A framework of smart pipeline system and its application on multiproduct pipeline leakage handling

Energy 188 (2019) 116031

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Energy journal homepage: www.elsevier.com/locate/energy

A framework of smart pipeline system and its application on multiproduct pipeline leakage handling Guoxi He a, *, Yansong Li b, Yuanjie Huang a, Liying Sun a, Kexi Liao a a b

Petroleum Engineering School, Southwest Petroleum University, 610500, Chengdu, China China University of Petroleum-Beijing, 102249, Beijing, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 May 2019 Received in revised form 23 August 2019 Accepted 28 August 2019 Available online 3 September 2019

The management system is essential to ensure the smooth operation of the long-distance pipeline. Here, a framework of a smart pipeline system and its application on multiproduct pipeline network are proposed where a pipeline monitoring and accidental leak handling system based on real-time Big Data and Distributed Computing for integrity management is introduced. A novel construction of data-chain and module-chain in the management of pipeline integrity is given. New computational modules for locating a leak, calculating leakage volume and executing shutdown procedure are proposed for emergency treatment. A new optimal model aiming at minimal ALV (accumulative leakage volume) is established to obtain a pipeline shutdown scheme. Several experiments relying on multiproduct pipelines are implemented to test the effectiveness of the framework and evaluate the applicability of the modules. The results demonstrate that the estimations of the starting time, location, coefficient, and leakage volume could be fulfilled within the duration of a negative pressure wave going through the selected pipeline. Meanwhile, the pre-forming of the pipeline shutdown procedure as well as obtaining the ALV could be achieved. The outcomes could provide guidance for the construction of smart pipeline network, pipeline shutdown management, prediction of leakage influential range and subsequent incident investigations. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Smart pipeline system Integrity management Distributed computing Transient leaking Emergency handling

1. Introduction Pipeline transportation is the main way of oil and gas transportation at present, which plays an extremely important role in the development of the oil and gas industry [1,2]. Automatic detection, monitoring, controlling, and management are needed to be carried out during the process of transportation in a longdistance pipeline. The rapid development of digital pipeline construction makes it possible to use emerging technologies such as Big Data, Cloud Computing, IoT (Internet of Things) to ensure the safety of the pipeline. Those technologies are utilized to improve the system of data acquisition and storage capacity. In order to improve data processing capability, efficient real-time and distributed computing is also needed and thus Cloud Computing technology has been applied. The intelligent industrial control system also provides technical support for reasonable pipeline shutdown operation simultaneously. Therefore, to build a smart management system for the long-distance pipeline, we need to use

* Corresponding author. E-mail address: [email protected] (G. He). https://doi.org/10.1016/j.energy.2019.116031 0360-5442/© 2019 Elsevier Ltd. All rights reserved.

these technologies to develop intelligent application modules. Those modules are executed to calculate and analyze the demanded data, then to determine, operate and manage the pipeline events, and eventually to form a business chain. Previous scholars [3e8] have made preliminary explorations on digitalization, automation, and intellectualization of pipeline industry assessment and control system. Also, the digitalized and intelligent pipeline management system is being built in China and worldwide. In the technology framework of the IoT, based on Big Data and cloud storage technology, the pipeline integrity management platform can record and monitor the multitask operation of complex pipeline networks in real time. Once leaking occurs, the intelligent pipeline management system is expected to capture and process the leaking information at the earliest so that the pipeline shutdown operation can be taken immediately, where the current SCADA system is not able to handle. It is found that based on the current SCADA system, the data perception technology of IoT, Big Data and cloud storage technology are needed to improve data acquisition and storage capacity. For example, in the pipeline integrity management, intelligent application modules including leak detection warning, leak location

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G. He et al. / Energy 188 (2019) 116031

Nomenclature a ao,i A; Apipe AL Au Ad c C0 Cd dL dt D Di Dorifice e Ei f g hvf hpf H, Hp HL H0 Hair Hvapor DH k ko kLa l1 l2 L Lr LL m p pL

The velocity of the pressure wave, m/s The pressure wave of oil o in pipeline segment i, m/s The cross-sectional area of the pipeline, m2 The area of leaking orifice, m2 The amplitude of upstream pressure drop, Pa The amplitude of downstream pressure drop, Pa The specific heat capacity of the oil, J/(kg K) The coefficient of leaking orifice, C0 ¼ aCdAL, dimensionless The orifice's flow coefficient, 0.98e0.99, dimensionless The diameter of the branched pipe section, m The time step in the specific case, s The inner diameter of the pipeline, m The inner diameter of the pipeline segment i, m The diameter of the orifice, m The specific internal energy of the oil, J/kg The Young's modulus of pipeline segment i, Pa The frictional coefficient along the pipeline, dimensionless The gravitational acceleration, m/s2 The local friction of the valve, m The local friction of the branched leaked oil pipe section, m The pressure head in the pipeline, m The initial pressure head at leaking point, m The pressure head outside the leaking orifice, m The head of local air pressure, m The head of oil-saturated vapor pressure, m The change of the pressure head, m The kth time step, dimensionless The oil elastic modulus of batch o, Pa The quantity of time steps, dimensionless The distance between the upstream station and the leaking point, m The distance between the downstream station and the leaking point, m The total length of the pipeline, m The distance from the start to the leaking point, m The length of the branched leaked oil pipe section, m Flow regime index, dimensionless The pressure in the pipeline, Pa The vapor pressure of oil in the oil tank, Pa

identification, leak rate prediction, leak orifice characteristics prediction, shutdown scheme, cumulative leak measurement, and emergency information release. It needs to be pointed out that a smooth data chain is the most important logical guarantee that these application modules and the business chain can be integrated. The smart management system of the long-distance pipeline must guarantee the effective coordination and connection between the various technologies of pipeline business and data flow for the field management. Many scholars [9e15] have studied and developed one of the above application modules, but they have not considered whether the data link is smooth, that is, they do not consider the communication between modules and the time consumption of execution of the modules. There are more studies on leak detection warning and leak location identification [16e26] than researches on leak rate prediction, leak orifice characteristics prediction, leakage

Pu pd Dp L

Dp u Dp d q qP Q QL t Dt T v Dv x z zL

a ap di q l x r ro t c Subscript d i j k L o p, P u

The pressure at the upstream station, Pa The pressure at the downstream station, Pa The change of pressure in a pipeline at leaking point during Dt, Pa The change of pressure at the upstream station during Dt, Pa The change of pressure at the downstream station during Dt, Pa The heat flux of the oil, W/m2 The flowrate of leaked oil, m3/s The flowrate in the pipeline, m3/s The instantaneous leakage flowrate, m3/s Time, s Time difference, s The temperature of the oil in the pipeline, oC The average velocity of the oil in the pipeline, m/s The change in velocity, m/s The distance from the upstream station, m The elevation of the pipeline, m The relative elevation at the leaking point, m The flow contraction coefficient, 0.62e0.66, dimensionless The inflation coefficient of the oil, 1/oC The thickness of pipeline segment i, m The included angle between the leaking segment and horizontal direction, rad The Darcy friction coefficient, dimensionless The coefficient of resistance, depending on the structure, opening, and caliber, dimensionless The average density of the oil at the cross-sectional area, kg/m3 The oil density of batch o, kg/m3 Time, s The correction factor of the pipeline when calculating wave speed, dimensionless

Downstream Distance node Time node Numbering of time step Leak point Oil Pipeline Upstream

measurement [27,28], whereas few researchers are focused on the shutdown scheme, cumulative leakage measurement, and emergency information release. Further, there are few research integrating the data chain and module chain into the smart management system of the long-distance pipeline, and upgrade pipeline integrity management technology. Most previous studies [10,12e15] focused on methods of leak detection and localization of gas or liquid in the transportation pipeline, some out of which have been proven to be extensively applied, including negative pressure waves, ITA (inverse transient analysis), and intelligent algorithmbased methods. Further studies [2,27e29] have introduced the method to assess the related risks and carried out the calculation of the leakage volume during the period of unsteady or steady state leaking [27e29]. Others [30e33] are concentrated on the diffusion or dispersion of the released fluid or gas from pressurized pipelines into the air, soil or water especially the high-consequence area.

G. He et al. / Energy 188 (2019) 116031

However, the executable sustainable response strategies [34] to prevent, minimize, control or mitigate leaking fluid hazards remains under investigation. After the occurrence of a leak, the prompt action in all situations is to isolate the leaking section from the pipeline, i.e. to activate the upstream ESD (emergency shut down) valve and any intermediate valves at the upstream/downstream of the leak point [35]. However, this method is not a clear shutdown strategy, which does not give a reasonable explanation or goal as well as a quantitative operating procedure. Further, there are few researchers focusing on integrating the data chain and module chain into the smart management system of the longdistance pipeline, or upgrading the pipeline integrity management technology. Therefore, the demand for the smart long-distance pipeline is becoming urgent. This paper proposes a technical framework of leak analysis and accident handling system for the long-distance pipeline based on real-time data gathering and Distributed Computing for pipeline network monitoring and Integrity management. Secondly, a case study of pipeline leakage accident has been carried out to implement the proposed construction idea of data chain and module chain. The proposed methodology and the details of the mathematical models are listed in the third section. In the fourth section, the accuracy, computational stability, and robustness of the proposed method are evaluated with experimental data in real-life pipeline case. Finally, conclusions are listed in the fifth section. This paper provides several novel contributions to the field: (1) Based on ideas and technology of Big Data, Cloud Computing, and IoT, the technical framework of the smart management system for long-distance pipeline is put forward, and the construction idea of data chain and module chain in the management of pipeline integrity is given. (2) A new method of leak location identification is proposed. The goal is to locate the leak position as soon as possible. (3) The strategy of the emergency shutdown after a leak is given. (4) The leakage of the whole leaking process is calculated, and the minimum leakage volume before the pipeline shutdown is obtained. (5) The two groups of field experiments verify the feasibility of the pipeline smart management system and the accuracy and reliability of the three modules. 2. Smart management system for long-distance pipeline As depicted in Fig. 1, a generic pipeline physical model including pump stations, valves, diameter change, oil batch interface, leaking point and various kinds of sensors is established to study the conditions and features of the pipeline leaking at some point with different surroundings. The fluid flow in a pipeline can be regarded as a onedimensional flow that satisfies the conservation of mass, momentum, and energy. The outside environment of the damaged area on a pipeline is probably with air, water, soil or mutual situation, which can be easily found in the multiproduct pipeline network in southern China. 2.1. Large system architecture The long-distance pipeline smart management system integrates the whole lifecycle data of pipeline through the architecture of “terminal þ Cloud þ Big Data”, provides intelligent analysis and decision support by Cloud Computing and distributed computing. At the same time, the system also improves the quality, rate of progress and safety management by means of information, which is to realize the visualization, networking and intelligent management of pipelines. Finally, a safe and efficient smart oil and gas pipeline network with the capability of comprehensive

3

perception, automatic prejudgement, intelligent optimization, and self-adjustment is formed. It will greatly promote the technology level of pipeline integrity management by enhancing pipeline data acquisition, risk assessment, prevention and control, defect detection and evaluation. As shown in Fig. 2, the architecture of IoT technology in pipeline smart management system comprises the perception layer, network layer, and perceptual layer. It is fully integrated with the existing long-distance pipeline SCADA system, combining with the pipeline integrity management system, giving full play to the advantages of the IoT technology, and strengthening the application depth of long-distance pipelines. The perceptual layer collects and stores all kinds of data, and updates applicable services from the original manual acquisition to the automatic acquisition of the equipment. The network layer realizes fast and reliable data transmission and sharing through various transmission media and communication technologies. The perceptual layer conducts business application and analysis on production data and provides the basis for management and decision-making layer. Cloud Computing, Big Data, and other technologies are used as core modules to store, calculate, process and analyze pipeline data collected by the perceptual layer. Then the real-time control, integrity management and scientific decision-making of longdistance pipelines can be realized. In this technical framework, Cloud Computing technology can be applied to integrate and store massive, real-time and accurate data from inductive components embedded in oil and gas pipelines, equipment and facilities, environmentally sensitive points, and maintenance tools. Using Big Data technology, one can dynamically analyze data from multiple perspectives and multi-levels, explore the potential value of data with specific businesses, optimize the process and prevent and control risks, so as to ensure safe and efficient operation of pipelines. With the IoT, all data of the longdistance pipelines can be transmitted in real time through field bus, industrial Ethernet and satellite communication. 2.2. Construction of data chain and module chain Fig. 3 shows the data flow and emergency response of the pipeline system IoT and module functions implementation. The pipeline system IoT can utilize Big Data and Cloud Computing technology to automatically realize the functions of leaking point positioning, leakage measurement, leak orifice feature prediction, and emergency plan formulation in leak accident conditions. When a leak occurs in the pipeline system, the ASPEN database will transmit the data such as pressure and flow along the pipeline to the leak location module. This module uses the leak location algorithm proposed in this paper to calculate the location of the leaking point and shows the specific leaking point through the GIS (geographic information system). Then the leak location module transmits the leaking point position information to the leakage measurement module. Based on the pressure, temperature, and flow data collected by ASPEN and the location of the leaking point, the module calculates the leakage and leak rate during the leakage period of the pipe and predicts the shape and size of the leaking orifice. Finally, the operation scheme of the valves along the pipeline and the minimum leakage in the process of pipeline shutdown is calculated by calling the pipeline accident emergency response module. 3. Mathematical model and methodology This section includes the transient flow simulation model, fast leak detection and locating model, prediction of the damaged area and leakage severity model, and analysis model of pipeline

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G. He et al. / Energy 188 (2019) 116031

Fig. 1. The Generic pipeline model and illustration of leaking situation.

shutdown. 3.1. The transient flow simulation model The transient flow coupled with heat transfer in the pipeline is an unsteady state process. Accordingly, the mathematical model for this issue consists of the one-dimensional continuity equation, momentum and energy conservation equation, and related initial and boundary conditions. The equations set for transient pipe flow and heat transfer are listed as follows [36e39]:

vðrAÞ vðrAvÞ þ ¼0 vt vx

(1)

vv 1 vp vv l þ ¼ v  g sin q þ vjvj vt r vx vx 2D

(2)

vT Ta2 ap vv vT lv3 4q þ þv ¼  vt vx 2Dc rDc c vx

(3)

Hence, the equation of pressure wave can be denoted as a ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi maxðao;i Þ and ao;i ¼ 1= ro ð1=ko þ Di ci =Ei di Þ. Upstream and downstream boundaries are determined by upstream temperature, upstream pressure/flowrate, and downstream flowrate/pressure. The method of characteristic line combined with the finite difference method is used to solve the problem of hydrothermal coupled transient flow problem [35]. The algorithm procedure is shown in Algorithm 1. 3.2. A novel fast leak detection and locating model The noise will affect the accuracy when locating leak point with negative pressure waves, it is required that the denoised pipeline pressure signal should be smooth and the mutations of the signal should be conserved so that pressure drop caused by leakage can be well recognized and leakage can be accurately located. An SNREMD (small noise reduction method based on empirical mode decomposition) was proposed by Lu et al. [40] and Lang et al. [41]. The NPW (negative pressure wave) produced by the leakage propagating up and down simultaneously at the speed a. The produced NPW is detected by the sensors at t and t þ Dt time, respectively, with pressure signals pu ðt þDtÞ and pd ðtÞ when l1>l2,

G. He et al. / Energy 188 (2019) 116031

5

Fig. 2. Overall design - the technical architecture of the long-distance pipeline smart management system.

as shown in Fig. 4. Because the attenuation coefficient of the decompression wave is independent on the amplitude, a point within the pressure drop range can be taken as a reference point to normalize the pressure gradient. Then the pressure is standardized as pu ðtÞ )pu ðtÞ  minðpu ðtÞÞ and pd ðtÞ )pd ðtÞ  minðpd ðtÞÞ. According to the leakage experimental data, the vp vt in the upstream and downstream are functions related to t, and the shapes of line A and B are very similar, as shown in Fig. 5. The correlation analysis can be used to calculate the phase difference between the two functions. The function Au ðtÞ of the upstream pressure gradient on t and the function Ad ðtÞ of the downstream pressure gradient on t are obtained by the cross-correlation analysis, line C and D, respectively, as shown in Fig. 5. The time difference between the upstream and downstream on receiving the decompression wave is Dt , and the negative pressure wave is received later in the upstream because of l1>l2. Then Dt can be obtained by using the next formula:

Dt ¼ tu  td

tu ¼ ftjAu ðtÞ ¼ minðAu ðtÞÞg t

(4)

td ¼ ftjAd ðtÞ ¼ minðAd ðtÞÞg t

The formula for the cross-correlation analysis is as follows:

1 Au ðtÞ ¼ lim 2T

Dt2 ¼ tu2  td2

(5)

t

Where, t2ð  L=a; L=aÞ. When the pipeline leak occurs, the crosscorrelation function will change along with the change of the

(6)

The velocity of the wave is a, and the fluid flow velocity is v. If the pipe is divided into several sections according to the mileage, there is: Lðr

ðT pu ðtÞpd ðt  tÞdt

pipeline leakage situation. When the difference between the value of the cross-correlation function AðtÞ and the stability value exceeds a preset threshold, it can be determined that a leak has occurred in the pipeline. When t ¼ t, the minimal value is reached and thus the tu and td as well as Dt ¼ tu  td can be determined. In order to improve the accuracy, the concavity and convexity of the pressure change curve are used to determine the time when the pressure changes rapidly, that is, the time when the first decompression wave is received at upstream and downstream. The upstream and downstream pressure curves are convex function curves, and the second derivative is less than 0. The result graphs of 2 v2 pu and vvtp2d are the curves E and F, respectively, as shown in Fig. 5. vt 2 The moments corresponding to the minimum point on the curve are tu2 and td2 , respectively. Therefore, we get tu2 and td2 with higher precision by cross-correlation analysis. Then the leakage duration is calculated via:

0

dx  aðxÞ  vðxÞ

ðL Lr

dx ¼ Dt2 aðxÞ þ vðxÞ

(7)

The Dt2 of leakage is obtained by using the measured data at kth time step. Lkr means the distance from the start to the leaking point, where the location of leaking point is decided via the measured data at kth time step. The t k represents the time from leakage

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G. He et al. / Energy 188 (2019) 116031

Fig. 3. Data chain and module chain in IoT of pipeline network under leak condition.

Algorithm 1 Transient flow simulation of long-distance liquid pipeline Require: Pipeline mileage, elevation; leak point position; upstream pressure, flow, temperature in leakage time, downstream pressure and other parameters. Build mesh Discrete the partial differential equations by the finite difference method while time < time limit do while the results do not meet the accuracy requirement do for each node do Calculate the physical properties of oil Calculate the coefficients of DE (Discrete Equation) Compute the flowrate and Pressure of grid nodes on characteristic lines end for Calculate the temperature of grid nodes if iterative convergence then Locate the interface between oil batches Update the variables under boundary conditions Set time ) the next time else Pass the temperature to physical properties model of oil end if end while end while

occurring to kth time step.

min Lkr

F k2

   Lðkr ðL    dx dx     ¼    Dt k2    aðxÞ þ vðxÞ aðxÞ  vðxÞ 0

(8)

Lkr

Lkr

The is calculated by using the measured data at kth time step. Through repeating the above process until t k ¼ k,dt, lim tðtÞ ¼ Dt k2 t/t k and lim Lr ðtÞ ¼ Lkr can be obtained. In this way the newly received t/t k data during the leaking process is fully utilized, and the leak occurrence time and the leaking point position are detected as quickly as possible. Thus, the real-time solution process for the leakage localization model is Algorithm 2.

3.3. An improved model for prediction of damaged area and analysis of leakage severity Negative pressure waves are transmitted from the leaking point to the ends of the pipeline creating pressure drops. The water hammer formula is DH ¼ agDv. The pressure is given more priority for calculating the size of the leaking orifice. The change of pressure at upstream and downstream station can be calculated based on negative pressure wave propagation attenuation model [35].

 l Dx Lr =Dx , v jDpu j ¼ jDpL j 1  2D a

(9a)

G. He et al. / Energy 188 (2019) 116031

7

Fig. 4. Diagram of leaking point location where l1>l2.

Fig. 5. Diagram of the pressure drop.

 l Dx ðLLr Þ=Dx , v jDpd j ¼ jDpL j 1  2D a

(9b)

The attenuation of the negative pressure wave is linked to the hydraulic friction coefficient. The characteristic equation of leaking boundary through an orifice is that the efflux flowrate at leakage node is:

qP ¼ QL ¼ C0

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   2g Hp  H0

(10)

The leaking parameter is defined as C0 ¼ aCd AL , which can be calculated as:

C0 ¼

Apipe jDpu j  Lr;i =Dx pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiQ li ,Dxv 1  2D ra 2gðHL  zL Þ i a i i

(11)

i

Different shapes of leak orifice have different leakage coefficients, which are list in Table 1.

The boundary conditions of leaking through a tube and leaking through orifice are different in terms of outflow coefficients, local friction, and environmental pressure. The pressure of the soil around the buried pipeline and the variation of the oil vapor pressure in the pipe are different. The following formula is derived for calculating the flowrate of leaking through the tube:

qP ¼ C0

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi    2g HL  zL  pL =ðrgÞ  hvf þ hf

p

(12)

In which, hvf ¼ f1 qP 2 , hf ¼ f2 qP 5LL The flow formula is shown in dL formula (33): p

2

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u H  z  pL =ðrgÞ u . qP ¼ t . L L 1 2gC 20 þ f1 þ f2 LL d5L The leakage parameters C0 of leaking through the tube is:

(13)

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G. He et al. / Energy 188 (2019) 116031

Algorithm 2 Fast leak detection and localization Require: Upstream pressure, downstream pressure, time step, number of time step in steady state, negative pressure wave velocity, pipeline length, diameter, flowrate. Calculate the maximal time of negative pressure wave going through the entire pipeline while Number of time steps less than (2  L=a=dt þ tsteady =dt) Smooth the upstream and downstream pressure data with SNR-EMD algorithm Calculate the pressure drop versus time, then normalize and smooth it Calculate the two cross-covariance vectors and find the minimal value, respectively Determine the time interval and its variable range Calculate the second derivative of pressure versus time, then normalize and smooth it Find the minimal value in the above-mentioned variable range, respectively Determine the time interval and leak location end while

C0 ¼

Apipe jDpu j rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Lr;i =Dx Q   li ,Dxv 1  2D ra 2g HL  zL  pL =ðrgÞ  hvf þ hpf i i ai i

(14) Thus, the leakage coefficient, transient leaking flowrate, and accumulative leakage volume can be obtained. Based on the transient flow simulation model, the procedure for prediction of the leaking area and calculating the leakage severity is as follows, where NPWPAM is negative pressure wave propagation attenuation model. 3.4. Analysis model of pipeline shutdowndnovel handling scheme After resolving the parameters like pressure, temperature, main and leaking flow rate during leaking, further research is required to establish a model with the target of minimizing leaking rate based on pressure transient flow inverse problem theory. A shutdown operational procedure can be figured out by simulating the operational conditions of the leaking pipelines to ensure safe transportation and to reduce losses of incidents. 3.4.1. Problem description A complete pipeline hydraulic system contains upstream, intermediate pump stations, functional valve group (Valve 1, Valve 2) and downstream, as shown in Fig. 6. Any operations of any equipment can affect all hydraulic parameters. When a leak occurs, the most important thing to be determined is adjusting the hydraulic parameters and reducing the leaking losses and risks. In this context, the valve group would be eventually closed to reduce the effects of leaking on the pipeline system. For example, when leaking occurs in the system in Fig. 6, Valve 1 and Valve 2 would be closed, which makes the leaking section as a separated hydraulic system. The closing speed of the valves is the decision variables for the leak rate of the leaking point. The objective function is to minimize the leakage quantity during the leaking process before a shutdown. Thus, an optimized hydraulic equipment control procedure can be solved by considering transient equations, hydraulic equipment constraints, and pipeline constraints. Based on the continuity and momentum equation, the transient characteristics during the pipeline leaking process are featured as

follows:

 1 vQ vQ vH þv þ þ fQ jQ j1m ¼ 0 gA vt vx vx

(15a)

vH vH a2 vQ þv þ ¼0 vt vx gA vx

(15b)

vQ vQ vH vH vH Since vQ vt > > v vx and vt > > v vx , the term v vx and v vx can be ignored, thus the above equations can be simplified as:

1 vQ vH þ þ fQ jQ j1m ¼ 0 gA vt vx

(16a)

vH a2 vQ þ ¼0 vt gA vx

(16b)

3.4.2. Optimized leakage volume control model The transient equation, hydraulic equipment constraints, and pipeline constraints are also considered, and the optimal control scheme for hydraulic equipment is solved.

3.4.2.1. a. Objective function. The objective of the model solution is to minimize the leakage volume of the whole process before the pipeline shutdown. It is the summation of the product of leak rate and the corresponding time step. The summation starts from the leaking beginning time to the moment of fully shutdown of the valve group.

min F ¼

3.4.2.2. b. Constraint equation. The water hammering momentum equation and continuity equation are differentiated. The partial derivative of the transient term is discretized by the implicit difference scheme. The distance partial derivative is discretized by a first-order upwind scheme, which is unconditionally stable. The discretized differential equations are as follows:

j

gAi Dt

Re

Circular orifice

Triangular orifice

Long strip orifice

>100 100

0.65 0.50

0.60 0.45

0.55 0.40

(17)

j

1 Table 1 The value of flow rate coefficients.

X j Q i;leak Dtj j2J; i2IL



jþ1

Qi

   1   j jþ1 jþ1 j jþ1  jþ1 1m Hi  Hi1 þ f i Q i Q i   Qi þ Dx

¼ 0 j2J; i2I (18a)

G. He et al. / Energy 188 (2019) 116031

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Algorithm 3 Prediction of leakage coefficient and analysis of leakage strength Require: Pipeline mileage, elevation; leaking point position; upstream pressure, flow, temperature in leakage time, downstream pressure and other parameters. Solve the transient pipe flow simulation model via Algorithm 1 while time < time limit do while the results do not meet the accuracy requirement do Solve the NPWPAM to obtain the pressure at the leaking point. Set the feature of the leaking area and predict the leakage coefficient end while Compute the current transient leaking flowrate and accumulate leakage volume end while

Fig. 6. The hydraulic system of a pipeline.

a2 gAji Dx



 1   jþ1 jþ1 j H i  Hi ¼ 0 j2J; i2I  Q i1 þ Dt

jþ1

Qi

(18b)

When the pump unit is under operation, the hydraulic head of the later point equals the prior head plus the pump head lift. When the pump unit is shutdown, the hydraulic head for the point before and after the unit are the same. j

j



j

j

(19a)

(19b) þ

j Bi;pump M

j2J; i2IP

(20a)

Hji;pump  H ji;pumpþ  Bji;pump M j2J; i2IP

(20b)

Bji;pump is a binary variable. It equals to 1 when the pump unit is in service, whereas it equals to 0 when the pump unit is shutdown. The characteristic curve of the pump boundary conditions while running can be set as:



DH ji;pump ¼ apump;i  bpump;i Q ji

2m

j

j

j 2 J; i2IP

j

Hi;valveþ ¼ Hi;valve þ DHi;valve j2J; i2IV

(23)

The valve resistance characteristic equation can be expressed as:

DHji;valve ¼

xvalve  2gðAi Þ2

j

Q i;valve

2

 2 j ¼ fi;valve Q i;valve j 2 J; i2IV (24a)

  j j j j Hi;pump  H i;pumpþ þ DHi;pump þ Bi;pump  1 M j2J; i2IP

j  H i;pumpþ

(22)

The later hydraulic head equals the prior hydraulic head minus the valve throttling head loss, as shown in the following equation:



Hi;pump  H i;pumpþ þ DHi;pump þ 1  Bi;pump M j2J; i2IP

j Hi;pump

DHji;pump ¼ 0 j2J; i2IP

(21)

The boundary condition of the pump unit while shutting down is set as:

Then, the flow rate coefficient for the valve at moment j can be calculated as:

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u u 10 j Q i;valve j2J; i2IV fi;valve ¼ 3600t j DHi;valve

(24b)

The flow rate before and after the pump unit and valves are the same. j

j

Q i;valve;pumpþ ¼ Q i;valve;pump j2J; i2IP∪IV

(25)

Based on the orifice outflow equation, the flow rate at the leaking point can be determined as:

  j2 j Q i;leak ¼ 2g a2leak AL2i;leak Hi;leak  Hleak  Hair j2J; i2IL

(26)

The flow rate before the orifice equals to the difference between the flow rate after the orifice and the flow rate at the leaking point.

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Algorithm 4 Shutdown strategy of the leaking pipe Require: Basic parameters of the pipeline; Pressure and flow data of the pipe when the valve begins to operate. 1 while Oil saturated vapor pressure > pressure & pressure > permissible pressure do Set the travel time of the upstream and downstream valves Compute the pressure at the upstream and downstream valves If Oil saturated vapor pressure < pressure < permissible pressure before the valve then Go out of the loop of while using break else Adjust the travel time of valves by the Golden ratio method end if end while 2 while jjPressure at the leaking point- calculated pressure at leaking point jj > error do Set the time to adjust the upstream and downstream valves Compute the pressure at the leaking point during the operation of valves If non-monotonic drop of pressure at the leaking point then Change the time to adjust the upstream and downstream valves else end if end while

j

j

j

Q i;leakþ ¼ Q i;leak þ Q i;leak j2J; i2IL

(27)

j

The pressure at the leaking point is:

P ji;leakþ ¼ P ji;leak j2J; i2IL

Hi  Hvapor j2J; i2I

j

(30)

(28)

The hydraulic head at any points cannot exceed the maximum allowable hydraulic head for the pipeline, meaning:

Hi  Hi;max j2J; i2I

minimum allowable hydraulic head for the pipeline, which is the head of the saturated vapor pressure:

(29)

The hydraulic head at any points also cannot drop below the

3.4.2.3. c. Decision variable. The decision variable of the optimized emergency shutdown process is the closing speed of the valves. After shutting down the pipeline, the variation of the valve closing speed can control the flow rate of the whole line as well as its pressure. This could avoid excess localized pressure and further

Fig. 7. Profile of the experimental pipeline and illustration of leak experiment 1.

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cracking after shutting down. 3.4.3. Model solving The model is solved by Algorithm 4. 4. Experimental scheme and algorithm performance testing Two leakage experimental protocols are designed relying on Sinopec China product pipelines, and the results of experiments are analyzed and used to validate the established computational models. 4.1. Experiment 1ddPunching experiments on pipeline The experimental setup for leaking through an orifice is shown in Fig. 7, where the multiproduct pipeline [42] in Sinopec South China was chosen to do the test. The length of the pipeline segment is 93.9 km with an inner diameter of 0.3078 m. The test leaking point is located at 90.5 km, the automatic valve in the upstream station is 90.5 km from the leakage point, and the automatic valve in the downstream station is 3.4 km from the leakage point. There are no automatic valves along the pipeline. The DN50 valve was set to control the leaking process, and the valve is located at the leak point. 0# Diesel was transported with 444.3 m3/h. The leaked oil was stored in a tank. Oil discharge is measured by flow pump on site, including three sets of tests under different valve opening.

11

4.1.1. Analysis of fast leak detection and locating In Fig. 8, there are the smoothed pressure data, the derivative of the pressure over time, the cross-correlation analysis of the upstream and downstream pressure derivatives, and the second derivative of the pressure over time. The different number of time steps are {36, 41, 46, 51, 56}, where the time step is dt ¼ 5 s (the corresponding time of 36 is 36  5 ¼ 180 s). No matter how many time steps are used, it can be obtained by analyzing the derivative of pressure over time: in this experiment, the leakage starts from the 20th time step, before which it is in steady state as a normal condition. The wave velocity a ¼ 1022 m/s and the length L ¼ 93.9 km are obtained from the transient flow simulation of the pipeline. It takes L ¼ 18 time steps for the negative pressure wave to pass kLa ¼ adt through the entire pipeline. Therefore, data up to the twice of this time is taken, that is, the data length of the leakage state is from 20th to 56th time steps. From the cross-correlation analysis and the second derivative curve of pressure over time, the time at which the negative pressure wave is transmitted from the leaking point to the signal receiver in the downstream station is the 22nd time step. The negative pressure wave transmitted from the leaking point to the signal receiver in the upstream station is the 39th time step, where the time difference is Dt2 ¼ 17s. The position of the leaking point is calculated to be 90317.20 m, compared with the experimental data of 90500 m, the absolute error of the model calculation result is 182.79 m, the relative error is 0.20%, and the calculation accuracy of

Fig. 8. Pressure data at different time length and its Dt2 .

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the model is acceptable. Fig. 9 shows the results of leaking location by using different duration data. Between 20th and 56th time steps, 12 numbers of different durations were taken. The different number of time steps are {20, 24, 27, 30, 34, 37, 40, 44, 47, 50, 53, 56}, respectively. It can be seen that the accurate positioning results can be obtained at the 40th time step. Therefore, it can be considered that with the leak detection data from kLa to 2kLa , high accuracy of the leak locating results can be obtained.

4.1.2. Prediction of damaged area and analysis of leakage severity The experimental back pressure was set as 200 kPa which cannot be neglected in practice when the liquid was discharged into a tank to simulate the leaking process in experiment 1 (Southern China). The leaking experiment lasted for 510s, where the upstream pressure, flowrate, temperature and downstream pressure are needed to simulate the transient thermo-hydraulic parameters along the pipeline, as can be seen in Fig. 10. It can be seen in Fig. 10(a)and(b) that the pipeline has a steady state from 0s to 120s, where the pressure at both station GY and AS as well as the flowrate and temperature at station GY fluctuate slightly. Once the leaking happens, as shown in Fig. 10(c), the pressure and flowrate at the leaking point drop sharply and instantly according to the simulation result, which causes negative pressure waves transmitting towards both upstream and downstream. The arrival times of the waves at the double endings are different since the distances between the leaking point and the endings are absolutely different. Depending upon the model, the equivalent diameter of the leaking orifice is 20.18 mm. As shown in Fig. 10(d), the accumulated volume is 3.87 m3 while the measured leakage volume is 3.66 m3. The relative error is 5.7%. The transient leaking flowrate increases shortly and then decreases slowly, where the leaking flowrate finally converges to a relatively stable value of 0.007813 m3/s after the fluctuation. The accumulated leakage volume rises approximately linearly, which is a significant increasing

tendency with a relatively stable growth rate. The flow and pressure heads along the pipeline are shown in Fig. 10(e), which indicates that the fluid flow in the pipeline can be simulated in real-time by our model. The simulation results show that the flowrate at the downstream side of the leaking point was drastically reduced, which is consistent with the actual situation and thus demonstrates the accuracy and reliability of the model. In Fig. 10(f), the different leakage areas with the different shapes of leak orifices are shown. For the same leakage coefficient, the leakage area of the circular hole is the smallest and that of the triangle is larger, while the leakage area of the stripped hole is the largest. This is because their outflow coefficients are successively smaller. 4.1.3. Analysis of pipeline shutdown The front pressure head of the downstream valve with different valve closing time is shown in Fig. 11(a). There is an inflection point of head in Fig. 11 (a) because of the valve's piecewise-linear flow characteristics. The valve's minimum travel time is 30s, during which the front pressure head of the downstream valve exceed 1630 m. When the valve closing time is too short, the front pressure wave is larger than the specified pressure upper limit (1630 m) of the valve. When the valve closing time is too long, the time for highly-strengthen leaking will also be extended although the valve will not be over pressurized. Thus, the appropriate closing time is 51s which also means the valve's minimum closing time is 51s. After obtaining the minimal valve closing time, the operation strategy of the upstream and downstream valves is optimized. If the leaking point is too close to the downstream, the time required for the decompression wave generated by the upstream valve to propagate to the leaking point is much longer than the time for the valve to safely close. Therefore, in this case, the leakage flow at the leaking point in the valve closing phase cannot be mitigated by using the principle that the decompression wave and the supercharging wave overlap each other, that is, in the case of ensuring safety, the fastest closing method is the optimal way to close the valve. The change of the upstream and downstream valve flow coefficient over time is shown in Fig. 11(b). It is shaped like a branch of a hyperbola. Fig. 11(c) shows the change of the pressure head at the leaking point during the valve shutting process, where the pressure at the leaking point is either still in an increasing trend due to being close to the downstream valve, and the maximum pressure generated by closing the piping system within 1s was 1465 m. Fig. 11(d) shows the instantaneous leaking flowrate and the cumulative leakage volume during the valve closing process, and the accumulated volume is going to be a parabola when the flowrate increases. The cumulative leakage during the valve closing process is 0.42 m3. The variation of the head and the flowrate along the pipe during the valve adjustment process are shown in Fig. 11(e) and (f) respectively. With the closure of the upstream and downstream valves, the pressure at the starting point gradually decreases while the pressure at the endpoint gradually increases, and the flowrate along the pipeline always decreases over time. 4.2. Experiment 2ddOil delivering experiment

Fig. 9. The results of leak location by using different durations data.

As shown in Fig. 12, CXD- DX (A-B) and DX-ZH (BeC) oil product pipelines were chosen as the experimental object. As an intermediate point of the pipeline, Station DX as an oil delivery station was applied to imitate the process of large-scale oil leakage. The pipeline length is 95.4 km and the leaking point is located at 25.4 km away from the upstream Station CXD. Also, we have D ¼ 0.3097 m and Q ¼ 304.1430 m3/s.

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Fig. 10. The accumulated and transient leakage volume during transient leaking process.

4.2.1. Analysis of leak locating In Fig. 13, different number of time steps are {122, 149, 175, 202, 229, 255, 282, 309}, where the time step is dt ¼ 1s. The leak in this experiment starts from the 120th time step, before which it is in steady state. It is noticed that three red lines at kth ¼ 202, 229, 255 are converged together and almost overlapped. Also, two red lines at kth ¼ 282, 309 are overlapped. As for the green lines, it is firstly detected at the moment of 175s, which means the detected

downstream pressure decreasing is later than that of the upstream. The reason is that the pipeline length is 95.4 km and the leaking point is located at 25.4 km away from the upstream Station. Additionally, only 6 distinguished lines exist and there is no overlapping phenomenon among them. The wave velocity a ¼ 1010.9 m/s and pipeline length L ¼ 95.4 km are obtained from the transient flow simulation of the L ¼ 95 time steps for the negative pipeline. It takes kLa ¼ adt

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Fig. 11. Determination of valve closing time and change of the parameters along the pipeline during valve closing.

G. He et al. / Energy 188 (2019) 116031

Fig. 12. Profile of the experimental pipeline and illustration of leaking experiment 2.

Fig. 13. Leakage point localization results through using different data from different durations.

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25400 m, the absolute error of the model calculation result is 7.31 m, the relative error is 0.03%. It is further verified that the calculation accuracy of the model is high. From Fig. 14, the smaller the time step is, the smaller the leak locating error is. In the early stage of leakage, the larger the number of the time step is, the higher the leak locating accuracy is. When 2L , the number of collection points can no the number exceeds adt longer improve the positioning accuracy. In this context, the number of data points is enough to locate the leaking point. After the leak occurs, the leak location can be known as quickly as 70 s. For the sake of safety, it is assumed that the leak location can be located correctly after 95s. As in Fig. 15 and Table 2, the sooner the leakage of the pipeline is detected and the sooner the shutdown measures are taken, the less leakage in the whole leaking process is. It can also be seen from the table that the earlier the leakage is detected and the earlier the valve closing measure is started, the longer the adjustment time of the valve will be.

Fig. 14. Lr , tðtÞ and Lr ðtÞ for several sets of leakage condition data.

Fig. 15. The accumulated leakage volume at different valve closing time.

pressure wave to pass through the entire pipeline. Therefore, the data length of the leakage state is from 120th to 309th time steps. It can be obtained that the time at which the negative pressure wave transmitted from the leaking point to the signal receiver in the downstream station is the 139th time step. The negative pressure wave transmitted from the leaking point to the signal receiver in the upstream station is the 183rd time step, where the time difference is Dt2 ¼ 44s. The position of the leaking point is calculated to be 25407.31 m, compared with the experimental data of

4.2.2. Prediction of damaged area and analysis of leakage severity According to the data recorded by the SCADA system from leaking experiment 2, the inlet pressure of station C and outlet pressure of station A are illustrated in Fig. 16 (a) and (b). Data of former 120s represent the normal working condition and the experiment then lasts for 298s (120se418s). In Fig. 16(a), the distances between upstream and downstream from the leaking point are different, resulting in different arrival times of negative pressure waves. The pressure at station CXD drops earlier than that at station ZH. In Fig. 16 (b), the upstream temperature and flow of the station CXD are either still in a fluctuating state. In Fig. 16(c), when the leakage occurs, the pressure and flowrate of the leaking point drop abruptly. The leaking flowrate depicted in Fig. 16 (d) soon reaches its maximum value and then keeps decreasing while the total leakage volume increases all the time. The calculated leakage volume is 2.75 m3, as shown in Fig. 16(d), while the practical volume is 2.89 m3. The relative error is 5.08%. The leaking orifice has a diameter of 10.348 mm, which is simplified as an equivalent circular orifice. The flow and pressure heads along the pipeline are shown in Fig. 16(e). Similar to experiment 1, the simulated results are consistent with the actual situation, which further illustrates the accuracy and reliability of the model. In Fig. 16(f), the different leakage areas under the different shapes of leak orifices are shown. Leak orifices with different shapes have different outflow coefficients, resulting in different leakage areas coefficients. 4.2.3. Analysis of pipeline shutdown In the second set of examples, the pressure head in front of the downstream valve must not exceed 580 m. The pressure head in front of the downstream valve over different valve closing time is shown in Fig. 17. The minimal valve closing time is 58 s. Fig. 17(a) shows the change of the front pressure head of the downstream valve at different valve closing time. When the valve closing time is 58s, the upstream pressure at the valve reaches the

Table 2 The fastest shutdown times and total leakage volume at different valve closing time. Time to shut valves(s)

Leakage volume before shutting valves (m3)

Leakage volume during shutting valves (m3)

Time duration of shutting valves(s)

Total duration of transient leaking(s)

Total leakage volume (m3)

70 80 95 120 190

0.543 0.646 0.795 0.987 1.585

0.632 0.632 0.623 0.614 0.586

67 67 66 65 62

137 147 161 185 252

1.175 1.278 1.418 1.601 2.171

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Fig. 16. Experimental data and calculation results of delivery experiment as a pipeline leakage leaking experiment 2 (Beijing).

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Fig. 17. Determination of valve closing time and change of the parameters along the pipeline during valve closing.

G. He et al. / Energy 188 (2019) 116031

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Table 3 Analysis of the results of the above 2 tests. Exp. Type

Punching for leakage

Delivering for leakage

Duration of leaking (s) Calculated leaking location Exp. leaking location Relative error of leaking localization Measured leakage volume (m3) Calculated volume (m3) Relative error of leakage volume (%) Calculated orifice size (mm) Exp. leakage flowrate (m3/h) Calculated leakage flowrate (m3/h) Relative error of leakage flowrate (%) Duration of pipeline shutdown (s) Minimal leakage volume during shutdown (m3) Minimal leaking duration before shutdown(s) Minimal leakage volume before shutdown (m3) Minimal total leakage volume (m3) Normal total leakage volume (m3) Reduced percentage (%)

510 90.32 90.50 0.20% 3.66 3.87 5.7% 20.18 26.90 28.45 5.7% 51 0.42 100 0.77 1.19 5.38 77.88%

298 25.41 25.40 0.03% 2.89 2.75 5.08% 10.348 34.96 33.19 5.06% 67 0.63 70 0.54 1.17 6.07 80.72%.

maximum, that is, the valve's minimum closing time is 58s. Fig. 17(b) shows the change of the upstream and downstream valve flow coefficient over time. Fig. 17(c) shows the change of the pressure head at the leaking point during the valve closing process. As in Fig. 17(c), with the valve operation scheme shown in Fig. 17(b), the leaking point pressure is always in a decreasing trend. Fig. 17(d) shows the instantaneous leaking flowrate and cumulative leakage volume during the valve closing process. The cumulative leakage volume during the valve closing process is 0.55 m3. The variation of the head and the flowrate along the pipe during the valve adjustment process are shown in Fig. 17(e) and (f) respectively. With the closure of the upstream and downstream valves, the pressure at the starting point gradually decreases while the pressure at the endpoint gradually increases, and the flowrate along the pipeline always decreases over time. 4.3. Analysis of above 2 tests All the calculated results are listed in Table 3. As shown in Table 3, the relative errors of leaking point localization are 0.20% and 0.03%. The average and maximum relative errors of the total leakage volume in the leaking process between the calculation results and the experimental results are 5.7% and 5.08% respectively. The errors of the leakage flowrate are 5.7% and 5.06% respectively. Therefore, the feasibility and reliability of the model are proved. The minimum total leakage volumes are 1.19m3and 1.17 m3, while the normal total leakage volumes are 5.38 m3 and 6.07 m3, respectively, which indicates that the leakage volume could be reduced as much as 77.88% and 80.72%. Here, the normal total leakage volume is the volume obtained during the leaking process before normal shutdown action taking place in which the average leaking duration time is about 15 min. Thus, the application of this modules chain in pipeline smart management system has its advantages in lowering economic losses and reducing the following pollution risks to the environment. 5. Conclusions Based on Big Data, Cloud Computing, and IoT technology, the technical framework of the smart management system for a longdistance pipeline is put forward. The pipeline integrity management technology for long-distance pipeline architecture is also established. Specific business requirements are used to explore, analyze and optimize management models to improve economic

efficiency, and promote the gradual progress of existing digital pipelines to be scientific and intelligent. The convenience of the modules chain is that the modules are linked with the real-time database to read data online, calculate the leakage online, operate the emergency system simply, and calculate the required results quickly and conveniently. The reliability of the modules chain is that the modules can accurately predict the leakage based on several experiments in situ via product oil pipelines and relying on field application results. A case study of pipeline leakage accident has been carried out to implement the proposed construction idea of data chain and module chain in the management of pipeline integrity. The proposed methodology and the details of the mathematical models are listed in this paper, including the pipeline flow simulation model, leak detection and locating model, pipeline leak rate calculation model, leakage volume calculation model, and the formulation model of pipeline shutdown scheme. The solutions of the corresponding models are followed. Two leakage experimental (punching experiment on pipeline and oil delivering experiment) protocols are designed relying on Sinopec China product pipelines. The accuracy, computational stability, and robustness of the proposed method are evaluated with experimental data obtained from the real pipeline, where the practicality of the model is verified based on another real-life case. The calculation error is less than 5%, which can preliminarily evaluate the economic losses caused by the leakage of oil. Based on the modules and the system, the leak point can be located as quickly as possible, the leakage can be calculated in real-time, and the emergency response plan is provided fast to improve the risk management efficiency. Looking into the future, the intelligent pipeline will effectively solve the issues of construction quality controlling and data collection during the construction period. Through advanced perceptual systems, the following key technologies could also be improved which include systematical data analysis and decision, pipeline risk identification, evaluation and control during operating, enhancement of the pipeline emergency response capabilities, and building of new pipeline operation management platform. Acknowledgment This work is funded by China Postdoctoral Science Foundation (2019M653481) and supported by the National Natural Science Foundation of China (51674212).

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