INTELLIGENT REMOTE MONITORING SYSTEM FOR CATHODIC PROTECTION OF TRANSMISSION PIPELINES

A. Peratta, J. Baynham, and R. Adey

CM BEASY Ltd Ashurst Lodge

Southampton, Hampshire, SO40 7AA, UK

Gervasio F. Pimenta ISQ Production Technologies

Instituto de Soldadura e Qualidade Taguspark, Cerais, 2780-994, Portugal

ABSTRACT

The paper presents an intelligent remote monitoring system for the assessment of coated transmission pipelines of few hundred kilometres long. The objective of the remote system is to be able to collect, transmit and interpret measurements of potential coming from different sensors distributed in the field along the pipeline in order to have a continuous monitoring and assessment of the system.

Keywords: Cathodic Protection, CP, simulation, remote monitoring, pipelines, Boundary Element Method, reverse modelling

INTRODUCTION

Corrosion in pipelines is normally controlled by two main different mechanisms: Coating and Cathodic Protection (CP) systems. Both need to be combined with a monitoring system in order to have an efficient solution for corrosion control. The monitoring approach needs to be cost efficient and accurate in order to detect anomalies and potential failures in real time, so that to aid pipeline operators in their maintenance and decision making policies. Monitoring is traditionally done by surveys (such as DCVG, or CIS), which do not allow systematic continuous quality assessment of the grid, nor it allows real time reports. In addition they are costly and time consuming. Traditionally, the monitoring of CP systems in pipelines requires periodic inspection and thorough analysis of the data from specialised operators. Field inspection is costly, time consuming, and prone to human errors if not done by qualified personnel. Existing modelling and monitoring techniques can be combined in order in order to avoid the aforementioned drawbacks. The benefits of an intelligent remote monitoring system are multiple. Firstly, it enables pipeline operators to assess the level of protection of their structures against corrosion avoiding the need of costly field surveys. Secondly, it helps to predict the remaining working lifetime of decayable/perishable components in advance, such as for example organic coatings and protective paints, or in the case of offshore structures sacrificial anodes. The data collected from the system can be employed to detect anomalies, such as the introduction of foreign metallic structures, stray currents, localised damage in the coatings, etc.

The prototype remote system is divided into a monitoring system, and a modelling system. The former consists of the set of hardware and software required to collect, transmit and store information from

the CP system associated with the pipeline, such as voltages and currents read from the sensors distributed along the infrastructure. The latter consists of a set of simulation tools for the analysis of the collected data, in order to evaluate and predict the level of protection against corrosion. This paper is structured into two parts. The first part describes the remote monitoring system, while the second one describes the modelling system.

REMOTE MONITORING SYSTEM

The technology behind the remote monitoring system is based on standard communications technology - cellular phones, antennas and GPS devices which may be used to assess readings on remote sensors, and therefore allowing for pipeline monitoring at the touch of a button. In countries with mature cell phone coverage, it is a technology to be immediately implemented. Figure 1 illustrates the elements involved in the remote control system, and their inter-relation.

FIGURE 1- Schematic of the functioning of the GPS based system

A series of sensors mounted in monitoring points at different locations in the field collects soil to pipe potential readings by means of a reference cell (ON and OFF). Also the status of the anode beds and rectifiers are collected and transmitted wirelessly to the network. The information is collected by servers across the network where the data is processed. The process involves updating a database which contains the historical data, as well as comparing the measured values against results coming from the simulation software. Finally, a series of reports on the status of the system become available in the network, so that any client terminal can access it; and in case of failures, or anomalies different warning signals are sent to mobile devices.

Current pipeline practices require ongoing inspection, corrections and maintenance of the system over the complete lifetime of the pipeline. Remote transmission of data using GSM, as far as the authors are aware, is not widely available in the European market so far. This may be due to the fact that GSM is a relatively recent communication technology. The high quality of GSM European coverage will allow the widespread use of the proposed surveillance technology. The illustration of a typical test post used in Portugal by the Natural Gas operator is shown in Figure 2. The circle shows the location of the device for collecting and transmitting data.

FIGURE 2 - Typical test post used in Portugal by the Natural Gas operator

The main objectives of the remote monitoring system are: (1) To acquire the relevant readings from the CP test posts; both on a daily scheduled timetable automatically, and on demand (manually). (2) To communicate these results to the central computer where the person in charge with analyzing the results can do his work. (3) To send an alert any time that it is not working properly or it has been stolen.

FIGURE 3 - Post installed in the field (left) and main server (right)

Figure 3 shows a picture of the central server which will supervise 1000 monitoring posts. Some specification details of the hardware developed for the CP system are included in Appendix A. The communications between base and remote stations is established by means of GSM/GPRS protocols.

CP MODELLING TOOL

Until the development of reliable computer modelling techniques the prediction of the detailed protection levels provided by CP systems was extremely difficult and relied on the skill and experience of the corrosion engineer and extensive post commissioning surveys. The development of boundary element methods has finally provided the necessary tools to accomplish these tasks. In this work the BEASY Corrosion and CP software is used to predict levels of CP protection interference on buried natural gas pipelines.

The main objective of the modelling tool is to determine the distribution of electric potential, electric field, and current in the electrolyte, anodes and pipeline in order to assess the level of protection of the structure against corrosion, to provide a better interpretation of the data collected by the remote monitoring system from the field, and to predict possible vulnerabilities in the coating, and determine

the existence of foreign structures which may interfere with the CP.

The purpose of this section is to describe the main features and to present calibration results of the modelling tool applied to the data coming from the field.

The condition of coatings on the pipeline surface changes over its lifetime due to the action of third parties, deterioration of the paint itself and damage caused by impacts, etc. Although increased current demand from the CP system can indicate the presence of damage, the location and extent is unknown as well as the local level of protection provided. The prediction of the state of the pipeline using data from the anodes and reference cells located along the pipeline is known as Inverse Problem. Some information about the conditions of the pipeline is assumed and an optimization process used attempt to match the predictions made by the BEM model and the measured data. Once a successful match is achieved the model not only provides information on the protection potentials at the reference cells but also all along the pipeline. When the process is linked with automated monitoring systems and/or survey data it has the ability to provide more complete information on the protection levels provided to the pipeline but also minimize the frequency of the collection of data both in the spatial sense and time.

Prediction of the Condition of the Pipeline from Reference Cells Measurements

The optimisation process requires that the problem is posed in the form of an objective function, design variables and constraints. In order to match the measured reference cell potentials the objective function was defined as the least squares of the difference between the target potentials at the reference cells and the potentials predicted by the model. The distribution of coating breakdown factor along the pipeline is considered as a degree of freedom in the optimisation (design variable). The objective function (W), describing the difference between the measured potential (Vj) and the calculated one (vj) at the different monitoring points is defined as follows:

( )2

1

opN

j j

j

W V v=

= −∑ (1)

where Nop is the number of observation points. The constraints of the problem are the known variables in the model, including:

• Polarisation data of the metallic structure

• Geometry of the pipeline (diameter, x,y,z points along its path)

• Metal resistance per unit length (cross section and resistivity)

• Type, geometry and location of anodes relative to the pipeline, impressed currents

• Definition of the CP electric circuit, location in the pipeline of the different connection points, as well as resistors and rectifiers

• Soil resistivity (homogeneous, vertically layered, and/or piecewise homogeneous in x-y direction)

The breakdown factor at each point in the pipeline is modified with an optimisation technique in order to minimise the corresponding objective function. The software can interpret a three dimensional map of the soil resistivity defined in terms of different regions and layer conductivities. However this accuracy and degree of detail is not always easy to obtain in practice. In uniform soils a soil conductivity defined with 10% of error translates typically into potential errors of 10%. The search for the optimal solution is done by varying coating breakdown factor, in addition to any other input data of the model which has a certain inaccuracy. The search is done by a combined the Simulating Annealing (SA) and the Nelder-Mead (NM) approach, also known as Donhill-Simplex method(1,2). This approach has been previously studied by Qiu and Orazem in previous works(3).

Conceptual Model and Modelling Approach

The conceptual model consists of the pipeline network immersed in a layered heterogeneous partially or totally saturated soil, considered as an electrolyte with variable conductivity. The CP system consists of a series of anodes, either sacrificial or with impressed currents, installed in the vicinity of the pipeline. The anodes are electrically interconnected by means of discrete electrical elements such as rectifiers, diodes and/or resistors between them and/or between a number of connections nodes in the pipeline. The conceptual model is illustrated in Figure 4. The model is considered as a multi-domain problem within the context of the Boundary Element Method (BEM) (4), as illustrated in Figure 4. It is basically composed of at least two regions: exterior and interior. The exterior region consists of the electrolyte (heterogeneous layered soil), while the interior consists of the highly conductive metallic pipelines involved in the calculation. The coupling between the two problems is done by imposing the corresponding continuity boundary conditions in the common interface. These matching conditions are provided by the polarisation curve, which describes the voltage drop (over-voltage) between metal and electrolyte in terms of the normal component of the current density flowing throughout the common interface.

Ve Vm

Γs

m- Ve

V= F(j.n)

Ωm

Ω e

ΓA

ΓC

j.n

Anode

Cathode

Electrolyte

Non-Conductive

External circuit

(rectifier+resistors+diodes)V

Non-ConductiveBoundary

Boundary

ρResistivity

Conductivityk

(Metallic structure)

FIGURE 4 - BEM conceptual model of a pipeline immersed in the electrolyte

Exterior problem: The governing equation in the electrolyte is given by the conductivity equation:

( ) 0=Vk ee∇−⋅∇ (2)

where Ve is the electric potential field in the electrolyte, and ke is the electrolyte (soil) conductivity. The current density is defined as:

Vk=J ∇− (3)

The volume occupied by the electrolyte is designed as eΩ , and its boundary Ω=Γ e ∂ with exterior

unitary normal n may have, in general, different types of boundary conditions (see Table 1.)

The electrolyte eΩ is considered to be a thin multi-layer whose characteristic x-y extension is much

bigger than the thickness (hi) of the different layers:

hLxy >> , (4)

where

∑L

N

=i

ih=h1

(5)

is the total depth of the soil involved in the model, and NL is the number of layers.

TABLE 1 BOUNDARY CONDITIONS FOR DIFFERENT INTERFACES IN THE MODEL

Symbol Boundary Condition Γ A Anode nJ=J eA

ˆ⋅r

Γ s Electrolyte in contact with the pipeline (or any other metallic structure)

)nf(J=VV meˆ⋅−

Γ j Ground plane 0=Ve

Lateral surfaces (bounding box of the model) 0ˆ =nJ e ⋅

Bottom surface Either “infinite-wall” condition or “non-

conductive” wall 0ˆ =nJ e ⋅

Foreign electrified structures Either eV or nJ eˆ⋅ is known

Interface between different types of electrolyte Continuity of eV and nJ eˆ⋅ is enforced

Interior problem. The interior problem is also ruled by the conductivity equation, and in case of homogeneous materials involved in the structure, it reduces to the Laplace equation (6).

( ) 0=Vk m∇−⋅∇ , (6)

where subindex m indicates metal. The following assumptions are valid for the metallic pipeline:

• em kk >>

• pp DL >>

• Vm is independent of the angular coordinate in the pipe section Therefore, the conductivity equation can be simplified into the following 1D Poisson equation:

∫∂

⋅−∂

∂

A

e2

mmp dCnJ=

z'

VkA ˆ

2

(7)

where Ap is the area of the cross sectional surface of the pipeline, z’ is the local normal to the cross

section in the pipeline, and C is the path surrounding the cross section (see Figure 5).

Ap

z’

C

FIGURE 5 - Tube element for modelling pipelines

Matching conditions. The equations coming from the exterior (electrolyte) and interior (metal) regions are coupled together by considering the usual matching condition equations throughout their common interface. These are the continuity of normal current density given in eq. (9),

( ) 0ˆ =nJ+J me ⋅ (8)

and the potential difference between metal and electrolyte across the thin layer near the electrodes given by the polarisation characteristics of the materials involved (i.e. polarisation curve):

)nf(J=VV eemˆ⋅− (9)

Solution Approach The Boundary Element Method (BEM)(4) has been widely used to solve Laplacian equations and in particular to simulate cathodic protection systems for underground and offshore structures(5,6,7). The most significant advantages of the method are first that the formulation is based on the fundamental

solution of the leading partial differential operator in the governing equation, and second that it requires only mesh discretisation on the boundaries of the problem. The former aspect confers high accuracy, while the latter substantially simplifies the pre-processing stage of the model, since volume discretisation is not needed.

The standard BEM is traditionally aimed at solving homogeneous electrolytes. In case of non-homogeneous conditions it is common practice to combine BEM with domain decomposition or multi-region (MR) technique. In this way the integration domain (electrolyte), considered as piecewise homogeneous, is represented as a collection of sub-regions, each one of them with homogeneous conductivity. Then, neighbouring regions are connected to each other by prescribing continuity of potential and normal current density through the boundary elements in the common interface.

The electrolyte consists of the soil surrounding the pipeline network, and represents the integration domain for the exterior problem. This domain can be considered as stratified in the vertical direction, involving a collection of thin layers of different thickness and conductivity. By “thin”, we mean that the thickness in the vertical direction (h) is much smaller than its characteristic horizontal extension (L) as shown in Fig 6.

FIGURE 6 – Layered soil structure. The light blue line on the right represents an arbitrary pipeline network

The solution of stratified media with a multi-domain approach involves discretising all the surfaces in between soil layers into a number of boundary elements. The size of the element must be at least a fraction of the thickness h. Each boundary element in the interface between layers adds two degrees of freedom to the final system of equations. Hence, the scale of models which can be solved with this approach is very limited due to the high computational cost. In order to avoid the discretisation between different soil layers, a new multi-layer technique for computing accurately and efficiently 3D problems has been recently developed and tested in the BEASY software.

The idea behind the multi-layer method is that the stratified nature of the medium is packaged into the corresponding Green’s function. In other words, the BEM is applied in the same way as in the case of the homogeneous electrolyte, except that the Green’s function for the homogeneous Laplace equation

given by ( )r 4/1 π is replaced by the multi-layer Green’s function given by:

∑= +−

=exp

1 4

1),,,(

N

k ijji

ijml

m

jik

nmGgxx

xxα

π (5)

where x denotes the 3D coordinates, the sub indices i and j stand for the source and field point,

respectively; m and l indicate the layer of the source and field points, respectively; ijmlα is a weight

coefficient and ijmlg denotes a displacement vector. The Green’s function written in this way can be

regarded as the one produced by a weighted method of images. These images reflect the stratified nature of the medium. The calculation of the weight and displacement vectors goes beyond the scope of this paper and can be derived from earlier works(8,9) and references therein. Finally, the Green’s function (5) replaces the 1/r kernel used for homogeneous regions, and the same BEM strategy can be employed. The advantage of the multi-layer kernel over the traditional multi-region approach is that the number of

degrees of freedom in the final system of equations is independent of the number of layers representing the soil, and depends only on the number of elements assigned to the pipeline and the anodic beds. Implementation Strategy The modelling system consists of several simulation tools sharing common numerical resources. The main components of the modelling system are: a pre-processor, main solver, calibration tool, a forward analysis tool, and a reverse analysis tool. The software implementation is based on a modification of the BEASY CP software where various modules have been modified to provide the necessary computational components. The pre-processor acts as an interpreter between the field data and the inputs of the model. The inputs of the model are listed as follows:

• Terrain properties (number of layers, electrical resistivity and vertical thickness)

• Pipeline location and geometry (x,y,z coordinates of points describing the path of the pipelines)

• Location and arrangement of anodes (geometrical description of the anodes)

• External circuit interconnecting the anodes and the pipeline (details of electrical components participating in the external circuit)

• Location of connection nodes in the pipeline

• Voltage or current supplied to the system (rectifier)

• Polarisation curves of the material structure in the electrolyte and their expected seasonal variations (these depend on the pipeline-to-soil material properties)

• Location of monitoring sensors (x,y,z coordinates of monitoring points)

• Sensor readings (voltage-to-pipe readings of each sensor) The outcomes of the simulation tool are:

• Potential ON and OFF at any point in the soil

• Overpotential at any point along the pipeline

• Distribution of normal currents flowing to the pipeline

• Distribution of coating breakdown factor along the pipeline

CASE STUDY

The concepts described above have been applied to a gas pipeline installed in the Northern part of Lisbon (Portugal). The pipeline spans approximately 32 km, from the city of Alenquer to Loures, as shown in Figure 7. The pipeline specifications relevant to the modelling are as follows:

• Pipe type: 12" API 5L X52

• Wall thickness: 7.9 mm

• Steel resistivity r = 0.16 Ohms m/mm2

• Electrical cross resistance: 14,7366 Ohms x Km

• Coating: high-density polyethylene (2.5 mm thickness) The modelling tool can handle both linear and non-linear polarisation curves. However, the polarisation data corresponding to this particular pipeline-soil configuration was not available at the moment, and therefore a simple linear relationship was assumed from previous experience obtained with similar steels. The overpotential at zero current was assumed to be -700 mV. Then, the overpotential at -1200 mV was corresponded to a normal current density of 40 mA/m2. On the other hand, the electrical conductivity in the region varies substantially from point to point. And an accurate description of the soil conductivity was not available at the moment. Hence, a two-layer model was assumed (see Table 2). One anodic bed consisting of three anodes (Titanium MMO 25×1000 mm) delivering a total constant current of 5A was assumed to be located close to the point indicated as GMRS 1219 in Figure 7 (i.e. about 100 m away from the pipeline endpoint).

TABLE 2 SOIL PROPERTIES

Conductivity [S/m] Thickness [m] 1E-3 from z = 0 to z = -50m 1e-7 from z = -50m to z = -300m

It is important to stress the fact that, once calibrated, the reverse modelling tool will always try to find the set of input data which produces outputs that best match the observed values coming from the sensors distributed across the field.

FIGURE 7 - Layout of the gas pipeline in case study 2. This image was reconstructed by overlaying the data provided by ISQ over a satellite image from Google Earth mapping system

MODEL CALIBRATION

In order to define a baseline for the coating breakdown factor and to adjust the unknown soil conductivity, the modelling tool was calibrated against previously available survey data. In this case, a uniform breakdown factor has been used, and the result of the search was BF = 0.00305 out of 1. To clarify, BF=1 indicates bare steel or completely removed coating while BF = 0 indicates that the coating acts as a perfect insulator. Figure 8 shows a comparison between potential ON and OFF measured during a survey, and the modelling results after adjusting the overall breakdown factor and soil conductivity in the first layer. The test was conducted between km 8 and 32 in the pipeline. The adjustment corresponds to the case BF = 0.00305.

The soil conductivity in the first layer was also allowed to vary in view of the high uncertainty of this variable as input data. The result found by the modelling tool modified the original uniform input conductivity from 1E-3 S/m to 7.5E-4 S/m. The difference between ON and OFF potential shown as

∆V = 250 mV in the figure is due to the IR drop in the soil. This difference remains almost independent of the breakdown factor associated to the pipeline, which instead is responsible for shifting vertically both VON and VOFF at the same time. Figure 9 shows the distribution of normal current along the pipeline predicted by the modelling tool after the calibration with BF and soil conductivity.

FIGURE 8 - Comparison of On and Off potentials measured along the pipeline at ground level from the field against the values obtained by simulation.

FIGURE 9 - Normal current density distribution along the pipeline

Figure 9 shows the distribution of normal current along the pipeline obtained by simulation in this particular configuration. The reverse tool will always find a match between. Finally, Figure 10 shows the horizontal distribution of the ON Potential along the pipeline. The blue region corresponds to an ON potential of -5.3 V. The ON potential becomes more positive as the measuring point gets closer to the pipeline, to approximately an average value of -1450mV in the imaginary path above the pipeline on the ground level.

FIGURE 10 - Aerial view of On potential distribution and the direction of the sub-surface current density vector. This image was reconstructed by overlaying modelling results over a

satellite image from Google Earth mapping system

CONCLUSIONS

The vast majority of the monitoring of pipeline CP systems performed currently in Portugal and in Europe requires a periodic visit of an operator to the test posts in order to take readings. This approach is usually time consuming, resource demanding, and it cannot be conducted on a day by day basis. The remote monitoring system combined with a reverse modelling simulation tool presents the following advantages:

• This system allows pipeline operators to quantitatively know the levels of protection on their pipelines without the need for personnel displacement for field surveys;

• The system provides a real time display of the protection levels of all their test posts. This system would have the advantage of allowing the following functions: On-line monitoring of relevant data including potential and current data.

• In addition, the system allows for immediate detection of troubleshooting; for example after a thunderstorm, theft of the test post, interference with other structures installed in the same corridor, etc.

• With this system, the operator has the possibility to call the CP station at any time in order to read the data and to predict the status of the pipeline using the computer model. In addition he will be able to implement immediate corrective measures remotely.

• Incomplete data can be recomposed by means of the reverse modelling tool.

REFERENCES

1. J.A. Nelder and R. Mead, Computer Journal, 1965, vol 7, pp 308-313

2. K.I.M. McKinnon. Convergence of the Nelder-Mead simplex method to a non-stationary point, SIAM J Optimization, 1999, vol 9, pp148-158

3. Chenchen Qiu. “Model Interpretation of pipeline survey data”. PhD Thesis. University of Florida, US. 2003

4. C.A. Brebbia, J.C.F. Telles, L.C. Wrobel. “Boundary Element Techniques”. Springer Verlag. Berlin Heidelberg, NY, Tokyo. 1984

5. D. P. Riemer, M. E. Orazem, “Modelling Coating Flaws with Non-Linear Polarization Curves for Long Pipelines,” in Corrosion and Cathodic Protection Modelling and Simulation, Volume 12 of Advances in Boundary Elements, R. A. Adey, editor, WIT press, Southampton, 2005, 225-259.

6. D. P. Riemer, M. E. Orazem, “Application of Boundary Element Models to Predict the Effectiveness of Coupons for Accessing Cathodic Protection of Buried Structures,” Corrosion, 56 (2000) 794-800.

7. R.A. Adey, J. Baynham. Design and optimization of cathodic protection systems using computer simulation. CORROSION 2000, Paper\723. Houston, Texas. NACE Int., 2000.

8. H. Ymeri, B. Nauwelaers, K. Maex. Computation of conductance and capacitance for IC interconnects on a General Lossy Multilayer substrate. Active and Passive Elec. Comp. Vol 24. pp 87-114, 2001

9. T. Smedes, N.P. van der Mejis, A.J. van Gendered. Boundary Element methods for 3D capacitance and substrate resistance calculations in inhomogeneous media in a VLSI layout verification package. Advances in Engineering Software 20. pp 19-27. 1994.

APPENDIX A. HARDWARE - CHARACTERISTICS OF THE REMOTE MONITORING STATION

The main server can run on a Pentium II 400 or above PC, with at least 64MB Ram and at least 10 MB of space available in the hard disk. The system uses a GSM/GPRS modem which connects to a standard USB Gate. The hardware electronics for data acquisition and transmission developed by ISQ has the following characteristics: Protocol: GSM/GPRS Acquisition: 8Analogic input channels with a rage of 0-200V (hardware configurable) and protected of spikes over 15000V 2 Analogic output 8I and 8O Digital channels USB and RS232 gates to communicate in local place Real Time Clock to control the wakeup of the Station GPS and Inertial Sensor for security reasons. The sensor in the remote post will issue a warning signal if the post is moved or tumbled. Power supply: Internal battery for the Station (Duration = 3 years in normal use operation)

APPENDIX B. LIST OF SYMBOLS Symbol Description

W Objective function Vi Potential measured from the field at point i vi Potential obtained by modelling at point i Nop Number of observation points (x,y,z) 3D coordinates of points Ve, Vm Potential in the electrolyte (e) and metal (m) J, Je, Jm Current density, current density in electrolyte, current density in metal ke Electrical conductivity of the electrolyte

∇ Gradient operator

Γ , Ω Boundary, Integration domain

n Unitary normal to the boundary surface

hI Thickness of i-th soil layer G Green’s function of conductivity equation in multi-layer media BF Coating Breakdown factor