Blockage Detection in Pipeline Based on the Extended Kalman Filter Observer

Currently numerous approaches with various applicability have been generated in order to detect damage in pipe networks. Pipeline faults such as leaks and partial or complete blockages usually create serious problems for engineers. The model-based leak, as well as block detection methods for the pipeline systems gets more and more attention. Among these model-based methods, the state observer and state feedback based methods are usually used. While the observability, as well as controllability, are taken to be the prerequisites for utilizing these techniques. In this work, a new technique based on the extended Kalman filter observer is proposed in order to detect and locate the blockage in the pipeline. Furthermore, the analysis of observability and controllability in the pipe networks is investigated. Important theorems are given for testing the observability as well as controllability of the pipeline system

Optimization Procedure to Identify Blockages in Pipeline Networks via non-invasive Technique based on Genetic Algorithms

The existence of blockages in pipeline networks leads to serious issues that affect the efficiency of the infrastructure, losses of services and environmental risks. To these regards, this study proposes a technique to identify the pipes that are blocked within pipeline or a complex pipe network. This thesis focuses on detecting blockages by using a technique based on a few measurements that are usually gathered from normal operational conditions of the pipeline system. The same approach can be implemented in different fields of engineering to identify the damage, which it is the object of recent interest and development. Such technique can provide significant economic benefits especially for the gas and oil industries (i.e., this pipe blockage detection method leads to time and monetary savings compared to traditional inspection techniques which are more expensive). Long term blockages have the potential to cause permanent damage inside the pipes. To this respect, an optimization procedure that relies upon non-invasive measurements of the flow rate and pressure head, is used to assess the system functionality through Genetic Algorithms (GAs) that aim to solve this problem and perform the optimization procedure. The framework of this technique relies on both a Finite Element-like simulator and GAs to perform the optimization procedure. More investigations have been done experimentally and numerically in this study to determine the occlusions that occur inside looped or branched pipeline networks. The main contribution of the following study explores the validity, sensitivity and accuracy of such methodology by considering different blockage scenarios through two major parts: Part 1 (Experimental work) - A series of experiments were designed and performed by our team, involving myself and 7 more students from the civil and mechanical engineering departments under my supervision, in the span of 12 weeks to validate the robustness of the proposed technique empirically. The study was proven numerically by some researchers with real cases [Marzani et al., 2013 and Bocchini et al., 2014], while there has not been any research publicly available to validate this technique experimentally. For the first time, a comprehensive empirical study has examined the capability of this technique to identify the presence of blockages within different pipeline networks (evaluate the accuracy and the sensitivity). Several looped and branched networks by utilizing PVC pipes were tested throughout this study. The experimental data (flow in pipes and nodal pressure heads) acquired from the testing were analyzed and used to validate the proposed technique. Based on empirical data, it is evident that the technique could successfully identify the location of blockages inside the pipes with a reasonable degree of accuracy. More importantly, the proposed technique can cope even with missing measurements. Such technique is still a valid option for detecting the blockage in pipeline system, but with limitation in the accuracy based on several parameters (i.e., the structure of the network itself, the selected objective function and boundary conditions). Results, errors and conclusions are presented thereafter. Part 2 (Theoretical work) – Several numerical tests have been conducted to improve the technique by considering parametric studies. The theoretical work is focused on assessing the accuracy, robustness, computational efficiency and limits of applicability of the methodology. Many parameters are taken into consideration, such as friction factor (��), objective function (� (�)) and other design criteria (i.e., the input data) to observe its effect on the technique’s sensitivity. As a part of this study, strategies to improve the technique are investigated and summarized. Then, real cases are considered to evaluate the overall performance of the suggested technique. The results of blockage identification, advantages and disadvantages of the procedure for practical implementation are presented

Analysis Of Pressure Distribution Along Pipeline Blockage Based On The Cfd Simulation

Pipeline blockage, which results from solid and hydrocarbon deposition caused by changes in pressure, temperature, or composition, is a critical issue in oil & gas production and transportation systems. Sometimes blockage, which extends several miles in the long-distance pipeline, can be assumed as a new pipe with a smaller diameter. Therefore, it is imperative to detect the location and size of blockage in pipelines more accurately and efficiently to reduce the number of pipeline accidents. This paper explores the distribution of pressure and pressure gradient through the pipeline without/with single blockage under different operating conditions. 3-dimensional (3D) computational fluid dynamic (CFD) simulations under steady state are carried out to examine the effects of blockage location, blockage diameter and blockage length. The orthogonal array testing technique is applied to study the extent to which factor affects the pressure drop most. The dimensionless parameters like dimensionless blockage location, dimensionless blockage diameter, dimensionless blockage length and dimensionless pressure drop, are introduced to evaluate the relationship among the pressure drop and blockage characterizations. Three fitting formulas of dimensionless parameters distribution are proposed and could be used to locate the pipeline blockage and estimate its diameter and length as well. Finally, laboratory experiments were run to validate the blockage prediction model. The fluid frictional apparatus is modified by replacing part of the pipe with a section of small diameter pipe to simulate the actual partial blockade pipeline. The obtained deviations of pressure drop between the lab experiment result and the prediction model is limited to under 30%. Therefore, the deviation should be taken into account while assessing the blockage through the pipeline based on the blockage prediction model, which also allow the operator to assess partial blockage efficiently and economically

Experimental verification of pipeline frequency response extraction and leak detection using the inverse repeat signal

Received 3 June 2014; accepted 20 October 2015/Open for discussion until 31 October 2016This paper presents the original design of a side-discharge valve based transient generator that can produce two types of pseudorandom binary signals: a maximum length binary signal and an inverse repeat signal. These two signals are both wide bandwidth, persistent and periodic, but the inverse repeat signal has the advantageous property that it is antisymmetric within each period. The two signals are used to extract the frequency response function of a single water pipeline in the laboratory. The experimental results demonstrate that the frequency response function extracted by the inverse repeat signal is closer to the theoretical linear results as obtained from the transfer matrix method due to it being able to cancel the effect of even-order nonlinearities. The customized transient generator is then applied to a pipeline with a leak. The location of the leak is successfully determined using the first three resonant peaks as extracted by the inverse repeat signal.Jinzhe Gong, Martin F. Lambert, Aaron C. Zecchin, Angus R. Simpso

Hydrate Formation in Multiphase Flow in Pipe

Hydrate blockage in pipelines is a serious problem to the oil and gas industries. Hydrate formation occur in pipelines which are under high pressure and fairly low temperature, most frequently encountered in deep sea oil and gas production. Plugged-up pipelines cast impacts on the fluid multiphase flow in pipes such as pressure drop and decreased flow rate. Specifically, this research’s objectives are firstly i) to develop multiphase model of the hydrate formation and deposition inside a multiphase flow pipe and ii) to investigate the effect of different inlet velocity, hydrates particles diameter, interfacial area density and flow viscosity on the hydrate formation and plugging behavior in pipelines. This research had performed modeling on the multiphase flow and hydrate growth using ANSYS CFX. The two objectives were met at the end of the project as a multiphase model which is able to represent the hydrate formation in multiphase flow pipe was developed. Also, the relationship between flow inlet velocity, hydrates particle diameter, interfacial area density and flow viscosity variation and the hydrate formation and plugging behavior in pipelines had been determined. Knowledge obtained from the research serves to further improve the oil and gas industry nowadays by maximizing its profit margin through implementation of hydrate plug-free pipelines

Condition assessment for water distribution pipelines using inverse transient analysis and the reconstructive method of characteristics

Modern civilisations rely on water distribution systems to deliver water resources to domestic and industrial consumers. During the lifespan of pipeline assets, they naturally deteriorate due to a combination factors such as ground movement, fatigue, high stresses, and external or internal corrosion. The gradual deterioration of pipelines may lead to the failure of pipelines which may have severe consequences in terms of water resource loss, disruption to industry, traffic and the wider community, repair costs and compensation claims. Developing an efficient and reliable pipeline condition assessment approach is essential to decision-making involving inspection, rehabilitation and replacement. Many existing methods can only investigate pipeline condition over a limited range, which makes them slow and expensive. Fluid transient-based methods can cover several kilometres of pipeline using a few seconds of transient-based test data, due to the fast wave propagation speed. In addition, a transient event can be generated and measured at existing access points along pipelines (for example, air valves or fire hydrants), so cutting the pipeline open and/or draining out the water from the pipeline is not required. Overall, fluid transient-based methods are cost-effective and non-invasive, which make them a promising tool for the future. To achieve the goal of continuous condition assessment for water distribution pipelines, this research focuses on the Inverse Transient Analysis (ITA) method and the previously developed Reconstructive Method of Characteristics (RMOC). The research proposes a faster and improved ITA approach by incorporating a new Head Based Method of Characteristics (HBMOC) and a flexible grid, which enhances the computational efficiency and avoids the need for incorporation of interpolation schemes such as those used in the traditional MOC approach. This efficient ITA approach is then developed into the multi-stage parameter-constraining inverse transient analysis (ITAMP) [MP subscript] by iteratively limiting the search-space, to overcome problem of lack of identifiability when inverse problems involve hundreds of decision variables. The previously developed RMOC for pipeline condition assessment requires a dead-end boundary and an access point immediately upstream of the dead-end boundary, which is difficult to achieve in the field. The RMOC is significantly generalised in this thesis by relaxing this requirement. The new generalised RMOC utilises two pressure transducers placed at any two interior points along a pipeline to achieve pipeline condition assessment. The number and location of pressure transducers required to achieve optimum identifiability are also investigated. It has been demonstrated by the generalised RMOC that if the pipeline condition between the two pressure transducers is unknown, pressure measurements by two transducers are not able to uniquely identify the wave speed distribution along a pipeline using transient-based methods. To improve identifiability, given that the first two sensors are N reaches apart (i.e. N pipe segments in the pipeline model), the third sensor should not be placed at nodes that are separated from any of the first two sensors by an integer multiple of N reaches. The generalised RMOC also provides insight into why general ITA methods struggle to find good solutions as it illustrates that an infinite number of plausible solutions are possible for the almost same pressure trace if the wave speed values between transducers are allowed to vary and a third sensor is placed at an integer multiple location. The verification of ITAMP [MP subscript] and generalised RMOC by a field and a laboratory experiment, respectively, demonstrates that methods developed in this research can serve as a valuable screening tool for pipeline condition assessment in the real world.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201

COMPUTATIONAL FLUID DYNAMICS (CFD) SIMULATION OF SAND DEPOSITION IN PIPELINE

Past reviews have demonstrated that transportation of reservoir fluid through pipeline is one of the most cost effective options for delivering the feed to the processing facility. However, most of the time sand particles are co-produced with the fluids. This will lead to sand deposition on the bottom of the pipeline whenever the transporting fluid velocity is below the critical velocity required. To prevent this from happening and ensure flow assurance, it is crucial to measure and identify the critical velocity. This study presents the results obtained from computational fluid dynamics (CFD) simulation for identifying critical velocity where the formation of static sand bed occurs. The critical velocity is found to be fairly influenced by the sand volume fraction. It was observed that formation of sand dunes occur at the bottom of the pipe at low fluid velocity. The result from the simulations is compared with other studies for validation and analytical comparison

Application of Artificial Intelligence Approaches in the Flood Management Process for Assessing Blockage at Cross-Drainage Hydraulic Structures

Floods are the most recurrent, widespread and damaging natural disasters, and are ex-pected to become further devastating because of global warming. Blockage of cross-drainage hydraulic structures (e.g., culverts, bridges) by flood-borne debris is an influen-tial factor which usually results in reducing hydraulic capacity, diverting the flows, dam-aging structures and downstream scouring. Australia is among the countries adversely impacted by blockage issues (e.g., 1998 floods in Wollongong, 2007 floods in Newcas-tle). In this context, Wollongong City Council (WCC), under the Australian Rainfall and Runoff (ARR), investigated the impact of blockage on floods and proposed guidelines to consider blockage in the design process for the first time. However, existing WCC guide-lines are based on various assumptions (i.e., visual inspections as representative of hy-draulic behaviour, post-flood blockage as representative of peak floods, blockage remains constant during the whole flooding event), that are not supported by scientific research while also being criticised by hydraulic design engineers. This suggests the need to per-form detailed investigations of blockage from both visual and hydraulic perspectives, in order to develop quantifiable relationships and incorporate blockage into design guide-lines of hydraulic structures. However, because of the complex nature of blockage as a process and the lack of blockage-related data from actual floods, conventional numerical modelling-based approaches have not achieved much success. The research in this thesis applies artificial intelligence (AI) approaches to assess the blockage at cross-drainage hydraulic structures, motivated by recent success achieved by AI in addressing complex real-world problems (e.g., scour depth estimation and flood inundation monitoring). The research has been carried out in three phases: (a) litera-ture review, (b) hydraulic blockage assessment, and (c) visual blockage assessment. The first phase investigates the use of computer vision in the flood management domain and provides context for blockage. The second phase investigates hydraulic blockage using lab scale experiments and the implementation of multiple machine learning approaches on datasets collected from lab experiments (i.e., Hydraulics-Lab Dataset (HD), Visual Hydraulics-Lab Dataset (VHD)). The artificial neural network (ANN) and end-to-end deep learning approaches reported top performers among the implemented approaches and demonstrated the potential of learning-based approaches in addressing blockage is-sues. The third phase assesses visual blockage at culverts using deep learning classifi-cation, detection and segmentation approaches for two types of visual assessments (i.e., blockage status classification, percentage visual blockage estimation). Firstly, a range of existing convolutional neural network (CNN) image classification models are imple-mented and compared using visual datasets (i.e., Images of Culvert Openings and Block-age (ICOB), VHD, Synthetic Images of Culverts (SIC)), with the aim to automate the process of manual visual blockage classification of culverts. The Neural Architecture Search Network (NASNet) model achieved best classification results among those im-plemented. Furthermore, the study identified background noise and simplified labelling criteria as two contributing factors in degraded performance of existing CNN models for blockage classification. To address the background clutter issue, a detection-classification pipeline is proposed and achieved improved visual blockage classification performance. The proposed pipeline has been deployed using edge computing hardware for blockage monitoring of actual culverts. The role of synthetic data (i.e., SIC) on the performance of culvert opening detection is also investigated. Secondly, an automated segmentation-classification deep learning pipeline is proposed to estimate the percentage of visual blockage at circular culverts to better prioritise culvert maintenance. The AI solutions proposed in this thesis are integrated into a blockage assessment framework, designed to be deployed through edge computing to monitor, record and assess blockage at cross-drainage hydraulic structures