate through complex pipelines and cover distances of up to tens of meters. Schempf et al. [211], [256] from Carnegie Mellon University developed the GRISLEE robot system, which utilizes coiled tubing for remote operation and can inspect over 600 m of pipeline per day without stopping pipeline transport. Schempf et al. [257], [258] also developed the Explorer robot system—the first wireless robot deployed in underground gas distribution pipelines. In multiple field trials, this robot successfully traversed several complex pipeline segments, covering over 900 linear meters in a single run. To address issues with the Explorer related to transmission and reception, illumination, and obstacle handling, the next-generation Explorer II was developed and successfully tested in the field [259]. ULC Technologies’ M1 Live Gas Main Inspection Crawler System has been deployed at three locations in London’s medium-pressure pipeline network [260], [261]. This system successfully inspected a 677-m large-diameter gas pipeline with multiple bends while the pipeline remained under pressure, significantly reducing the need for excavation. ROSEN deployed a tethered self-propelled ultrasonic inspection tool from an offshore platform to inspect a 1.3-km unpiggable subsea flowline [262]. The tool precisely controlled the detector’s position and completed corrosion and deformation detection. Wellte’s Well Tractor 212 was successfully deployed to a horizontal oil well 6145 m away and transmitted data successfully [263]. Wang et al. [264] from Shanghai Jiao Tong University developed a new type of pipeline robot and conducted field tests on a 3.5 km beach sea oil pipeline at Shengli Oilfield in Shandong Province, China, achieving positioning accuracy that met the field requirements. Li et al. [265] developed a pipeline inspection robot for the standard pipelines of the China National Petroleum Corporation. In field tests, the robot operated continuously for approximately 31 h, covering about 70 km, and functioned normally at −20 °C, successfully detecting defects as small as 0.4 mm in the pipeline.
Currently, most acoustic pulse reflectometry methods are conducted in low-pressure environments. It is necessary to investigate acoustic wave propagation characteristics further and determine the maximum detection distance in high-pressure pipelines. Low-frequency acoustic waves can propagate over longer distances but have lower detection accuracy, whereas high-frequency acoustic waves offer higher detection accuracy but limited propagation distances. Algorithms must be designed to resolve the tradeoff between the detection distance and accuracy of acoustic waves. In addition, since the power of acoustic waves dictates their propagation distance, developing high-power, pressure-resistant acoustic wave transmitters suitable for industrial applications is crucial. Methods such as the eigenfrequency frequency shift, transient wave reflectometry, and transient frequency response all present issues related to the detection distance, which requires high-power transmitters.
Meanwhile, the impact of high power on pipeline operation as well as the attenuation and distortion of generated waves must need to be further investigated. The widely promising acoustic methods with broad prospects and transient-based methods both rely on having complete structural data for a pipeline. In complex pipeline networks, monitoring signals can be exceptionally complex and must be combined with simulations to eliminate signals from the pipelines, thereby improving the detection efficiency.
The ultrasonic guided wave method presents difficulties regarding buried pipeline inspections, as acoustic waves propagate into the surrounding soil, affecting the detection accuracy. It is necessary to study the influence of the surrounding environment on guided wave propagation or to develop coatings to prevent ultrasonic leakage and improve detection accuracy. Vibration analysis methods that utilize acceleration sensors for localized pipeline vibration detection have a limited detection distance and can be complemented by fiber optics. Similarly, fiber optic detection can be integrated with pipeline flow and hydrate generation simulations. Blockages can be predicted using distributed measurements obtained from fiber optics to train models. However, the main limitation of fiber optics is the need to pre-install fibers along the pipeline, which incurs high equipment and installation costs.
Transmission-based methods are suitable for precise local inspections but are challenging for long-distance inspections. Like ultrasonic guided wave technology, ROVs can be used for segmented inspection. As a traditional detection method, CCTV can be used with a smart PIG; however, accurate positioning methods must be developed to determine real-time positions. The backpressure method can be used as an initial method to enable real-time monitoring. Other methods can be promptly activated for verification when abnormal signals are detected. A combination of multiple methods can be used to achieve real-time monitoring and timely and accurate diagnosis of pipeline blockages, reduce false alarm rates, and improve detection efficiency.
6. Conclusions
This article provided a comprehensive review of hydrate blockage detection techniques in oil and gas pipelines, offering valuable insights for researchers and industry experts in pipeline flow assurance. The detection of hydrate blockages is critical, as the density of blockages increases over time, potentially leading to severe damage. Early detection and accurate diagnosis are essential to prevent operational disruptions.
Various detection methods have been evaluated based on their principles, usage scenarios, and limitations. Acoustic pulse reflectometry is effective for long-distance blockage detection in both onshore and subsea environments, although further research is needed to validate its performance under high-pressure conditions. The eigenfrequency shift method, though useful for onshore pipelines, requires historical data for accurate results. Similarly, vibration analysis can be employed onshore but may benefit from integration with fiber optic technologies to enhance detection accuracy.
Ultrasonic guided wave technology, which is suitable for onshore detection, could be adapted for subsea applications through integration with ROVs. Transient wave reflection and transient frequency response show promise for both onshore and subsea pipelines due to their rapid response and high accuracy, but their application in gas pipelines remains under-researched. CCTV and transmission methods provide high accuracy for short-distance inspections but are limited in detection range and cost-effectiveness, especially in subsea environments. Electrical methods are fast and accurate but are currently limited to onshore applications. The backpressure method, while inexpensive and responsive, lacks precise localization and is best suited for early warnings when combined with other methods. Fiber optics methods offer real-time, high-accuracy detection for both onshore and subsea pipelines but come with higher costs.
For onshore pipelines, the methods discussed are generally applicable due to the easier access and less-severe operating conditions onshore. For subsea pipelines, remote detection techniques such as acoustic pulse reflectometry and transient wave methods are recommended, as they enable blockage identification from platforms or onshore. Subsequent detailed inspections using ROVs with CCTV or transmission methods can further enhance precision.
Future research should address the unique challenges posed by deep-water environments, which can affect the applicability and stability of detection methods. Field verification in these complex conditions is essential. The significant length of deep-water pipelines introduces challenges such as signal weakening, environmental interference, and the need for large-scale equipment deployment. Developing more robust detection methods that can overcome these obstacles is crucial. In addition, 2D and 3D cross-section scanning technologies should be developed to more accurately determine hydrate distribution in pipeline cross-sections, which would provide valuable technical support for the efficient removal of hydrate blockages.
Further efforts should focus on improving detection accuracy, lowering detection thresholds, and enabling early identification of blockages at the initial stage of hydrate formation. This would support better monitoring and early warning systems. Moreover, integrating detection technologies to address multiple pipeline defects, such as plugging, corrosion, and leakage, is crucial for ensuring the safe and reliable operation of pipelines in challenging environments.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (52476058, U21B2065, 52006024, and 52306188) and the National Key Research and Development (2022YFC2806200).
Compliance with ethics guidelines
Yang Meng, Bingyue Han, Jiguang Wang, Jiawei Chu, Haiyuan Yao, Jiafei Zhao, Lunxiang Zhang, Qingping Li, and Yongchen Song declare that they have no conflict of interest or financial conflicts to disclose.
ZhangJ, LiC, ShiL, XiaX, YangF, SunG.The formation and aggregation of hydrate in W/O emulsion containing different compositions: a review.Chem Eng J2022; 445:136800.
[2]
NingY, YaoM, LiY, SongG, LiuZ, LiQ, et al.Integrated investigation on the nucleation and growing process of hydrate in W/O emulsion containing asphaltene.Chem Eng J2023; 454:140389.
[3]
YuW, SongS, LiY, MinY, HuangW, WenK, et al.Gas supply reliability assessment of natural gas transmission pipeline systems.Energy2018; 162:853-870.
[4]
WangT, SunL, FanZ, WeiR, LiQ, YaoH, et al.Promoting CH4/CO2 replacement from hydrate with warm brine injection for synergistic energy harvest and carbon sequestration.Chem Eng J2023; 457:141129.
[5]
WangZ, ZhaoY, ZhangJ, WangX, YuJ, SunB.Quantitatively assessing hydrate-blockage development during deepwater–gas–well testing.SPE J2018; 23(4):1166-1183.
[6]
LangC, ChenZ, HassanpouryouzbandA, FarahaniMV, ZhangL, ZhaoJ, et al.Spontaneous lifting and self-cleaning of gas hydrate crystals.ACS Nano2024; 18(49):33671-33680.
[7]
LiuW, SunZ, HuJ, ChenS, WuK, SunY, et al.Prediction of hydrate formation risk based on temperature–pressure field coupling in the deepwater gas well cleanup process.Energy Fuels2021; 35(3):2024-2032.
[8]
WangF, MaR, XiaoS, EnglishNJ, HeJ, ZhangZ.Anti-gas hydrate surfaces: perspectives, progress and prospects.J Mater Chem A2022; 10(2):379-406.
[9]
FarhadianA, ZhaoY, NaeijiP, RahimiA, BerishaA, ZhangL, et al.Simultaneous inhibition of natural gas hydrate formation and CO2/H2S corrosion for flow assurance inside the oil and gas pipelines.Energy2023; 260:126797.
[10]
ZengX, FengJ, KeW, WangJ, ZhangS, XieY.Film formation kinetics of methane–propane hydrate on gas bubble in MEG and luvicap EG solutions.Appl Energy2023; 330:120301.
BothAK, GaoY, ZengXC, CheungCL.Gas hydrates in confined space of nanoporous materials: new frontier in gas storage technology.Nanoscale2021; 13(16):7447-7470.
LeeD, JeoungS, MoonHR, SeoY.Recoverable and recyclable gas hydrate inhibitors based on magnetic nanoparticle-decorated metal–organic frameworks.Chem Eng J2020; 401:126081.
[15]
LeeD, GoW, KoG, SeoY.Inhibition synergism of glycine (an amino acid) and BMIM BF4 (an ionic liquid) on the growth of CH4 hydrate.Chem Eng J2020; 393:124466.
[16]
AndersonBJ, TesterJW, BorghiGP, TroutBL.Properties of inhibitors of methane hydrate formation via molecular dynamics simulations.J Am Chem Soc2005; 127(50):17852-17862.
[17]
MajidAAA, WuDT, KohCA.A perspective on rheological studies of gas hydrate slurry properties.Engineering2018; 4(3):321-329.
[18]
ZhaoX, FangQ, QiuZ, MiS, WangZ, GengQ, et al.Experimental investigation on hydrate anti-agglomerant for oil-free systems in the production pipe of marine natural gas hydrates.Energy2022; 242:122973.
[19]
XiaoP, LiJ, ZhangHL, ChenGJ, SunCY.Study on fluidizing the highly converted methane hydrate for gas storage and transportation.Chem Eng J2022; 427:132047.
[20]
KamalMS, HusseinIA, SultanAS, von SolmsN.Application of various water soluble polymers in gas hydrate inhibition.Renew Sust Energ Rev2016; 60:206-225.
[21]
ZiM, WuG, WangJ, ChenD.Investigation of gas hydrate formation and inhibition in oil–water system containing model asphaltene.Chem Eng J2021; 412:128452.
KakitaniC, MarquesDC, TeixeiraA, ValimL, Marcelino NetoMA, SumAK, et al.Experimental characterization of hydrate formation in non-emulsifying systems upon shut-in and restart conditions.Fuel2022; 307:121690.
[24]
SumAK.Prevention, management, and remediation approaches for gas hydrates in the flow assurance of oil/gas flowlines.In: Proceedings of OTC Brasil; 2013 Oct 29–31; Rio de Janeiro, Brazil. Richardson: OnePetro; 2013.
[25]
SloanE, KohC, SumA, BallardA, ShoupG, McMullenN, et al.Hydrates: state of the art inside and outside flowlines.J Pet Technol2009; 61(12):89-94.
[26]
GoncalvesM, CamargoR, NieckeleAO, FaracoR, BarretoCV, PiresLFG.Hydrate plug movement by one-sided depressurization.In: Proceedings of 31st ASME International Conference on Ocean, Offshore and Arctic Engineering; 2012 Jul 1–6; Rio de Janeiro, Brazil. New York City: ASME; 2012.
[27]
SloanED.Fundamental principles and applications of natural gas hydrates.Nature2003; 426(6964):353-359.
[28]
SloanED.A changing hydrate paradigm—from apprehension to avoidance to risk management.Fluid Phase Equilib2005; 228:67-74.
[29]
SloanD, KohCA, SumAK.Natural gas hydrates in flow assurance. Gulf Professional Publishing, Burlington (2010)
[30]
PerfeldtCM, SharifiH, von SolmsN, EnglezosP.Oil and gas pipelines with hydrophobic surfaces better equipped to deal with gas hydrate flow assurance issues.J Nat Gas Sci Eng2015; 27:852-861.
[31]
ChaudhariP, ZerpaLE, SumAK.A correlation to quantify hydrate plugging risk in oil and gas production pipelines based on hydrate transportability parameters.J Nat Gas Sci Eng2018; 58:152-161.
[32]
WeiN, PeiJ, LiH, SunW, XueJ.Application of in-situ heat generation plugging removal agents in removing gas hydrate: a numerical study.Fuel2022; 323:124397.
[33]
KashouS, SubramanianS, MatthewsP, ThummelL, FaucheauxE, SubikD, et al.GOM export gas pipeline, hydrate plug detection and removal.In: Proceedings of Offshore Technology Conference; 2004 May 3–6; Houston, TX, USA. Richardson: OnePetro; 2004.
[34]
YuY, SafariA, NiuX, DrinkwaterB, HoroshenkovKV.Acoustic and ultrasonic techniques for defect detection and condition monitoring in water and sewerage pipes: a review.Appl Acoust2021; 183:108282.
[35]
CheT, DuanH, LeePJ.Transient wave-based methods for anomaly detection in fluid pipes: a review.Mech Syst Sig Proc2021; 160:107874.
[36]
BrunoneB, MaiettaF, CapponiC, DuanHF, MeniconiS.Detection of partial blockages in pressurized pipes by transient tests: a review of the physical experiments.Fluids2023; 8(1):19.
[37]
DattaS, SarkarS.A review on different pipeline fault detection methods.J Loss Prev Process Ind2016; 41:97-106.
[38]
WongB, McCannJA.Failure detection methods for pipeline networks: from acoustic sensing to cyber–physical systems.Sensors2021; 21(15):4959.
[39]
WangJ, MengY, HanB, LiuZ, ZhangL, YaoH, et al.Hydrate blockage in subsea oil/gas flowlines: prediction, prevention, and remediation.Chem Eng J2023; 461:142020.
[40]
SunZ, ShiK, GuanD, LvX, WangJ, LiuW, et al.Current flow loop equipment and research in hydrate-associated flow assurance.J Nat Gas Sci Eng2021; 96:104276.
[41]
RaoI, KohCA, SloanED, SumAK.Gas hydrate deposition on a cold surface in water-saturated gas systems.Ind Eng Chem Res2013; 52(18):6262-6269.
[42]
Di LorenzoM, AmanZM, Sanchez SotoG, JohnsM, KozielskiKA, MayEF.Hydrate formation in gas-dominant systems using a single-pass flowloop.Energy Fuels2014; 28(5):3043-3052.
[43]
CharltonTB, Di LorenzoM, ZerpaLE, KohCA, JohnsML, MayEF, et al.Simulating hydrate growth and transport behavior in gas-dominant flow.Energy Fuels2018; 32(2):1012-1023.
[44]
WangY, KohCA, DapenaJA, ZerpaLE.A transient simulation model to predict hydrate formation rate in both oil- and water-dominated systems in pipelines.J Nat Gas Sci Eng2018; 58:126-134.
[45]
TurnerDJ.Clathrate hydrate formation in water-in-oil dispersions [dissertation].Golden: Colorado School of Mines; 2005.
[46]
SongG, LiY, WangW, JiangK, YeX, ZhaoP.Investigation of hydrate plugging in natural gas + diesel oil + water systems using a high-pressure flow loop.Chem Eng Sci2017; 158:480-489.
[47]
ZerpaLE, SalagerJL, KohCA, SloanED, SumAK.Surface chemistry and gas hydrates in flow assurance.Ind Eng Chem Res2011; 50(1):188-197.
[48]
Fidel-DufourA, GruyF, HerriJM.Rheology of methane hydrate slurries during their crystallization in a water in dodecane emulsion under flowing.Chem Eng Sci2006; 61(2):505-515.
[49]
MelchunaA, CameiraoA, HerriJM, GlenatP.Topological modeling of methane hydrate crystallization from low to high water cut emulsion systems.Fluid Phase Equilib2016; 413:158-169.
[50]
AustvikT, LiX, GjertsenLH.Hydrate plug properties: formation and removal of plugs.Ann N Y Acad Sci2000; 912(1):294-303.
[51]
LebaH, CameiraoA, HerriJM, DarbouretM, PeytavyJL, GlenatP.Chord length distributions measurements during crystallization and agglomeration of gas hydrate in a water-in-oil emulsion: simulation and experimentation.Chem Eng Sci2010; 65(3):1185-1200.
[52]
ZerpaLE, AmanZM, JoshiS, RaoI, SloanED, KohC, et al.Predicting hydrate blockages in oil, gas and water-dominated systems.In: Proceedings of Offshore Technology conference; 2012 Apr 30–May 3; Houston, TX, USA. Richardson: OnePetro; 2012.
[53]
BassaniCL, MelchunaAM, CameiraoA, HerriJM, MoralesREM, SumAK.A multiscale approach for gas hydrates considering structure, agglomeration, and transportability under multiphase flow conditions: i. phenomenological model.Ind Eng Chem Res2019; 58(31):14446-14461.
[54]
KvammeB.Enthalpies of hydrate formation from hydrate formers dissolved in water.Energies2019; 12(6):1039.
[55]
AvlonitisD.An investigation of gas hydrates formation energetics.AlChE J2005; 51(4):1258-1273.
[56]
GuptaA, LachanceJ, Sloan JrED, KohCA.Measurements of methane hydrate heat of dissociation using high pressure differential scanning calorimetry.Chem Eng Sci2008; 63(24):5848-5853.
[57]
LievoisJS, PerkinsR, MartinRJ, KobayashiR.Development of an automated, high pressure heat flux calorimeter and its application to measure the heat of dissociation and hydrate numbers of methane hydrate.Fluid Phase Equilib1990; 59(1):73-97.
[58]
KangSP, LeeH, RyuBJ.Enthalpies of dissociation of clathrate hydrates of carbon dioxide, nitrogen, (carbon dioxide plus nitrogen), and (carbon dioxide plus nitrogen plus tetrahydrofuran).J Chem Thermodyn2001; 33(5):513-521.
[59]
YoonJH, YamamotoY, KomaiT, HanedaH, KawamuraT.Rigorous approach to the prediction of the heat of dissociation of gas hydrates.Ind Eng Chem Res2003; 42(5):1111-1114.
[60]
RydzyMB, SchicksJM, NaumannR, ErzingerJ.Dissociation enthalpies of synthesized multicomponent gas hydrates with respect to the guest composition and cage occupancy.J Phys Chem B2007; 111(32):9539-9545.
[61]
LingelemMN, MajeedAI, StangeE.Industrial experience in evaluation of hydrate formation, inhibition, and dissociation in pipeline design and operation.Nat Gas Hydrates1994; 715(1):75-93.
[62]
SinghP, VenkatesanR, FoglerHS, NagarajanN.Formation and aging of incipient thin film wax–oil gels.AlChE J2000; 46(5):1059-1074.
[63]
NaBC, WebbRL.New model for frost growth rate.Int J Heat Mass Transfer2004; 47(5):925-936.
[64]
NicholasJW, KohCA, SloanED.A preliminary approach to modeling gas hydrate/ice deposition from dissolved water in a liquid condensate system.AlChE J2009; 55(7):1889-1897.
[65]
ChongZR, YangSHB, BabuP, LingaP, LiXS.Review of natural gas hydrates as an energy resource: prospects and challenges.Appl Energy2016; 162:1633-1652.
[66]
ShenX, HouG, DingJ, ZhouX, TangC, HeY, et al.Flow characteristics of methane hydrate slurry in the transition region in a high-pressure flow loop.J Nat Gas Sci Eng2018; 55:64-73.
[67]
HassanpouryouzbandA, JoonakiE, FarahaniMV, TakeyaS, RuppelC, YangJ, et al.Gas hydrates in sustainable chemistry.Chem Soc Rev2020; 49(15):5225-5309.
[68]
ZhangX, LeeBR, SaJH, KinnariKJ, AskvikKM, LiX, et al.Hydrate management in deadlegs: effect of header temperature on hydrate deposition.Energy Fuels2017; 31(11):11802-11810.
[69]
ZhangX, LeeBR, SaJH, KinnariKJ, AskvikKM, LiX, et al.Hydrate management in deadlegs: effect of wall temperature on hydrate deposition.Energy Fuels2018; 32(3):3254-3262.
[70]
SongG, LiY, SumAK.Hydrate management in deadlegs: thermal conductivity of hydrate deposits.Energy Fuels2021; 35(4):3112-3118.
[71]
KumarP, TurnerD, SloanED.Thermal diffusivity measurements of porous methane hydrate and hydrate–sediment mixtures.J Geophys Res Solid Earth2004; 109:B01207.
[72]
LiD, LiangD.Experimental study on the effective thermal conductivity of methane hydrate-bearing sand.Int J Heat Mass Transfer2016; 92:8-14.
[73]
RenS, LiuY, LiuY, ZhangW.Acoustic velocity and electrical resistance of hydrate bearing sediments.J Pet Sci Eng2010; 70(1–2):52-56.
[74]
HuGW, YeYG, ZhangJ, LiuCL, DiaoSB, WangJS.Acoustic properties of gas hydrate-bearing consolidated sediments and experimental testing of elastic velocity models.J Geophys Res Solid Earth2010; 115:B02102.
[75]
BuQ, HuG, YeY, LiuC, LiC, WangJ.Experimental study on 2-D acoustic characteristics and hydrate distribution in sand.Geophys J Int2017; 211(2):990-1004.
[76]
BuQ, HuG, LiuC, XingT, LiC, MengQ.Acoustic characteristics and micro-distribution prediction during hydrate dissociation in sediments from the South China Sea.J Nat Gas Sci Eng2019; 65:135-144.
[77]
PrasadNT, MurthyKNVV, SandhyaCS, GobichandhruD, RameshS, RamadassGA, et al.Experimental investigations on in-situ properties measurement of gas hydrate systems.In: Proceedings of 2022; 2022 Feb 21–24; ChennaiOCEANS, India. IEEE; 2022.
[78]
DuchkovAD, DuchkovAA, PermyakovME, ManakovAY, GolikovNA, DrobchikAN.Acoustic properties of hydrate-bearing sand samples: laboratory measurements (setup, methods, and results).Russ Geol Geophys2017; 58(6):727-737.
[79]
XingL, ZhuT, NiuJ, LiuC, WangB.Development and validation of an acoustic–electrical joint testing system for hydrate-bearing porous media.Adv Mech Eng2020; 12(3):1971-1992.
[80]
GimaltdinovIK, KhusainovIG, KhusainovaGY, GimaltdinovaAA.Reflection of acoustic waves from a bubble screen in water with hydrate bubbles.IOP Conf Ser Mater Sci Eng2020; 919:062055.
[81]
HuG, YeY, ZhangJ, LiuC, LiQ.Acoustic response of gas hydrate formation in sediments from South China Sea.Mar Pet Geol2014; 52:1-8.
[82]
GubaidullinAA, BoldyrevaOY, DudkoDN.Waves in porous media containing gas hydrate.In: Proceedings of 15th All-Russian Seminar on Dynamics of Multiphase Media (DMM); 2017 Oct 3–5; Novosibirsk, Russia. AIP Publishing; 2017.
[83]
MinshullTA, SinghSC, WestbrookGK.Seismic velocity structure at a gas hydrate reflector, offshore western colombia, from full-wave-form inversion.J Geophys Res Solid Earth1994; 99(B3):4715-4734.
[84]
ChapmanR.Fiery ice from the sea: marine gas hydrates.J Acoust Soc Am2014; 136(4):2316.
[85]
Du FraneWL, SternLA, WeitemeyerKA, ConstableS, PinkstonJC, RobertsJJ.Electrical properties of polycrystalline methane hydrate.Geophys Res Lett2011; 38:L09313.
[86]
Du FraneWL, SternLA, ConstableS, WeitemeyerKA, SmithMM, RobertsJJ.Electrical properties of methane hydrate plus sediment mixtures.J Geophys Res Solid Earth2015; 120(7):4773-4783.
[87]
ShankarU, RiedelM.Gas hydrate saturation in the Krishna-Godavari basin from P-wave velocity and electrical resistivity logs.Mar Pet Geol2011; 28(10):1768-1778.
[88]
LuR, SternLA, Du FraneWL, PinkstonJC, RobertsJJ, ConstableS.The effect of brine on the electrical properties of methane hydrate.J Geophys Res Solid Earth2019; 124(11):10877-10892.
[89]
PearsonC, MurphyJ, HermesR.Acoustic and resistivity measurements on rock samples containing tetrahydrofuran hydrates—laboratory analogs to natural-gas hydrate deposits.J Geophys Res Solid Earth1986; 91(B14):14132-14138.
[90]
LeeJY, SantamarinaJC, RuppelC.Parametric study of the physical properties of hydrate-bearing sand, silt, and clay sediments: electromagnetic properties.J Geophys Res Solid Earth2010; 115:B11104.
[91]
BuffettBA, ZatsepinaOY.Formation of gas hydrate from dissolved gas in natural porous media.Mar Geol2000; 164(1–2):69-77.
[92]
ZatsepinaOY, BuffettBA.Experimental study of the stability of CO2–hydrate in a porous medium.Fluid Phase Equilib2001; 192(1–2):85-102.
[93]
LiF, SunC, LiS, ChenG, GuoX, YangL, et al.Experimental studies on the evolvement of electrical resistivity during methane hydrate formation in sediments.Energy Fuels2012; 26(10):6210-6217.
[94]
SpangenbergE, KulenkampffJ.Influence of methane hydrate content on electrical sediment properties.Geophys Res Lett2006; 33(24):L24315.
[95]
ZhouX, FanS, LiangD, WangD, HuangN.Use of electrical resistance to detect the formation and decomposition of methane hydrate.J Nat Gas Chem2007; 16(4):399-403.
[96]
LiS, XiaX, XuanJ, LiuY, LiQ.Resistivity in formation and decomposition of natural gas hydrate in porous medium.Chin J Chem Eng2010; 18(1):39-42.
[97]
HaukalidK, FolgeroK.Broad-band permittivity measurements of formation of gas hydrate layers using open-ended coaxial probes.Energy Fuels2016; 30(9):7196-7205.
[98]
JakobsenT, SjoblomJ, RuoffP.Kinetics of gas hydrate formation in w/o-emulsions the model system trichlorofluoromethane/water/non-ionic surfactant studied by means of dielectric spectroscopy.Colloid Surf A Physicochem Eng Asp1996; 112(1):73-84.
[99]
JakobsenT, FolgeroK.Dielectric measurements of gas hydrate formation in water-in-oil emulsions using open-ended coaxial probes.Meas Sci Technol1997; 8(9):1006-1015.
[100]
HaukalidK.Impact of gas hydrate formation/dissociation on water-in-crude oil emulsion properties studied by dielectric measurements.Energy Fuels2015; 29(1):43-51.
[101]
ChaouachiM, FalentyA, SellK, EnzmannF, KerstenM, HaberthuerD, et al.Microstructural evolution of gas hydrates in sedimentary matrices observed with synchrotron X-ray computed tomographic microscopy.Geochem Geophys Geosyst2015; 16(6):1711-1722.
[102]
LeiL, SeolY, JarvisK.Pore-scale visualization of methane hydrate-bearing sediments with micro-CT.Geophys Res Lett2018; 45(11):5417-5426.
[103]
ZhangL, GeK, WangJ, ZhaoJ, SongY.Pore-scale investigation of permeability evolution during hydrate formation using a pore network model based on X-ray CT.Mar Petrol Geol2020; 113:104157.
[104]
KimS, LeeM, LeeK, AhnT, LeeJ.Data-driven estimation of three-phase saturation during gas hydrate depressurization using CT images.J Pet Sci Eng2021; 205:108916.
[105]
KimS, LeeK, LeeM, LeeJ, AhnT, LimJT.Evaluation of saturation changes during gas hydrate dissociation core experiment using deep learning with data augmentation.J Pet Sci Eng2022; 209:109820.
[106]
LeTX, BornertM, AimedieuP, ChabotB, KingA, TangAM.An experimental investigation on methane hydrate morphologies and pore habits in sandy sediment using synchrotron X-ray computed tomography.Mar Pet Geol2020; 122:104646.
[107]
LiR, ZhouY, ZhanW, YangJ.Pore-scale modelling of elastic properties in hydrate-bearing sediments using 4-D synchrotron radiation imaging.Mar Pet Geol2022; 145:105864.
[108]
ZhangL, WangY, LangC, YangL, ZhaoJ, SongY.Crystal growth of CO2–CH4 hydrate on a solid surface with varying wettability in the presence of PVCap.Cryst Growth Des2024; 24(11):4697-4706.
[109]
UchidaT, TakagiA, KawabataJ, MaeS, HondohT.Raman-spectroscopic analyses on the growth-process of CO2 hydrates.Energy Convers Manage1995; 36(6–9):547-550.
[110]
SasakiS, HoriS, KumeT, ShimizuH.Microscopic observation and in situ Raman scattering studies on high-pressure phase transformations of a synthetic nitrogen hydrate.J Chem Phys2003; 118(17):7892-7897.
[111]
KimDY, LeeH.Spectroscopic identification of the mixed hydrogen and carbon dioxide clathrate hydrate.J Am Chem Soc2005; 127(28):9996-9997.
[112]
AbbasiGR, ArifM, IsahA, AliM, MahmoudM, HoteitH, et al.Gas hydrate characterization in sediments via X-ray microcomputed tomography.Earth Sci Rev2022; 234:104233.
[113]
MinagawaH, NishikawaY, IkedaI, MiyazakiK, TakaharaN, SakamotoY, et al.Characterization of sand sediment by pore size distribution and permeability using proton nuclear magnetic resonance measurement.J Geophys Res Solid Earth2008; 113:B07210.
[114]
Rousina-WebbA, LeekDM, RipmeesterJ.Effect of clathrate hydrate formation and decomposition on NMR parameters in THF-D2O solution.J Phys Chem B2012; 116(25):7544-7547.
[115]
SunH, ChenJ, JiX, KarunakaranG, ChenB, RRanjithPG, et al.Optimizing CO2 hydrate storage: dynamics and stability of hydrate caps in submarine sediments.Appl Energ2024; 376:124309.
[116]
LuH, SeoY, LeeJ, MoudrakovskiI, RipmeesterJA, Ross ChapmanN, et al.Complex gas hydrate from the Cascadia margin.Nature2007; 445(7125):303-306.
[117]
ZhangX, DuZ, LuanZ, WangX, XiS, WangB, et al.In situ Raman detection of gas hydrates exposed on the seafloor of the South China Sea.Geochem Geophys Geosyst2017; 18(10):3700-3713.
[118]
MengQ, LiuC, LiC, HaoX, HuG, SunJ, et al.Effect of common guest molecules on the lattice constants of clathrate hydrates.Acta Phys Chim Sin2020; 36(11):1910010.
[119]
KumarR, LingaP, MoudrakovskiI, RipmeesterJA, EnglezosP.Structure and kinetics of gas hydrates from methane/ethane/propane mixtures relevant to the design of natural gas hydrate storage and transport facilities.AlChE J2008; 54(8):2132-2144.
[120]
SumAK, BurrussRC, SloanED.Measurement of clathrate hydrates via Raman spectroscopy.J Phys Chem B1997; 101(38):7371-7377.
[121]
SubramanianS, KiniRA, DecSF, SloanED.Evidence of structure II hydrate formation from methane plus ethane mixtures.Chem Eng Sci2000; 55(11):1981-1999.
[122]
SeoYT, LeeH.C-13 NMR analysis and gas uptake measurements of pure and mixed gas hydrates: development of natural gas transport and storage method using gas hydrate.Korean J Chem Eng2003; 20(6):1085-1091.
[123]
UchidaT, TakeyaS, KamataY, IkedaIY, NagaoJ, EbinumaT, et al.Spectroscopic observations and thermodynamic calculations on clathrate hydrates of mixed gas containing methane and ethane: determination of structure, composition and cage occupancy.J Phys Chem B2002; 106(48):12426-12431.
[124]
Truong-LamHS, ChoSJ, LeeJD.Simultaneous in-situ macro and microscopic observation of CH4 hydrate formation/decomposition and solubility behavior using Raman spectroscopy.Appl Energy2019; 255:113834.
DecSF.Surface transformation of methane–ethane sI and sII clathrate hydrates.J Phys Chem C2012; 116(17):9660-9665.
[127]
Truong-LamHS, SeoS, KimS, SeoY, LeeJD.In situ Raman study of the formation and dissociation kinetics of methane and methane/propane hydrates.Energy Fuels2020; 34(5):6288-6297.
[128]
BbosaB, OzbayogluE, VolkM.Experimental investigation of hydrate formation, plugging and flow properties using a high-pressure viscometer with helical impeller.J Pet Explor Prod Technol2019; 9(2):1089-1104.
[129]
CamargoR, PalermoT, SinquinA, GlenatP.Rheological characterization of hydrate suspensions in oil dominated systems.Ann N Y Acad Sci2000; 912:906-916.
[130]
PengB, ChenJ, SunC, DandekarA, GuoS, LiuB, et al.Flow characteristics and morphology of hydrate slurry formed from (natural gas plus diesel oil/condensate oil plus water) system containing anti-agglomerant.Chem Eng Sci2012; 84:333-344.
[131]
YanK, SunC, ChenJ, ChenL, ShenD, LiuB, et al.Flow characteristics and rheological properties of natural gas hydrate slurry in the presence of anti-agglomerant in a flow loop apparatus.Chem Eng Sci2014; 106:99-108.
[132]
ShiB, ChaiS, DingL, ChenY, LiuY, SongS, et al.An investigation on gas hydrate formation and slurry viscosity in the presence of wax crystals.AlChE J2018; 64(9):3502-3518.
[133]
MajidAAA, WuDT, KohCA.New in situ measurements of the viscosity of gas clathrate hydrate slurries formed from model water-in-oil emulsions.Langmuir2017; 33(42):11436-11445.
[134]
WangW, FanS, LiangD, YangX.Experimental study on flow characters of CH3CCl2F hydrate slurry.Int J Refrig Rev Int Froid2008; 31(3):371-378.
[135]
JoshiSV, GrassoGA, LafondPG, RaoI, WebbE, ZerpaLE, et al.Experimental flowloop investigations of gas hydrate formation in high water cut systems.Chem Eng Sci2013; 97:198-209.
[136]
TangC, ZhaoX, LiD, HeY, ShenX, LiangD.Investigation of the flow characteristics of methane hydrate slurries with low flow rates.Energies2017; 10(1):145.
[137]
SinquinA, PalermoI, PeyssonY.Rheological and flow properties of gas hydrate suspensions.Oil Gas Sci Technol2004; 59(1):41-57.
[138]
LvX, ZuoJ, LiuY, ZhouS, LuD, YanK, et al.Experimental study of growth kinetics of CO2 hydrates and multiphase flow properties of slurries in high pressure flow systems.RSC Adv2019; 9(56):32873-32888.
[139]
ShiB, DingL, LiuY, YangJ, SongS, WuH, et al.Hydrate slurry flow property in W/O emulsion systems.RSC Adv2018; 8(21):11436-11445.
[140]
DingL, ShiB, LiuY, SongS, WangW, WuH, et al.Rheology of natural gas hydrate slurry: effect of hydrate agglomeration and deposition.Fuel2019; 239:126-137.
[141]
AmanZM, Di LorenzoM, KozielskiK, KohCA, WarrierP, JohnsML, et al.Hydrate formation and deposition in a gas-dominant flowloop: initial studies of the effect of velocity and subcooling.J Nat Gas Sci Eng2016; 35:1490-1498.
[142]
FuW, YuJ, XiaoY, WangC, HuangB, SunB.A pressure drop prediction model for hydrate slurry based on energy dissipation under turbulent flow condition.Fuel2022; 311:122188.
[143]
JoshiS.Experimental investigation and modeling of gas hydrate formation in high water cut producing oil pipelines [dissertation].Golden: Colorado School of Mines; 2012.
[144]
ZerpaLE, RaoI, AmanZM, DanielsonTJ, KohCA, SloanED, et al.Multiphase flow modeling of gas hydrates with a simple hydrodynamic slug flow model.Chem Eng Sci2013; 99:298-304.
[145]
DingL, ShiB, LvX, LiuY, WuH, WangW, et al.Investigation of natural gas hydrate slurry flow properties and flow patterns using a high pressure flow loop.Chem Eng Sci2016; 146:199-206.
[146]
LiuZ, LiuZ, WangJ, YangM, ZhaoJ, SongY.Hydrate blockage observation and removal using depressurization in a fully visual flow loop.Fuel2021; 294:120588.
[147]
AlborsGO, KyleAM, WodickaGR, JuanEJ.Computer simulation tool for predicting sound propagation in air-filled tubes with acoustic impedance discontinuities.In: Proceedings of 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2007 Aug 22–26; Lyon, France. IEEE; 2007.
[148]
SilvaLL, MonteiroPCC, VidalJLA, NettoTA.Acoustic reflectometry for blockage detection in pipeline.In: Proceedings of 33rd International Conference on Ocean, Offshore and Arctic Engineering; 2013 Jun 8–13; San Francisco, CA, USA. IEEE; 2013.
[149]
KinslerLE, FreyAR, CoppensAB, SandersJV.Fundamentals of acoustics.Hoboken: John Wiley & Sons; 2000.
[150]
MorganES, CrossePAE.The acoustic ranger, a new instrument for tube and pipe inspection.NDT Int1978; 11(4):179-183.
[151]
AmirN, BarzelayO, YefetA, PechterT.Condenser tube examination using acoustic pulse reflectometry.J Eng Gas Turbines Power Trans ASME2010; 132(1):014501.
[152]
WangX, LewisKM, PapadopoulouKA, LennoxB, TurnerJT.Detection of hydrate and other blockages in gas pipelines using acoustic reflectometry.Proc Inst Mech Eng Part C J Eng Mech Eng Sci2012; 226(C7):1800-1810.
[153]
AnY, WangXC, YueB, QuZG, WuLQ, ChuRH.Compensation of sound velocity variation based on resampling algorithm for natural gas pipeline safety monitoring.Measurement2019; 148:106942.
[154]
DuanW, KirbyR, PrisutovaJ, HoroshenkovKV.On the use of power reflection ratio and phase change to determine the geometry of a blockage in a pipe.Appl Acoust2015; 87:190-197.
[155]
AnY, WangX, YueB, JinS, WuL, QuZ.A novel method for natural gas pipeline safety online monitoring based on acoustic pulse compression.Proc Saf Environ Protect2019; 130:174-181.
[156]
ZhaoY, FengZ, ZhuX.Condition identification of buried drainage pipeline based on CEEMDAN-DE and time-frequency images recognition.In: Proceedings of the 32nd 2020 Chinese Control and Decision Conference (CCDC 2020); 2020 Aug 22–24; Hefei, China. IEEE; 2020.
[157]
PapadopoulouKA, ShamoutMN, LennoxB, MackayD, TaylorAR, TurnerJT, et al.An evaluation of acoustic reflectometry for leakage and blockage detection.Proc Inst Mech Eng Part C J Eng Mech Eng Sci2008; 222(6):959-966.
DomisMA.Acoustic resonances as a means of blockage detection in sodium-cooled fast-reactors.Nucl Eng Des1979; 54(1):125-147.
[160]
DomisMA.Frequency dependence of acoustic resonances on blockage position in a fast reactor subassembly wrapper.J Sound Vib1980; 72(4):443-450.
[161]
WuQL, FrickeF.Estimation of blockage dimensions in a duct using measured eigenfrequency shifts.J Sound Vib1989; 133(2):289-301.
[162]
WuQL, FrickeF.Determination of blocking locations and cross-sectional area in a duct by eigenfrequency shifts.J Acoust Soc Am1990; 87(1):67-75.
[163]
De SalisMHF, OldhamDJ.A rapid technique to determine the internal area function of finite-length ducts using maximum length sequence analysis.J Acoust Soc Am2000; 108(1):44-52.
LileNLT, HasnulMJ, SiregarRA, LeongJC.Effect of blockage size on pipe vibration.Adv Mat Res2012; 626:993-996.
[166]
MaJ, SimonettiF, LoweMJS.Scattering of the fundamental torsional mode by an axisymmetric layer inside a pipe.J Acoust Soc Am2006; 120(4):1871-1880.
[167]
SimonettiF, CawleyP.A guided wave technique for the characterization of highly attenuative viscoelastic materials.J Acoust Soc Am2003; 114(1):158-165.
[168]
MaJ, LoweMJS, SimonettiF.Feasibility study of sludge and blockage detection inside pipes using guided torsional waves.Meas Sci Technol2007; 18(8):2629-2641.
GatoLMC, HenriquesJCC.Dynamic behaviour of high-pressure natural-gas flow in pipelines.Int J Heat Fluid Flow2005; 26(5):817-825.
[171]
YuanZ, DengZ, JiangM, XieY, WuY.A modeling and analytical solution for transient flow in natural gas pipelines with extended partial blockage.J Nat Gas Sci Eng2015; 22:141-149.
[172]
AlghlamAS, StevanovicVD, ElgazdoriEA, BanjacM.Numerical simulation of natural gas pipeline transients.J Energy Resour Technol2019; 141(10):JERT-18-1862.
[173]
HinderdaelM, JardonZ, GuillaumeP.An analytical amplitude model for negative pressure waves in gaseous media.Mech Syst Sig Proc2020; 144:106800.
[174]
GhidaouiMS, MingZ, McInnisDA, AxworthyDH.A review of water hammer theory and practice.Appl Mech Rev2005; 58(1):49-76.
FerranteM, BrunoneB, MeniconiS.Leak detection in branched pipe systems coupling wavelet analysis and a Lagrangian model.J Water Supply Res Technol2009; 58(2):95-106.
[177]
HaghighiA, CovasD, RamosH.Direct backward transient analysis for leak detection in pressurized pipelines: from theory to real application.J Water Supply Res Technol2012; 61(3):189-200.
[178]
LeePJ, DuanHF, TuckJ, GhidaouiM.Numerical and experimental study on the effect of signal bandwidth on pipe assessment using fluid transients.J Hydraul Eng2015; 141(2):04014074.
[179]
BeckSBM, CurrenMD, SimsND, StanwayR.Pipeline network features and leak detection by cross-correlation analysis of reflected waves.J Hydraul Eng2005; 131(8):715-723.
[180]
FerranteM, BrunoneB, MeniconiS.Wavelets for the analysis of transient pressure signals for leak detection.J Hydraul Eng2007; 133(11):1274-1282.
[181]
MeniconiS, BrunoneB, FerranteM.In-line pipe device checking by short-period analysis of transient tests.J Hydraul Eng2011; 137(7):713-722.
[182]
WangX, LinJ, GhidaouiMS.Usage and effect of multiple transient tests for pipeline leak detection.J Water Resour Plann Manage2020; 146(11):06020011.
[183]
TianY, ZhaoX, TianD, WuR, TangH.Dynamic detection of the multiple hydrate blockages in natural gas pipeline using mass pulse at the inlet.Appl Mech Mater2014; 490:490-497.
[184]
MeniconiS, BrunoneB, FrisinghelliM, MazzettiE, LarentisM, CostisellaC.Safe transients for pipe survey in a real transmission main by means of a portable device: the case study of the Trento (I) supply system.Proc Eng2017; 186:228-235.
[185]
AdelekeN, ItyokumbulMT, AdewumiM.Blockage detection and characterization in natural gas pipelines by transient pressure-wave reflection analysis.SPE J2013; 18(2):355-365.
[186]
ChuJ, LiuY, LvX, LiQ, DongH, SongY, et al.Experimental investigation on blockage predictions in gas pipelines using the pressure pulse wave method.Energy2021; 230:120897.
[187]
YuW, WenK, MinY, HeL, HuangW, GongJ.A methodology to quantify the gas supply capacity of natural gas transmission pipeline system using reliability theory.Reliab Eng Syst Saf2018; 175:128-141.
[188]
ZhaoJ, LangC, ChuJ, YangL, ZhangL.Flow assurance of hydrate risk in natural gas/oil transportation: state-of-the-art and future challenges.J Phys Chem C2023; 127(28):13439-13450.
[189]
ZanganehR, JabbariE, ShabakhtyN, KeramatA.The first half-period of pressure heads for frequency response extraction and analysis of waterhammer with fluid–structure interaction.European Journal of Mechanics B Fluids2023; 101:89-105.
[190]
StephensM, LambertM, SimpsonA, NixonJ, VitkovskyJ.Water pipeline condition assessment using transient response analysis.In: Proceedings of New Zealand Water and Wastes Association Conference; 2005; Auckland, New Zealand. The University of Adelaide; 2005.
[191]
DuanH, LeePJ, GhidaouiMS, TungYK.Extended blockage detection in pipelines by using the system frequency response analysis.J Water Resour Plann Manage2012; 138(1):55-62.
[192]
MohapatraPK, ChaudhryMH, KassemA, MolooJ.Detection of partial blockages in a branched piping system by the frequency response method.J Fluids Eng Trans2006; 128(5):1106-1114.
[193]
SattarAM, ChaudhryMH, KassemAA.Partial blockage detection in pipelines by frequency response method.J Hydraul Eng2008; 134(1):76-89.
[194]
DuanH, LeePJ, KashimaA, LuJ, GhidaouiMS, TungYK.Extended blockage detection in pipes using the system frequency response: analytical analysis and experimental verification.J Hydraul Eng2013; 139(7):763-771.
[195]
MeniconiS, DuanHF, LeePJ, BrunoneB, GhidaouiMS, FerranteM.Experimental investigation of coupled frequency and time-domain transient test-based techniques for partial blockage detection in pipelines.J Hydraul Eng2013; 139(10):1033-1040.
[196]
LouatiM, GhidaouiMS, MeniconiS, BrunoneB.Bragg-type resonance in blocked pipe system and its effect on the eigenfrequency shift.J Hydraul Eng2018; 144(1):04017056.
[197]
MohapatraPK, ChaudhryMH.Frequency responses of single and multiple partial pipeline blockages.J Hydraul Res2011; 49(2):263-266.
[198]
LeePJ, VitkovskyJP, LambertMF, SimpsonAR, LiggettJA.Discrete blockage detection in pipelines using the frequency response diagram: numerical study.J Hydraul Eng2008; 134(5):658-663.
[199]
BrunoneB, FerranteM, MeniconiS.Discussion of “detection of partial blockage in single pipelines” by PK Mohapatra, MH Chaudhry, AA Kassem, and.J. Moloo. J Hydraul Eng2008; 134(6):872-874.
[200]
DuanHF, LeePJ, GhidaouiMS, TuckJ.Transient wave–blockage interaction and extended blockage detection in elastic water pipelines.J Fluids Struct2014; 46:2-16.
[201]
LouatiM, GhidaouiMS.Eigenfrequency shift mechanism due to variation in the cross sectional area of a conduit.J Hydraul Res2017; 55(6):829-846.
[202]
LouatiM, GhidaouiMS.Eigenfrequency shift mechanism due to an interior blockage in a pipe.J Hydraul Eng2018; 144(1):04017055.
[203]
LouatiM, MeniconiS, GhidaouiMS, BrunoneB.Experimental study of the eigenfrequency shift mechanism in a blocked pipe system.J Hydraul Eng2017; 143(10):04017044.
[204]
DuanHF, LeePJ, CheTC, GhidaouiMS, KarneyBW, KolyshkinAA.The influence of non-uniform blockages on transient wave behavior and blockage detection in pressurized water pipelines.J Hydro Environ Res2017; 17:1-7.
[205]
CheTC, DuanHF, LeePJ, PanB, GhidaouiMS.Frequency responses for pressurized water pipelines containing blockages with linearly varying diameters.J Hydraul Eng2018; 144(8):04018054.
[206]
CheTC, DuanHF, PanB, LeePJ, GhidaouiMS.Energy analysis of the resonant frequency shift pattern induced by nonuniform blockages in pressurized water pipes.J Hydraul Eng2019; 145(7):04019027.
SuTC, YangMD, WuTC, LinJY.Morphological segmentation based on edge detection for sewer pipe defects on CCTV images.Expert Syst Appl2011; 38(10):13094-13114.
[209]
HalfawyMR, HengmeechaiJ.Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine.Autom Constr2014; 38:1-13.
[210]
ZhouQ, SituZ, TengS, ChenG.Convolutional neural networks-based model for automated sewer defects detection and classification.J Water Resour Plann Manage2021; 147(7):04021036.
[211]
SchempfH, MutschlerE, GoltsbergV, ChemelB.Robotic repair system for live distribution gasmains.Field and Service Robotics Conference, FSR.
[212]
ShiY, HaoL, CaiM, WangY, YaoJ, LiR, et al.High-precision diameter detector and three-dimensional reconstruction method for oil and gas pipelines.J Pet Sci Eng2018; 165:842-849.
[213]
KarkoubM, BouhaliO, SheharyarA.Gas pipeline inspection using autonomous robots with omni-directional cameras.IEEE Sens J2021; 21(14):15544-15553.
[214]
BurkettS, SchempfH.Wireless self-powered visual and NDE robotic inspection system for live gas distribution mains.Report. Pittsburgh: Carnegie Mellon University; 2006.
[215]
ZhouS, YuH, RenZ, MaQ, DuH, DongL, inventors; WangM.An intelligent pipe cleaner for natural gas pipelines.China patentCN202110624300.7. 2021.
[216]
KimJ, JungS, MoonJ, ChoG.A feasibility study on gamma-ray tomography by Monte Carlo simulation for development of portable tomographic system.Appl Radiat Isot2012; 70(2):404-414.
[217]
AlnaimatF, ZiauddinM.Wax deposition and prediction in petroleum pipelines.J Pet Sci Eng2020; 184:106385.
[218]
LiX, LiuY, LiuZ, ChuJ, SongY, YuT, et al.A hydrate blockage detection apparatus for gas pipeline using ultrasonic focused transducer and its application on a flow loop.Energy Sci Eng2020; 8(5):1770-1780.
[219]
GouveiaMAG, LopesRT, de JesusEFO, CameriniCS.Materials characterization in petroleum pipeline using Compton scattering technique.Nucl Instrum Methods Phys Res Sect A2003; 505(1–2):540-543.
[220]
BensonD, RobinsL.Nonintrusive pipeline-inspection techniques for accurate measurement of hydrates and waxes within operational pipelines.In: Proceedings of SPE Offshore Europe Oil and Gas Conference and Exhibition; 2007 Sep 4–7; Scotland, UK. Richardson: OnePetro; 2007.
[221]
RaoP, YelgaonkarV, TiwariC, DhakarV, PanickerM.Locating block caused by stuck PIGs in multi product pipeline using combination of radioisotope techniques.Appl Radiat Isot2023; 193:110660.
[222]
ChengC, JiaW, HeiD, GengS, WangH, XingL.Determination of thickness of wax deposition in oil pipelines using gamma-ray transmission method.Nucl Sci Tech2018; 29(8):109.
[223]
SharmaA, SandhuB, SinghB.Incoherent scattering of gamma photons for non-destructive tomographic inspection of pipeline.Appl Radiat Isot2010; 68(12):2181-2188.
[224]
SalgadoWL, DamRSdF, TeixeiraTP, ContiC, SalgadoC.Application of artificial intelligence in scale thickness prediction on offshore petroleum using a gamma-ray densitometer.Radiat Phys Chem2020; 168:108549.
[225]
AlkabaaAS, NazemiE, TaylanO, KalmounEM.Application of artificial intelligence and gamma attenuation techniques for predicting gas–oil–water volume fraction in annular regime of three-phase flow independent of oil pipeline’s scale layer.Mathematics2021; 9(13):1460.
[226]
AskariM, TaheriA, KochakpourJ, SasanpourMT.An intelligent gamma-ray technique for determining wax thickness in pipelines.Appl Radiat Isot2021; 172:109667.
[227]
CandeiasJP, de OliveiraDF, dos AnjosMJ, LopesRT.Scale analysis using X-ray microfluorescence and computed radiography.Radiat Phys Chem2014; 95:408-411.
[228]
OliveiraDF, SantosRS, MachadoAS, SilvaASS, AnjosMJ, LopesRT.Characterization of scale deposition in oil pipelines through X-ray microfluorescence and X-ray microtomography.Appl Radiat Isot2019; 151:247-255.
[229]
HararaW.Deposit thickness measurement in pipes by tangential radiography using gamma ray sources.Russ J Nondestr Test2008; 44(11):796-802.
[230]
OliveiraDF, NascimentoJR, MarinhoCA, LopesRT.Gamma transmission system for detection of scale in oil exploration pipelines.Nucl Instrum Methods Phys Res Sect A2015; 784:616-620.
[231]
RosleeMN, MujiSZM, PusppanathanJ, ShaibMFA.A pilot study on pipeline wall inspection technology tomography.In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology; 2019 Dec 2–3; Perth, Malaysia. Singapore: Springer; 2021.
[232]
PiaoC, KimSH, LeeJK, ChoiWG, KimYY.Non-invasive ultrasonic inspection of sludge accumulation in a pipe.Ultrasonics.2022; 119:106602.
[233]
PiaoC, LeeJ, KimSH, KimYY.Ultrasonic inspection of sludge accumulated in plastic pipes using meta-slab mode-converting wedge transducers.NDT E Int2024; 142:103020.
[234]
MaungCO, KawashimaD, OshimaH, TanakaY, YamaneY, TakeiM.Particle volume flow rate measurement by combination of dual electrical capacitance tomography sensor and plug flow shape model.Powder Technol2020; 364:310-320.
[235]
ZhangL, WangZ, WuS.Gas–liquid flow behavior analysis based on phase-amplitude coupling and ERT.IEEE Trans Instrum Meas2022; 71:1-10.
[236]
JingN, LiM, LiuL, ShenY, YangP, QinX.Visualization detection of solid–liquid two-phase flow infilling pipeline by electrical capacitance tomography technology.Comput Model Eng Sci2022; 131(1):465-476.
[237]
XuY, PuH, LiY, WangH.Flow pattern identification for gas–oil two-phase flow based on a virtual capacitance tomography sensor and numerical simulation.Flow Meas Instrum2023; 92:102376.
[238]
ZhaoY, YueS, ZhangY, WangH.Flow velocity computation using a single ERT sensor.Flow Meas Instrum2023; 93:102433.
[239]
LiY, WuN, LiuC, ChenQ, NingF, WangS, et al.Hydrate formation and distribution within unconsolidated sediment: insights from laboratory electrical resistivity tomography.Acta Oceanolog Sin2022; 41(9):127-136.
[240]
LiuY, ZouC, ChenQ, ZhaoJ, LiY, SunJ, et al.Cross-hole electrical resistivity tomography as an aid in monitoring marine gas hydrate reservoirs for gas recovery: an experimental simulation study.Geophys J Int2023; 233(1):195-210.
[241]
LiuY, ChenQ, LiS, WangX, ZhaoJ, ZouC.Characterizing spatial distribution of ice and methane hydrates in sediments using cross-hole electrical resistivity tomography.Gas Sci Eng2024; 128:205378.
[242]
SaJH, LeeBR, ZhangX, FolgeroK, HaukalidK, KocbachJ, et al.Hydrate management in deadlegs: detection of hydrate deposition using permittivity probe.Energy Fuels2018; 32(2):1693-1702.
[243]
ScottSL, SatterwhiteLA.Evaluation of the backpressure technique for blockage detection in gas flowlines.J Energy Resour Technol1998; 120(1):27-31.
[244]
ScottSL, YiJ.Flow testing methods to detect and characterize partial blockages in looped subsea flowlines.J Energy Resour Technol1999; 121(3):154-160.
[245]
LiuL, ScottSL.Development of a type curve to locate partial blockages in gas flowlines.In: Proceedings of SPE Annual Technical Conference and Exhibition; 2000 Oct 1–4; Dallas, TX, USA. Richardson: OnePetro; 2000.
[246]
YangL, FuH, LiangH, WangY, HanG, LingK.Detection of pipeline blockage using lab experiment and computational fluid dynamic simulation.J Pet Sci Eng2019; 183:106421.
[247]
LingK, WuX, ShenZ.A new method to detect partial blockage in gas pipelines.Oil Gas Facil2016; 5(5):SPE-174751-PA.
[248]
InaudiD, GlisicB.Long-range pipeline monitoring by distributed fiber optic sensing.In: Proceedings of 2006 International Pipeline Conference; 2006 Sep 25–29; Calgary, AB, Canada. ASME; 2006.
[249]
BrowerDV, PrescottCN, ZhangJ, HowerterC, RaffertyD.Real-time flow assurance monitoring with non-intrusive fiber optic technology.In: Proceedings of Offshore Technology Conference; 2005 May 2–5; Houston, TX, USA. Richardson: OnePetro; 2005.
AshryI, MaoY, WangB, HvedingF, BukhamsinAY, NgTK, et al.A review of distributed fiber–optic sensing in the oil and gas industry.J Lightwave Technol2022; 40(5):1407-1431.
[252]
BertholdJW.Detection of flowline blockage using bragg grating sensors.In: Proceedings of Fiber Optic and Laser Sensors and Applications; Including Distributed and Multiplexed Fiber Optic Sensors VII; 1999 Feb 3; Boston, MA, USA. SPIE; 1999.
[253]
MeniconiS, BrunoneB, FerranteM, CapponiC, CarrettiniCA, ChiesaC, et al.Anomaly pre-localization in distribution–transmission mains by pump trip: preliminary field tests in the Milan pipe system.J Hydroinf2015; 17(3):377-389.
[254]
FerranteM, BartocciD, BustiB, FracchiaS, GentileMT, MarelliF, et al.Diagnosis of water distribution systems through transient tests: the pilot study of Milan.J Water Resour Plann Manage2022; 148(6):05022004.
[255]
PanhuisP, denBoerH, vanDerHorstJ, PalejaR, RandellD, JoinsonD, et al.Flow monitoring and production profiling using DAS.In: Proceedings of SPE Annual Technical Conference and Exhibition; 2014 Oct 27–29; Amsterdam, Netherlands. Richardson: OnePetro; 2014.
[256]
SchempfH, MutschlerE, GoltsbergV, CrowleyW.GRISLEE: Gasmain repair and inspection system for live entry environments.Int J Rob Res2003; 22(7–8):603-616.
[257]
SchempfH, VradisG.Explorer: untethered real-time gas main assessment robot system.Report. San Francisco: Academia; 2003.
[258]
SchempfH, D’ZurkoD.Long-range untethered real-time live gas main robotic inspection system.Report. Washington, DC: US Department of Energy; 2004.
[259]
SchempfH, MutschlerE, GavaertA, SkoptsovG, CrowleyW.Visual and nondestructive evaluation inspection of live gas mains using the explorer™ family of pipe robots.J Field Rob2010; 27(3):217-249.
[260]
Verifying tee installations—solution for unlocatable PE gas main features. Report. Hauppauge: ULC Technologies; 2024.
[261]
Enablement works on London medium pressure gas network. Report. Hauppauge: ULC Technologies; 2024.
[262]
Rosen. Corrosion and crack detection in an offshore riser and flowline. Report. Obere Spichermatt: Rosen group; 2024.
[263]
PLT logging while tractoring with Well Tractor® 212 tandem. Report. Gydevang: Welltec; 2024.
[264]
WangZ, CaoQ, LuanN, ZhangL.Development of an autonomous in-pipe robot for offshore pipeline maintenance.Industrial Robot2010; 37(2):177-184.
[265]
LiH, LiR, ZhangJ, ZhangP.Development of a pipeline inspection robot for the standard oil pipeline of China National Petroleum Corporation.Appl Sci Basel2020; 10(8):2853.
PDF
(3052KB)
