Analytic model research of sound propagation in pipe wall with sound absorption

There are a lot of principles for sound transmission in the pipeline for whether sound transmission structure or noise reduction structure. Even in ultrasonic testing, there is a large number of principles for using pipeline sound transmission. Based on the sound propagation model and the boundary conditions of pipe wall sound absorption, the sound propagation equation for pipe wall sound absorption is given by establishing mathematical model and solving mathematical equation in this paper. When the distribution of sound field along the cross-section of the pipe (outlet) is ignored, the transmission efficiency of sound with different frequencies can be calculated or the sound absorption efficiency can be calculated. The analytical solution of the sound transmission equation in the pipeline has great theoretical significance and practical value for guiding the structural design of sound transmission and noise reduction, improving the calculation efficiency and verifying the numerical analysis results.

Iterative tomography of pipes during operation

The pipe wall thickness was estimated based on three-dimensional images of the pipe recovered from several X-ray projections, which were made in a limited angle of view. Since the effects of scattered radiation and beam hardening are up to 50 % of the main radiation, ignoring them leads to blur of the image and inaccuracy in determining dimensions. To restore pipe images from projections, a volume and/or shell representation of the pipe is used, as well as iterative Bayesian methods. Using these methods, the error in estimating the pipe wall thickness from the projection data can be equal to or less than 300 μm. It has been shown that standard X-ray projections on the film or imaging plates used to obtain data can be used to restore pipe wall thickness profiles in factory conditions.

Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence

Solids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions. More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score. The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before. This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.

Повышение точности прогнозных расчетов технического состояния нефтепроводов с учетом данных датчиков СОД о температуре перекачиваемой нефти

The experience of operating oil main pipelines laid underground in cryolithozone conditions shows that one of the reasons for the decrease in operational reliability of the pipeline is its thermal effect on permanently frozen ground. The parameter included in the list of initial data for predictive calculations of the technical condition of the oil pipeline is the temperature of the pumped oil, which is traditionally determined by the readings of the sensors measuring the temperature of the pipe wall of monitoring and supervisory control systems. However, the distance between these sensors can reach several tens of kilometers, so the measurements are valid only for selected sections on the pipeline segment, the shape of the temperature distribution function between them remains unknown, which negatively affects the accuracy of predictive calculations. To solve this problem it is proposed to use flow temperature sensors installed on cleaning and diagnostic facilities, with the help of which it is possible to measure the temperature of the pumped oil in each section of the pipeline. The authors set a goal to study the applicability of the results of oil temperature measurements by sensors from cleaning and diagnostics facilities to improve the accuracy of predictive calculations of thawing areolas and soil settlements at the base of main oil pipeline. In the course of the study, a series of tests was carried out using the oil temperature sensor installed on the inline inspection tool VIP 40-OPT.00-01.000 and pipe wall strap-on temperature sensor TSPU 011. According to the results of the study, the expediency of using the results of oil temperature measurements by the sensor of inline inspection tool when calculating the temperature of the pipeline wall to select the shape of the approximating function, as well as to solve related problems of geotechnical monitoring was confirmed. In order to improve the accuracy of predictive calculations of thawing areolas and soil settlements, an algorithm has been developed for checking the compliance of the calculated model of the oil pipeline with the actual pumping conditions. Опыт эксплуатации магистральных нефтепроводов, проложенных подземным способом в условиях криолитозоны, показывает, что одной из причин снижения эксплуатационной надежности трубопровода является его тепловое воздействие на многолетнемерзлый грунт. Параметром, входящим в перечень исходных данных для проведения прогнозных расчетов технического состояния нефтепровода, является температура перекачиваемой нефти, которая традиционно определяется по показаниям датчиков измерения температуры стенки трубы систем диспетчерского контроля и управления. Однако расстояние между этими датчиками может достигать десятков километров, поэтому проводимые измерения справедливы только для выбранных секций на участке трубопровода, форма функции распределения температуры между ними остается неизвестной, что отрицательно сказывается на точности прогнозных расчетов. Для решения данной проблемы предлагается использовать датчики температуры потока, устанавливаемые на средствах очистки и диагностики – с их помощью возможно производить измерения температуры перекачиваемой нефти в каждой секции трубопровода. Авторами поставлена цель по исследованию применимости результатов измерений температуры нефти датчиками со средств очистки и диагностики для повышения точности прогнозных расчетов ореолов оттаивания и осадок грунта в основании магистрального нефтепровода. В ходе исследования проведены испытания с использованием датчика температуры нефти, установленного на внутритрубном инспекционном приборе ВИП 40-ОПТ.00-01.000 и накладного датчика температуры стенки трубы ТСПУ 011. По итогам исследования подтверждена целесообразность использования результатов измерений температуры нефти датчиком внутритрубного инспекционного прибора при расчетах температуры стенки трубопровода для выбора формы аппроксимирующей функции, а также для решения сопутствующих задач геотехнического мониторинга. С целью повышения точности прогнозных расчетов ореола оттаивания и осадки грунта разработан алгоритм проверки соответствия расчетной модели нефтепровода фактическим условиям перекачки.

Ultrasonic Measurement for the Experimental Investigation of Velocity Distribution in Vapor-Liquid Boiling Bubbly Flow

This study proposes an ultrasonic velocity profiler (UVP) with a single ultrasonic gas-liquid two-phase separation (SUTS) technique to measure the velocity distribution of vapor-liquid boiling bubbly flow. The proposed technique is capable of measuring the velocity of the vapor bubble and liquid separately in boiling conditions. To confirm the viability of the measurement technique, the experiment is conducted on vertical pipe flow apparatus. The ultrasonic transmission and effect of ultrasonic refraction through the pipe wall and water are investigated at ambient temperature until subcooled boiling temperature is reached. The velocity profile in the water at elevated temperature is measured to verify the ability of the technique in this application. The bubbly flow velocity distribution measurement in boiling conditions is then demonstrated. The results show that the proposed technique can effectively investigate the velocity of both phases under various fluid conditions in boiling bubbly flow.

Analysis of Ultimate Bearing Capacity and Parameters of Steel Support Cutting Pipe Roofing Structure

A new pipe-roof construction method, the steel support cutting pipe method (SSCP), was proposed to improve the construction security and accuracy of pipe jacking as well as underground space usage. The pipe-roof method is one of the underground excavation methods which push multiple steel pipes into the soil, then connect the steel pipes horizontally to form a whole. The proposed structure’s failure mode and force characteristics were determined through theoretical analysis, and then its ultimate bearing capacity and influencing parameters were analyzed through laboratory experiments and numerical simulation. The research results show that the structure’s bearing capacity depends on the steel pipe’s buckling load; the structure’s failure mode is a result of the steel pipe’s buckling. The ultimate bearing capacity of the pipe-roof structure first increases and then decreases with the increase of the steel pipe chord height ratio. The ultimate bearing capacity reaches the maximum when the ratio is 0.33. In addition, the structure’s ultimate bearing capacity is positively related to the steel pipe wall thickness and the pipe section’s length. This can be obtained from the relationship curve showing that the steel pipe wall thickness should be selected according to the engineering requirements and that the pipe section’s length is preferably 2.3 times the diameter of the steel pipe in the construction design.

Bending-induced local buckling during offshore installation of multi-layered FRP pipelines

In the last two decades FRP pipelines have attracted the attention of the oil industry because of their high strength, excellent fatigue performance and low specific weight. On the other hand, the final cost of installation of FRP pipelines is comparable to the cost of carbon steel ones. Therefore, their implementation in offshore applications seems to be advantageous. During offshore installation, the curvatures of the pipes during the S-lay or J-lay installation processes cause high bending stresses and risk for bending-induced local buckling. Since the pipe wall is multi-layered and the laminae are anisotropic, the calculation of critical bending moments is difficult. In the present work, an analytical solution of critical bending moments for bending-induced local buckling is provided. The proposed method uses the classical lamination theory of multi-layered anisotropic materials and Flügge’s assumption for local buckling analysis of pipelines. Results for E-Glass fiber reinforced polymeric pipelines are provided and discussed.