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A mechanistic model has been applied for the prediction of corrosion rate in multiphase (water–oil-gas) flow regimes. This is due to inadequacies of existing models to adequately address a wide variety of corrosion dependent parameters with special emphasis on multiphase flow parameters. This model takes into account; water cut, volumetric phase fraction of oil and gas, temperature, pressure, other data collected relating to the pipe age, sand particle deposit, CO2 partial pressure, flow velocity, pH, chloride contents, mass transfer coefficients, and hydrogen sulfide. The applied model results show well above 91 percent prediction accuracy, while the prediction error ranges between 0.022 and 0.045 percent. When predicted results compared actual value (experimental data) with DeWaard and Milliams’ model; used in the oil and gas industries, it showed good agreement. It is seen that the resulting model produced an R2 value of 0.91 in comparison to 0.53, the value for Dewaard and Milliams’. It is obvious from numerical simulation that the multiphase flow parameters have major effects on the entire rate of corrosion of oil and gas multiphase flow pipelines. Finally, this research concluded by showing the developed model performance and comparison was made with numerical results. It is expected that the outcome of this research will be of significant help for the strengthening of the application of mechanistic models for predicting corrosion rate. This model will be useful for oil and gas industries worldwide for accurate prediction of corrosion rate.

Sand deposition in pipelines during crude oil transportation is a serious industrial problem; these problems often lead to extra expenses in the course of transportation, causing a threat to the environment and economic damage. The intent of this study is to model the minimum condition required to transport sand particles as dispersed phase (Suspended particles) in flowing crude in pipelines. A mechanistic approach based on force balance and particle trajectory of Lagrangian Multiphase modeling was used to develop a model for determining the minimum transport condition for sand flowing in petroleum to avoid sand trapping along the bed of the pipeline. The model developed is a quadratic function of slip velocity and the findings was compared with experimental data and other three models and found to compete favorably. Consistency exists between the predicted outcomes and the experimental results with a relative error of 1.1%. This model will be useful for oil and gas industries worldwide for accurate prediction of critical suspending velocities of petroleum containing sand and other tiny particles to avoid deposition of particles along the pipeline and eliminate corrosion-related danger in the pipelines caused by the particles.

The reverse distribution of renewable energy resources and load centers makes exploring the optimal transmission mode of long-distance and large-scale renewable hydrogen the key to solving the bottleneck of renewable hydrogen development. This study incorporates hydrogen pipeline (HGP), natural gas pipeline (NGP), and Ultra High Voltage (UHV) into an optimal planning model framework and analyzes the optimal transmission mode, quantity, network, and cost of large-scale renewable hydrogen in China. Constructing a sensitivity analysis framework, this study also investigates the optimal transmission mode changes under different scenarios. The results show that the optimal mode of large-scale renewable hydrogen transmission in the province is NGP, and 5.4% of supply level is the critical point to export renewable hydrogen inter-provincially. It switches to the combination of NGP and HGP when the unit transmission costs of these elements decrease to a certain proportion simultaneously or switches to HGP when the unit transmission cost of HGP decreases more than that of NGP. The complementary transmission mode of NGP and UHV is the optimal mode for inter-provincial transmission, and the HGP can be put into use for inter-provincial transmission only when the unit transmission cost of which is reduced to less than 25%. Jilin is the key node in the NGP network, and Tibet and Gansu are the key nodes in UHV network, and the participation or absence of which will have significant impact on the renewable hydrogen transmission system. Only minor adjustments to the transmission technical parameters of NGP or HGP can promote the qualitative overflight of the optimal transmission volume of them so as to achieve the target optimization at the minimum cost.

There are situations in offshore energy development where potential impact forces between submarine slides and pipelines need to be estimated. The horizontal slide – pipeline impact force, parallel to the main travel direction of the sliding mass and normal to the pipeline axis, is generally dominant compared with other force components and hence of particular concern. In practice, pipelines may be suspended at varying distances above the seabed (gap) and existing methods do not consider how this will affect the horizontal slide – pipeline forces. This paper investigates the effects of pipeline–seabed gap and pipeline diameter on the horizontal slide – pipeline impact force via 181 computational fluid dynamics (CFD) simulations at Reynolds numbers of 0.36–287. Results show that variation in the pipeline–seabed gap and pipeline diameter alters the slide mass flow behaviour as it flows past the pipeline and hence the impact force when the pipeline–seabed gap is below a critical value. A modified hybrid geotechnical – fluid dynamics framework for estimating the horizontal impact force is proposed by considering the effects of the pipeline–seabed gap and pipeline diameter, which is validated with existing experimental datasets.

This protocol aims to develop, validate, and deploy a prediction model using high dimensional data by both human and machine learning. The applicability is intended for clinical prediction in healthcare providers, including but not limited to those using medical histories from electronic health records. This protocol applies diverse approaches to improve both predictive performance and interpretability while maintaining the generalizability of model evaluation. However, some steps require expensive computational capacity; otherwise, these will take longer time. The key stages consist of designs of data collection and analysis, feature discovery and quality control, and model development, validation, and deployment.

Buried pipelines are commonly damaged when laid across strike-slip faults, always leading to some level of destruction with fault movement. The moment of the neutral axis of a pipe section was obtained by integration, and the equations for the bending moment and the bending strain on a pipeline’s section were presented here, based on a trilinear stress–strain model. The calculation method for the lateral soil pressure and pipeline’s bending strain near the fault-crossing point was improved. Lateral soil pressure was regarded as being related to lateral pipeline movement and, for accurate calculation, this part of the pipeline was divided into finite element segments. An iterative process for solving for pipe bending strain was derived, and the algorithm and program for calculating bending strain and the potential damage position of the pipeline’s sections was compiled based on Matlab software. Compared with finite element method (FEM) results and the current standard method, in the situation of a pipeline being in tension under the action of a strike-slip fault (intersection angle

Water distribution and transmission lines are indispensable to urban infrastructure. The water pipelines are subject to both structural and functional deterioration due to various reasons including aging, negligence, and high demand for water supply. Hence, to ensure a safe and reliable water supply, the water utilities need to perform routine pipe condition assessments. The condition assessment is usually carried out by visual inspection with the machine vision system carried by a robotic platform. The inspection platforms will capture the internal condition of the water pipelines in a video stream. However, the robotic platform frequently experiences difficulties while traversing through the valves installed along the pipeline. This inhibits and disrupts the inspection process of the water pipelines. Therefore, this paper proposes a deep learning-based automatic valve detection framework to facilitate the robot's navigation and ensure continuous inspection without any interruptions. The valve detection model is developed by combining MobileNet-160 and Feature Pyramid Network (FPN) and is named as MFPN. The developed framework also employs a generative adversarial network to solve the sparse data set issues and improve the generality of the framework. The comparative study and ablation analyses demonstrate that the proposed framework can achieve a higher mAP value of 89.11% in comparison with the state-of-the-art. Hence, this light-weight and efficient solution can be deployed to the robotic platform for real-time valve detection and enable autonomous navigation of the robotic platform for condition assessment of water pipelines.

Sub-bottom profiler (SBP) measurements play a very important role in marine pipeline detection and checking. However, sometimes because of bad sea conditions and complex underwater environments, only poor SBP images can be obtained. Pipeline recognition based on a poor SBP image manually is tedious and has low accuracy. In this article, based on the specific hyperbola shapes caused by pipelines on SBP images, a novel method to detect the marine pipelines automatically and robustly is presented. Two main steps are used. First, a comprehensive edge extraction method is proposed, which includes line-like structure enhancement and preliminary edge extraction, local phase calculation and phase edge extraction, final edge extraction, as well as edge processing. By applying this method, interference such as reverberation can be eliminated, and fine hyperbola edges can be obtained. Second, by referencing the theoretical hyperbola and using a buffer zone and a hyperbola contour consistency (HCC) criterion, the location of the pipeline can be determined robustly. This method was implemented and verified using data collected under real conditions. The results show that the proposed method is practicable.

The growing penetration of hydrogen vehicles and modern energy conversion technologies strengthen the coupling of transportation and energy networks. This paper proposes a hydrogen supply infrastructure planning model for the integration of hydrogen vehicles and renewable energy. To flexibly meet the energy demand of hydrogen vehicles, the proposed model makes investment decisions for various facilities, including hydrogen pipelines, hydrogen refueling stations, power to gas devices, and renewable energy generators. Besides, with the pipeline transportation method applied, the hydrogen network is constructed and coordinated with the electricity and transportation networks. The multi-network synergistic effect is thus fully utilized, and brings higher operational flexibility and investment economy. Furthermore, a two-stage stochastic planning model is provided to accommodate the variability of renewable energy and traffic loads. To reduce the computational complexity of the proposed stochastic planning model, both linearization techniques and Benders decomposition algorithm are applied. Simulation results of the 8-node system and the 24-node system demonstrate the effectiveness of the proposed model and algorithm. Compared to the uncoordinated model, the proposed model saves by 11% of the total cost for the 24-node test system. Also, the computational performance of the Benders decomposition algorithm surpasses that of the basic algorithm by more than 24.6% in the two test systems.

There is a large growth in hardware and software systems capable of producing vast amounts of image and video data. These systems are rich sources of continuous image and video streams. This motivates researchers to build scalable computer vision systems that utilize data-streaming concepts for processing of visual data streams. However, several challenges exist in building large-scale computer vision systems. For example, computer vision algorithms have different accuracy and speed profiles depending on the content, type, and speed of incoming data. Also, it is not clear how to adaptively tune these algorithms in large-scale systems. These challenges exist because we lack formal frameworks for building and optimizing large-scale visual processing. This paper presents formal methods and algorithms that aim to overcome these challenges and improve building and optimizing large-scale computer vision systems. We describe a formal algebra framework for the mathematical description of computer vision pipelines for processing image and video streams. The algebra naturally describes feedback control and provides a formal and abstract method for optimizing computer vision pipelines. We then show that a general optimizer can be used with the feedback-control mechanisms of our stream algebra to provide a common online parameter optimization method for computer vision pipelines.

Water pipelines are one of the most vital structures to convey fresh water for consumption or irrigation over long distances. However, the major problem of water transportation pipeline is leak which can cause water resources loss, possible human injuries, and environmental pollution problems. For that, these pipelines must be carefully and real-time monitored. In this work, we describe a comprehensive framework for existing monitoring water pipeline techniques based on both wired and wireless networks. Furthermore, we elaborate a comparative overview in terms of keys parameters touching monitoring models. After the evaluation of all the existing pipeline monitoring methods, it is much more evident that techniques based on wireless sensors networks have variety and are the best selection for water pipeline monitoring purposes. This study is a guideline to select which model is the most suitable to design a new pipeline monitoring scheme.

Due to the distributed structure of electric power networks (EPNs) and district heating networks (DHNs), distributed dispatch is favorable for coordination in an integrated electricity and heat system (IEHS). However, the feasibility issue of the dispatch solution remains unresolved. Without making this clear, critical threats may be posed to system operation, and there cannot be security guarantees. In this article, we propose a distributed method for real-time IEHS dispatch where feasibility is strictly ensured in each iteration. First, a network reduction method of DHNs retaining the temperature quasi-dynamics is derived. Based on that, a novel feasibility cut (FC) generation method is devised, which strictly maintains system security during the iterative process. Finally, modified Benders decomposition with guaranteed feasibility (MBD-GF) is proposed to tackle distributed IEHS dispatch. The case studies of two IEHSs validate the effectiveness and efficiency of the proposed method.

Hydrogen is gaining attention as a promising future energy source. It has the potential to be a clean alternative to fossil fuels, particularly if it is produced using renewable resources. Two key concerns will be addressed by hydrogen energy, viz, reduce reliance on fossil fuels, Cut down on pollutants and greenhouse gas emissions. This paper discusses various production methodologies, as well as challenges, consequences, and control measures, as well as methods for storing and transporting hydrogen, the role of density in storage, the use of existing pipelines and mapping for transportation, and the transition to cryogenic tanks and embrittlement resistant materials in the near future. India has a favourable geographical location and vast reserves for renewable energy which makes it a suitable candidate to emerge as a green hydrogen exporter by 2030. It also discusses how India can use its resources effectively, implement policies and measures and seek international collaboration for R&D in production technology and storage capabilities and materials in order to reduce Green House Gas (GHG) emissions, cost of production, replace fossil fuels with clean energy fuels and achieve its ambitious target of decarbonising by 2050.

Seismic events and aftershocks can cause devastating impacts on overground and underground energy system infrastructure. This article designs a novel approach to restore energy system functionality in response to seismic stresses by using coordinated dynamic system reconfiguration and operation. First, the impact of seismic stresses on system infrastructure is evaluated, considering critical system components, including electricity lines, substations, gas pipes, and operable units including combined heat pump (CHP), electrolyser, soft open point (SOP), energy storage units. Load curtailment is assumed to be caused by system branch overloading, reversed gas flows, and isolated branches. The objective of the proposed operation method is to maximize overall system restoration capability. Responding to seismic stresses and aftershocks, storage, SOPs and coupling components are dynamically operated to restore both gas and electricity supply thus reducing losses. A service index is quantified by considering both load curtailment and recovery time. A representative integrated electricity and gas system is employed to demonstrate the effectiveness of the proposed method. Results illustrate that the adverse impact of seismic stresses can be effectively reduced with the proposed model. This work provides system operators a powerful tool to restore the functionality of energy systems under seismic events, helping maintain supply security.

Natural gas pipelines are the most important networks which have to be dealt with care since methane gas have poisonous characteristics. Such pipelines are allowed to be laid with some serious restrictions and regulations. Gas Authority Of India Limited (GAIL) has proposed to lay pipelines across India for the transportation of methane gas. This project work has been suspended because of the protests set in by the owners of the farmland, which would affect the fertility of the land and crop production. So re-routing or the suggestion of alternate routes becomes essential to complete the proposed pipeline network. Hence, the farmers of these areas demanded the route to be laid along the national highways and not across their agricultural lands. This study aims at proposing an alternate route for the pipeline network using GIS for the transmission of NG. Several Land Use/Land Cover (LULC) features are considered and a buffer zone is created amongst these features and avoided in order to identify a new route for the transmission of NG. In this study, three new routes have been proposed and among the three routes, one optimal route, route 3 connecting covering a distance of 35 km is selected based on the proximity, availability and convenience for the transmission of the NG via pipelines without causing any hindrance to the environment. The other two routes, route 1 and route 2 are found to be unsuitable due to the fact that lengths are 81 and 52 km and may prove to be inefficient. Having known the optimal route, the materials for the construction, laying, and maintenance of the pipelines may be suggested such as high carbon steel in order to prevent leakage, and withstand the pressure within and outside the pipeline and protected with appropriate coating materials.

Corrosion phenomena are usually difficult to recognize without a deep knowledge of the electrochemical properties of materials, this creates difficulties in the analysis of corrosion in practice, control measures are developed through image processing to solve these problems. This article describes a methodology for the analysis of the inner surface of steam pipelines by means of digital image processing (DIP). A classification algorithm is presented that is able to detect four levels of corrosion on the inner surface of steam pipelines. Gray-level co-occurrence matrix GLCM, which analyzes all the attributes of each image texture was employed. The performance of the classification algorithm was evaluated by using a confusion matrix, which showed very reliable classification results by comparing with experimental tests.

Deep learning is currently used in the perception pipeline of autonomous systems, such as when estimating the system state from camera and LiDAR measurements. While this practice is typical, hard guarantees on the worst-case behavior of the closed-loop system are rare. In this letter, however, we leverage recent results on neural network approximation, combined with classical input-to-state stability (ISS) properties, and show how to design deep neural networks for state estimation that guarantee the safety and stability of the resulting closed-loop system.

During the pipeline transportation of backfill slurry, flow velocity has direct effects on the transport properties and then influences pipe blockage and wear. In this paper, a novel flow loop- horizontal electrical resistivity (HER) test system and Eulerian model were used to investigate the flow behavior and HER characteristic of cemented coal gangue-fly ash backfill (CGFB) slurry under different flow velocity. The results showed that: 1) The pressure drop of CGFB slurry increased linearly with flow rate. 2) The increase in flow rate significantly reduced coarse aggregate contents near the bottom of the pipeline. 3) As the flow rate increases, the fluctuation of measured HER with time increased, the HER differences decreased, and the ranges of the high and low resistance area at the bottom and top of the pipeline, respectively, kept decreasing. The results can provide guidance for HER monitoring of flow state of CGFB slurry in pipeline.

This paper presents the application of hydraulic modelling for the prediction of pipe bursts in a water distribution system (WDS). As a case study, more than 1430 km of pipelines within 29 water pressure zones (PZs) in Bendigo and Castlemaine WDSs, located in Victoria, Australia, were modelled and calibrated using an InfoWorks WS hydraulic model. A pressure index (PI) was defined to quantify the intensity of high pressures in the WDS pipes. The PIs for all the PZs were calculated using hydraulic model simulations. Ten years of actual pipe burst data (2007–2017) in the studied WDSs were used in the research. A burst index (BI) was used to measure the burst rate for the length of pipe network within the study area. A mathematical relationship between PI and BI was proposed based on the first eight years (2007–2015) of burst data and the results of the hydraulic modelling in the same period. The number of pipe bursts for the last two years (2016–2017) in each PZ was then predicted according to the proposed mathematical relationship. The predicted number of bursts fitted well with the actual recorded pipe burst data, with a coefficient of determination of 0.98.

This study aims to combine the smart pigs as a non-destructive test (NDT) inspection technique with software developed for the assessment of pipeline corrosion defects to ensure fitness for the surface. The software uses decision support systems, connected through the correlated linkage technique, which is coded using Microsoft Access and Visual C#. This software measures general internal pipeline corrosion forms to identify locations with potential corrosion features and predict corrosion conditions in the future. Computer-aided corrosion management program (CACM) examined maximum corroded depth of internal corrosion, maximum allowable axial corrosion defect length, failure pressure, the corrosion rate, and the remaining pipeline life. This work introduces a wide-ranging review of computer-aided corrosion management programs. The proposed method of assisting and detecting corrosion internal defects and defects data should be available. This software is easy to use without complicated analysis. It helps to reduce unplanned shutdowns in the oil and gas production industry.

One of the big challenges in transporting heavy oils through the pipelines is the high pressure drop due to the effect of viscosity and shear-thinning fluid behaviour of oil. Core annular technique has been applied in industry for transporting heavy oil via pipelines to considerably reduce pressure drop. Several studies have been conducted related to oil-water core annular flow (CAF) within pipelines. However, limited works are found in cases of CAF in widely used T and Y-shaped junction pipes which have important roles for dividing or combining the flow. In this study, a computational fluid dynamic (CFD) approach was applied to simulate the high viscous oil-water CAF through T and Y-shaped junction pipe configurations. The 2k factorial statistical experimental design was applied to investigate the effect of pipe diameter and junction angle on the flow performance through T and Y-shaped junction pipes. Eight cases were run with different junction pipe configuration combinations. By applying the factorial designs method, all possible combinations of factors were able to be investigated with lowest numbers of simulation run. The selected response variables of the flow system were the average values of oil hold up and pressure gradient as well as the standard deviation of pressure from inlet to outlets of pipes. It was observed that the stability of CAF was not achieved at the downstream region. The flow transformed into stratified and slug flow. The most attractive design was measured by the small average of pressure gradient and standard deviation of pressure but with a high value of oil holdup. The T-shaped pipe with smaller inlet diameter combined with larger outlet diameter showed the most considerable effect for a better flow performance.

The operation of dehydration is very important in the process of gas transportation. This study aims to evaluate the application feasibility of CO2 dehydration using triethylene glycol, which is also called TEG for short. Aspen plus software was used to simulate the dehydration process system of CO2 gas transportation using TEG dehydration. Parameter analysis and process improvement were carried out for the simulation of dehydration process. At first, a sensitivity analysis was conducted to analyze and optimize operating conditions of conventional CO2-TEG dehydration process system. Subsequently, a recycle unit was introduced into the conventional CO2-TEG dehydration process system, it can be found that the improved process system with the recycle unit has a higher CO2 recovery rate which was about 9.8% than the conventional one. Moreover, the improved process system showed excellent operation stability through the comparison of simulation results of several processes with various water contents in their feed gases. Although the energy consumption is increased by about 2%, the improved process was economically and technically feasible for the long-term availability of CO2 pipeline transportation. The simulated results showed that the improved CO2-TEG process system has promising application prospects in CO2 dehydration of CO2 gas transportation with high stability.

Petroleum industry is the mainstay of the economical enlargement of many nations. Most of the market prices of all commodities are built on the cost of wide-range of petroleum products. Prompt identifying and alleviating challenges facing this sector can reduce costs and enhance productivity and economic growths. Asphaltene deposition, a crucial operational challenge in some petroleum production facilities is one of the key challenges facing petroleum sector universally. Asphaltene clogs pipelines as cholesterols block blood vessels; this has made asphaltene deposition management a principal factor to consider before establishing any petroleum producing company. Therefore, in order to guarantee urbane and uninterrupted movement of petroleum to the consumers, it is vital for the field Operators, Engineers and Chemist to be pipeline deposition conscious as lines would suffer degradation owing to asphaltene precipitation, aggregation and eventual deposition. There are documented literatures on asphaltene, but almost new information is disseminated in literature or occurs in the brains of experts comparable to silent knowledge. The mission of this review paper is to publicize the information and close some knowledge-breaks. This particular article provides an inclusive appraisal of asphaltene interaction, precipitation, deposition, challenges and management stratagems. Besides, it provides a conclusion and prised recommendations.

Today, the issues related to solving the problem of finding an effective distribution of oil flows through the system of oil pipelines in order to reduce the total energy consumption are relevant. The solution to this problem is connected with selection of rational pumping modes for various technological sections of oil pipelines using modern methods of mathematical programming or new techniques for improving the energy and transport characteristics of oil. Reducing energy consumption during pumping of crude through oil trunk pipelines can be achieved by various methods. Numerous investigations in this direction are mainly carried out to save energy on separate single-line pipelines. However, due to the development of the network of trunk oil pipelines in the world over the past decades, the issues of energy efficient management of oil flows throughout the entire oil pipeline system of oil and gas enterprises become urgent. This paper analyses parameters for pipeline transport of high-viscosity and heavy oils. The article proposes a method for assessing the rheological properties of oil for further planning of pumping taking into account the preservation of oil quality and an increase in energy and transport characteristics. The proposed solutions and tasks for predicting changes in the viscosity-temperature characteristics of the flow for blends of different oil types are especially relevant in the current conditions of an increase in the share of oil production with complex rheological characteristics. Results of the presented investigations may be used for planning the measures of efficient transportation of high-viscosity and heavy oils.

Exploitation of oil and gas in the Arctic area is expected to expand in the coming years. These hydrocarbons are transferred through subsea pipelines from offshore to onshore; however, the marine pipelines are threatened by traveling icebergs where the seabed may be gouged by the moving masses in warmer months. Subsea trenching and backfilling are usually utilized to bury the subsea pipelines for physical protection against the ice scouring. Regarding the stress-based design methods, deformations and forces are generally the controlling design factors for the subsea assets. In this study, the subgouge clay displacements and the reaction forces were simulated using a non-tuned self-adaptive machine learning (ML) entitled “self-adaptive extreme learning machine” (SAELM). Initially, fifteen SAELM models were defined by means of the parameters affecting the ice-scoured features. Subsequently, 70% and 30% of the constructed dataset were respectively applied to train and test the machine learning models. After that, the optimum number of hidden layer neurons and the best activation function were selected for the SAELM network. By conducting a comprehensive sensitivity analysis, the premium SAELM models and the most influencing input parameters in estimation of the subgouge clay characteristics were introduced. Regarding the performed analyses, the horizontal load factor and the gouge depth ratio were identified as the most influential parameters to model the reaction forces, whereas the soil depth had a significant impact for simulation of the ice-induced clay deformations. Finally, a set of SAELM-based equations were presented to estimate the subgouge clay parameters.

In order to achieve the purpose of efficient, safe and reliable operation, it has become the development trend and direction to build an intelligent oilfield transportation pipeline network with big data analysis, comprehensive situation awareness and automatic control and management capabilities in the future. In this paper, Supervisory Control and Data Acquisition (SCADA) system was used to collect the production and operation data of an oilfield in Western China. The thermal and hydraulic models have been corrected based on these collected data. Then, the influence factors of thermal characteristics of several categories of pipelines were analyzed, and the critical temperature gradient of various categories of pipelines was obtained by interpolating fitting method. Furthermore, the optimal operation conditions of the transportation pipelines were obtained, and the universal relationship of pipeline operation parameters was established as well. The results indicated that the corrected models can predict the temperature drop and pressure drop of multiphase mixture transportation pipeline well, liquid flow rate, water cut and initial temperature do have obvious influences on pipeline operation parameters, and the universal relationship of pipeline operation parameters can effectively guide the production practice of oilfield. However, the operation parameters optimization universal relationship cannot meet the requirements of personalized control of all pipelines. Therefore, this paper proposed an intelligent control strategy for multiphase mixture transportation pipeline in oilfield. This case study is of great significance to guide the optimization of operation parameters and ensure high efficiency and safe operation of multiphase mixture transportation pipelines in oilfield.

In pipeline management, the advent of Internet of Things (IoT) technology enables effective pipeline maintenance with the deployment of embedded sensing units, the basic requirement of which is to make accurate and real-time measurement. Practically, it is impossible to directly measure the internal temperature of the pipe by laying conventional sensors in the oil. This work proposed a novel indirect testing framework to perform real-time and accurate temperature monitoring in oil pipes by solving the inverse problem based on pipe wall's temperature field. The contribution includes three aspects: 1) A rigorous theory was developed to validate the principle of equivalence for heat transfer in pipes. A single-layer boundary-equivalent diffusion model with effective thermal parameters exhibited excellent performance in predicting temperature change in a multilayer structure by simulation. 2) We established a microprocessor-enabled predictive temperature measurement modality to precisely determine the time-dependent temperature evolution in a multilayer composite pipe based on explicit analytical expressions. 3) The correctness and applicability of the modality were verified experimentally on a real oil pipe and a commercial device. The results show that precision of the predictive measurement modality reaches 0.3 K and 0.002 K/s for stable and linear-rising internal fluid temperature, respectively.

The use of information and measurement systems in the oil and gas industry helps to improve the commercial gas consumption metering supplied to consumers in order to ensure mutual settlements with consumers. Gas distribution stations (GDS) are one of the main objects of main gas pipelines. Gas distribution stations are designed to perform the following operations: receiving gas from the main gas pipeline; gas purification from mechanical impurities; decrease in pressure to a predetermined value; automatic maintenance of pressure at a given level; distribution of gas to consumers; measuring the amount of gas. In addition, secondary odorization of gas is carried out at the gas distribution station. Regardless of the throughput, the number of consumers, the inlet and outlet pressure, the nature of the load change (gas consumption), the process flow diagram of the gas distribution station consists of the following main units: the connection diagram of the gas distribution station to the gas pipelines, gas purification, pressure regulation, gas flow measurement and instrumentation devices (instrumentation), gas odorization. The unit for measuring the flow rate and amount of natural gas (hereinafter referred to as the gas metering unit) is designed to measure, record the measurement results and calculate the volume of gas reduced to standard conditions, as well as, if necessary, determine its quality indicators, including component composition, density, humidity, specific heat of combustion. Various types of flow meters were analyzed and an ultrasonic flow meter was selected.

Transition to low-carbon energy is on the political agenda of most developed states, supported by investment in green technologies and legislative initiatives. The EU is actively elaborating the 20...

Natural gas produced in the Russian Federation is transported through main gas pipelines united into the Unified Gas Supply System of the Russian Federation. The system of main gas pipelines - the most important link in the unified gas supply system – is a large, complex and continuously developing technological system. Maximum internal pressure is one of the key characteristics for any pipeline. This indicator helps to set the limit for the capacity of the pipeline (maximum volume of pumped gas per unit of time), its level of reliability, as well as the level of hazard and potential risk (the higher the pressure inside the pipeline, the more potential threat it carries). In order to increase the reliability of determining the pressure in the gas pipeline, it is proposed to perform noise suppression using a wavelet transform in the work. Noise reduction in the wavelet transform is carried out due to the fact that the signal is decomposed into approximating and detailing coefficients. After removing the detailing component, the decomposition is restored and the output is a slightly distorted signal. Thus, when you remove an insignificant part of the original signal, the graphs of the change in values become more visual. This paper compares the efficiency of wavelet based thresholding techniques in the presence of noise for various wavelet family. For comparison, the trend processing was performed by two types of wavelets recommended for noise reduction - Symlet and Daubechies wavelets.

Internal pipeline corrosion due to carbon dioxide (CO2) is a major challenge facing the oil and gas industry. The objective of this study was to investigate the corrosion behavior of aluminum (Al)-based alloys as sacrificial coatings to protect pipelines in a CO2-saturated aqueous electrolyte (3.5 wt% NaCl) at 4 bar CO2 partial pressure (3 bar) and 40°C. The corrosion resistance of Al-based alloys and thermal spray coatings was evaluated in an electrochemical reaction autoclave using electrochemical methods (potentiodynamic polarization, linear polarization resistance, and electrochemical impedance spectroscopy). Post-corrosion surface characterization was performed by scanning electron microscopy equipped with energy-dispersive x-ray spectroscopy. The obtained data show Al-based alloys demonstrated promising protection against CO2 corrosion with no breakaway degradation issues.

Condition assessment is an essential process to comprehend the condition of the water pipelines and facilitate the maintenance as well as the renewal plans. Nowadays, varied in-pipe inspection platforms equipped with closed-circuit cameras are employed to capture the internal condition of the water pipelines. However, the automated platform often faces the challenge to negotiate with the installed valves during the inspection. To ensure continuous inspection, the platform needs identify the valve automatically and activate the control mechanism to pass through it. Thus, the valves need to be detected to facilitate the negotiation and ensure that the control mechanism can take an action in time. This paper focuses on real-time valve detection using Jetson TX2™ and a lightweight algorithm, namely YOLOv3-tiny. The performance of the implementation is compared with state-of-the-art real-time detection models. The experimental results demonstrate that YOLOv3-tiny has a high detection speed in frame per second for valve detection and outperforms the state-of-the-art real-time algorithms. Hence, the deployment YOLOv3-tiny into the embedded system will aid the automated platform to accomplish the uninterrupted inspection and enhance the capability for the condition assessment of the water pipelines.

Rapid urbanization poses challenges to meet the increased water supply demands. Apart from the quantity, the distributed water quality often fails to meet the permissible level. This study aimed to conduct a citywide spatio-temporal variation of water quality parameters. Water sampling points were selected by applying the Analytical Hierarchical Process (AHP) technique using ArcGIS considering pipe leakage, source water quality, pipe age, and pipe materials. The Chattogram city comprises 397 km of pipes; pipe material distribution shows 71.28% PVC, 20.94% asbestos, 5.16% mild steel, 2.17% ductile, and 0.45% cast iron. The citywide pipe network was established in 1963; 20.87% of pipes aged over 30 years, 15.07% 20–30 years, 26.38% 10–20 years, and 37.68% pipelines are relatively new, i.e., within 0–10 years. Eight water quality parameters, i.e., pH, temperature, turbidity, biochemical oxygen demand after five days (BOD5), total coliform, fecal coliform, chloride, and residual chlorine, were collected from the secondary source cross-checked by field survey. Computation of the Water Quality Index (WQI) was interpolated using Inverse Distance Weighted (IDW) method to generate a WQI map. Thus, this study could be a basis to improve the treatment system and proper distribution network maintenance. HIGHLIGHTS Spatio-temporal variation of water quality parameters in the citywide distribution system.; Water sampling locations were identified using the Analytical Hierarchical Process (AHP).; Inverse Distance Weighted (IDW) method to generate a Water Quality Index (WQI) map.

Due to the energy linkage between electricity and gas networks, assessing the voltage security of the electricity system without considering the practical constraints of both systems, may lead to unrealistic values of loading margins (LM). This work proposes a model for investigating the impact of gas networks on the voltage security of electric transmission networks. The overall objective is to maximize the LM of the electricity network while satisfying all relevant constraints in both gas and electricity networks such as hourly line pack of gas pipelines, reactive power capability limits of generators, and complementarity constraints representing the generators active/reactive power limits based on the capability curves, power flow equations at both current operation, and security limit points. Three (small, medium, and large) case studies are presented as the applications of the proposed model for LM maximization in power systems that are highly coupled with gas networks. The obtained results corroborate the impact of both gas and electrical networks operation constraints such as voltage and reactive power limits, nodal gas pressure limits, gas network loading as well as the line pack phenomenon of gas pipelines on the LM of power systems. Science Foundation Ireland

Superconducting DC energy pipeline combines long-distanceelectricity and liquified natural gas transmission, which could improve the overall efficiency of the pipeline system and save construction space. The characteristics of superconducting DC energy pipelines with bipolar coaxial, double-core and partitioned structural layouts are analyzed and compared in this paper, which are significant for the selection of pipeline structure. The voltage levels are set as ±10 kV/±100 kV for electricity distribution and transmission applications, and the rated current is set as 1 kA. The simulation models of energy pipelines with each structural layout are established in order to evaluate the respective operation characteristics. The operation conditions of the steady state, the quench transient process under a short-circuited fault and the quench recovery transient process afterwards are analyzed in order to evaluate the stability and safety of the pipeline system. The comparison results illustrate the distinguishing features of each structural layout from various aspects. It is found that the double-core structure and the partitioned structure are the most suitable options at the voltage levels of ±10 kV and ±100 kV respectively.

The present study is focused on proposing, implementing, and optimizing machine learning based pipelines for estimating the pressure drop in evaporating R134a flow passing through micro-fin horizontal tubes. Accordingly, an experimental activity is first conducted, in which the pressure drop of the flow at various operating conditions is measured. Physical models that are available in the literature are then implemented and the corresponding accuracy, while being applied to the obtained dataset, is determined. Machine learning based pipelines, with dimensionless parameters provided as features and two-phase flow multipliers as targets, are then developed. In the next step, a feature selection procedure is performed and an optimization process is then conducted to find the algorithms and the corresponding hyper-parameters, using which results in the highest possible accuracy. The optimal pipeline is demonstrated to be the one in which the liquid only two-phase multiplier is chosen as the target and is provided with only 5 dimensionless parameters as selected input features. Employing the latter pipeline leads to a mean absolute relative deviation (MARD) of 6.27 % on the validation set and 6.41 % on the test set, which is notably lower than the one achieved using the most promising physical model (MARD of 18.74 % on the validation set and 18.08 % on the test set). Furthermore, as the dataset and the obtained optimal pipeline will be made publicly accessible, the proposed methodology also offers higher ease of use and reproducibility.

The composite energy pipeline (CEP) is an emerging multi-energy flow transmission technology that combines superconducting cable and liquefied natural gas (LNG) pipeline. The CEP carries a large amount of high-grade cold energy during operation. However, the existing regional integrated energy system(RIES) does not have a corresponding recycling plan for the input cold energy. We propose a new type of energy system coupled by a CEP and RIES, which is based on the research of the current situation of the cold, heat, electricity and gas supply modes of the RIES. On this basis, in order to make full use of the cold energy carried by the CEP, this paper also proposes a cold energy recovery method combined with the RIES, and the cold energy cascade utilization based on exergy analysis method. We improve the energy efficiency of the system through cold recovery and the unified optimized dispatch of the cold, heat, electricity and gas supply systems, finally realize the efficient use of energy by the system and improve the profitability of the system.

Natural gas has been recognized as a promising energy supply for modern society due to its relatively less air pollution in consumption, while pipeline transportation is preferred especially for long-distance transmissions. A simplified pipeline control scenario is proposed in this paper to deeply accelerate the management and decision process in pipeline dispatch, in which a direct relevance between compressor operations and the inlet flux at certain stations is established as the main dispatch logic. A deep neural network is designed with specific input and output features for this scenario and the hyper-parameters are carefully tuned for a better adaptability of this problem. The realistic operation data of two pipelines have been obtained and prepared for learning and testing. The proposed algorithm with the optimized network structure is proved to be effective and reliable in predicting the pipeline operation status, under both the normal operation conditions and abnormal situations. The successful definition of ”ghost compressors” make this algorithm to be the first self-adaptive deep learning algorithm to assist natural gas pipeline intelligent control.

Superconducting energy pipelines (SEPs) which can simultaneously transmit liquefied natural gas (LNG) and electricity over long distances are proposed these years. SEPs combining LNG pipelines and high-temperature superconducting (HTS) cables together have higher transmission efficiency. The stable and reliable operation of SEPs during short-circuit faults is important. The HTS cable generating heat may cause the liquid protective medium (LPM) inside the copper former of the cable to boil, endangering the safety of SEP during short-circuit faults. The objective of this paper is to obtain the influence of short-circuit faults on the temperature of HTS cables in SEPs. Three different structures of copper former in HTS cables of a ±100 kV/1 kA SEP are investigated including copper rod and two different copper tubes. One inside of the copper tube is vacuum calling internal vacuum copper tube (IVCT) and the other one is LPM calling internal LPM copper tube (ILCT). Simulation results show that the ILCT has the best properties due to its lowest temperature during short-circuit faults. When a fault current of 20 kA transports in the HTS cable for 100 ms, the cross-sectional area of the copper former should be larger than 187.46 mm2 to ensure safety.

The erosion-induced failure of elbows commonly occurs in the pipeline transport industry. In this study, a computational fluid dynamics (CFD) method was adopted to study the erosion of air-solid elbows. The results show that when the working conditions in the air-solid elbow are changed, there are three types of erosion morphologies: the concentrated-exit type, transition type, and “V” type; the erosion morphology is mainly related to the trajectory of the particles. An arc-shaped diversion erosion-inhibiting plate structure (ASDEIPS) is proposed to reduce the erosion rate for elbows with transition type or “V” type erosion morphologies by up to 41%. Another advantage of the ASDEIPS is its ability to improve the flow field of the elbow under any working condition and to reduce the pressure drop, turbulent kinetic energy, and turbulence dissipation rate of the elbow.

A machine-learning model was used to predict the performance of four crystallographic model-building pipelines (ARP/wARP, Buccaneer, Phenix AutoBuild and SHELXE) and their combinations., Proteins are macromolecules that perform essential biological functions which depend on their three-dimensional structure. Determining this structure involves complex laboratory and computational work. For the computational work, multiple software pipelines have been developed to build models of the protein structure from crystallographic data. Each of these pipelines performs differently depending on the characteristics of the electron-density map received as input. Identifying the best pipeline to use for a protein structure is difficult, as the pipeline performance differs significantly from one protein structure to another. As such, researchers often select pipelines that do not produce the best possible protein models from the available data. Here, a software tool is introduced which predicts key quality measures of the protein structures that a range of pipelines would generate if supplied with a given crystallographic data set. These measures are crystallographic quality-of-fit indicators based on included and withheld observations, and structure completeness. Extensive experiments carried out using over 2500 data sets show that the tool yields accurate predictions for both experimental phasing data sets (at resolutions between 1.2 and 4.0 Å) and molecular-replacement data sets (at resolutions between 1.0 and 3.5 Å). The tool can therefore provide a recommendation to the user concerning the pipelines that should be run in order to proceed most efficiently to a depositable model.

Numerous pipeline failures have occurred due to the drastic increase in oil and gas product distribution pipelines over the last three decades. Corrosion is a significant factor in the failure of offshore gas pipelines. It is necessary to maintain the integrity of the offshore transmission system, one of which is the riser, for the product to be adequately distributed. The purpose of this study is to ensure the riser's reliability by conducting a risk assessment of the probability and consequences of equipment failure, mitigating the impact of failure risk, and developing a more optimal inspection plan. The API 5L Grade X60 gas riser pipe is the subject of this study. The quantitative Risk-Based Inspection (RBI) technique was used in 2016 following the API 581 standard. This quantitative approach is founded on a numerical value model constructed using validated operational data and inspection results. The risk assessment indicates that the gas riser pipelines will have a high-risk level (5D) for the splash zone segment and a medium-risk level (2D) for the above-and below-water segments. The recommended inspection plan for the gas riser pipeline is one year after the risk-based inspection assessment.

One of the key strategic directions of the long-term program for the development of the oil and gas industry is the development of the resource base, including through the acquisition of new licenses at auctions and tenders held by Russian government agencies. To make a prompt decision to purchase a license area, it is necessary to carry out an objective technical and economic assessment. Official resources contain a minimum of initial data for making a decision, which led to the need to create a unified information system for assessing subsoil plots, accumulating information on new unallocated plots. The article gives valuable information about the main capabilities of the unique information resource, which was developed by specialists of Tyumen Petroleum Scientific Center LLC. This information resource is intended to provide the Company's specialists with a tool for rapid assessment of subsurface areas based on complex parameters of geology, development, infrastructure and economics in order to make a decision on the feasibility of acquiring an asset.

Large-scale carbon capture, utilization, and storage (CCUS) requires development of critical infrastructure to connect capture locations to geological storage sites. Here, we investigate what government policies would be required to make the development of CO2 pipelines and large-scale CCUS in the power sector economically viable. We focus on the transition from conventional coal to non-CO2-emitting natural gas-fired Allam-cycle power with CCUS and study a system in which 156 Allam-cycle power generators representing 100 GW of capacity send their captured CO2 emissions to three geological storage locations in the central United States through 7500 miles of new pipeline. Enabling policies for this system include low-interest government loans of approximately $20 billion for pipeline construction and an extended 20-year Section 45Q tax credit, or similar longer-term carbon price incentive. Additional policy support will be needed to enable initial construction of pipelines and early-mover power generators, such as cost-sharing, governments assuming future demand risk, or increased subsidies to early movers. The proposed system will provide reliable, dispatchable, flexible zero-emission power generation, complementing the intermittent generation by renewables in a decarbonized U.S. power sector. The proposed pipeline network could also connect into future regional infrastructure networks and facilitate large-scale carbon management.

Groups of spatially distributed wells interconnected by sections of pipelines (a common collector) supplying water to a clean water reservoir are used in water supply systems in order to provide drinking water to settlements. A group of wells together with a clean water reservoir is called a water intake facility of the water supply system. Further, water is supplied from the clean water reservoir to consumers in settlements by means of second stage pumping stations. In terms of each well the operating mode of the submersible pumping unit, i.e. the position of its operating point at a fixed moment in time, depends on the actual values ​​of many internal and external factors: dynamic water level in the well; the actual characteristics of the pumping unit; the position of the final control elements (drive speed, opening degree of the regulating valve); hydraulic resistances of pipeline sections of the water intake facility; the water level in the clean water reservoir; the number and operating modes of pumping units in other wells of the water intake facility. The paper describes a deterministic model of a water intake well. At the same time, the processes of water consumption are random processes, since they depend on many uncontrolled and unmanageable factors. The parameters of the technological equipment of the water intake facility are also random values, since they are estimated from experimental data of finite length. Therefore, a stochastic model of a water intake well is also proposed. A model of a quasi-stationary operation mode of a water intake facility, which is used to optimize the operation of a group of artesian wells operating on a common collector, is considered.

Oil and gas operators have been looking for a more reliable and cost-effective material for their pipelines to substitute steel in pipes and connectors. Reinforced thermoplastic pipes are a popular choice for this purpose, but they are still mainly using steel connectors, which have limited lifetimes due to corrosion. For this reason, efforts are being made to design a non-metallic electrofusion coupler to connect these. This article looks at the current state of the art in electrofusion welding of standard polyethylene pipes and aims to find possibilities of applying this technology to reinforced thermoplastic pipes.

Water supply pipelines and canals may possess a bulky amount of excess hydro energy. The renewable and clean feature of hydropower production with the lower cost, with shorter time of construction, and with contribution of small private sectors for investment on local projects are of major concerns. However, high expenses associated with the use of turbines in small hydropower plants have encouraged the water industry toward the use of pump as turbine (PAT). The main problem is that the pumps manufacturers do not supply the characteristics curves for using pumps in PAT mode. There is a great need for predicting the performance of PATs. The main aim of the present study was to investigate the performance of a single-stage and a multi-stage pump in turbine operating mode. An intensive field study was carried out, and the performance of the PATs was formulated and compared with the relationships presented by the preceding studies. The results indicated that the multi-stage pumps exhibit a low efficiency in turbine mode, mainly due to the high rate of leakage and non-uniform pressure distribution over the blades. Alternatively, the single-stage pumps in series operation are more effective. The top-point efficiency of PATs is higher than that of pumping mode; hence, the maximum efficiency of PATs is achieved for higher discharges and pressures. The results indicate that the existing experimental relationships are not yet capable of predicting a precise performance of different PATs. Therefore, manufacturers are to provide users with the characteristic curves of those pumps that are capable of being operating as turbines.

Analysis of buried pipelines under Permanent Ground Deformation (PGD) shows that the movement of earth faults near these lines causes damage to them. A review of past earthquake experience also shows that the defect of the pipe can be vulnerable to fault displacement. One of the most important mechanical defects of pipelines is wall dents. Because the location of this damage in the pipe wall affects the life of the pipes and due to the inevitable intersection of pipelines with faults and also reviewing the relevant technical literature indicates that dented pipelines at fault crossing sites have not been investigated. It is necessary to investigate and analyze dented pipelines at the intersection with faults and determine the most critical state of this damage in the pipe at the crossing of faults. Accordingly, during the present study, four numerical models were created in ABAQUS and the effect of the dent position in the pipe wall in each hour of pipe cross section (3, 6, 9 and 12 o'clock) was examined in the fault movement. Finally, the dent at the 3 o'clock position of the cross section of the pipe was selected as the most critical situation in the crossing strike-slip fault.