A TECHNIQUE FOR THE ASSESSMENT OF PIPELINE INTERNAL CORROSION BY THE MEASUREMENT OF CLEANING PIG VIBRATION

D A Russell and B Snodgrass Weatherford Pipeline & Specialty Services

Unit 3, Newhailes Industrial Estate Edinburgh, UK EH21 6SY

Dr J Lawson

ChevronTexaco Upstream Europe Seafield House

Hill of Rubislaw Aberdeen, UK AB15 6XL

ABSTRACT

The use of Intelligent Pigs to survey pipelines as part of pipeline integrity management has become almost routine in the oil and gas industry. The now mature pipeline infrastructures of the world, together with more prescriptive regulatory regimes, is likely to lead to increasing needs of such inspections with the attendant increase in demand for methods of economical inspection to provide corrosion data for those lines that currently may not be accessible using conventional intelligent pigs.

A new industry sponsored study in the form of a Joint Industry Project has been undertaken aimed at demonstrating that the presence of internal corrosion in pipelines induces measurable and identifiable vibration in a carrier pig as it passes through the line. The study is based on the conjecture that it is possible to characterize pig vibrations in such a way as to differentiate between pipe that is, for example, corroded, un-corroded or may have wax present. The project saw the design and development of a new pig-mounted device for measuring and recording vibration. The theory developed into a series of trials in which the pig was passed through test loops and operational pipelines with known corrosion defects, thus allowing consideration to be given to the effects of line diameter, pig speed and pig design in interpreting the vibration measured. The results to date have demonstrated that it is indeed possible to differentiate reliably between line characteristics and conditions while pigging with water or liquid product.

In this paper the authors discuss the principles of the project, the design of the pig-mounted vibration sensor and the constraints that the carrier pig design places on the capabilities of this new technology. Results to date and the ongoing field trials in operational pipelines in the North Sea are reviewed. Successful completion of these trials will offer operators an effective alternative to IP deployment for assessing the suitability of pipelines for continued service. Keywords: inspection, integrity management, pipelines, intelligent pigging.

INTRODUCTION

The importance of the role that pipelines play in getting oil and gas to each of the stages between the reservoir and point of sale cannot be overemphasised. Such pipeline systems may be offshore or onshore; they exist pre or post separation and also on both sides of the refining process. Pipelines are the main artery by which this inter-process transport occurs. Whilst a limited volume of product can be transported by tanker on both land and sea, interruption to flow because of failure in a pipeline system or one of its components, such as a valve or flange, incurs highly significant losses. In addition, and more importantly, the potential in terms of safety and the environment can range from challenging to disastrous. Pipeline integrity is therefore a key operational issue. Whether a pipeline is offshore or onshore, it is likely that it will be buried either under the seabed or simply buried if onshore. In either case, assessing the external condition will be inconvenient, expensive and time consuming. Also, in either case, assessing the internal condition will be all of these but, additionally, in many instances, virtually impossible. Clearly, condition assessment and management are the principal means by which pipeline system failure can be prevented, hopefully by early identification and prevention. Access to the bore of a pipeline will enable the identification of a wide range of information and, in many cases, will permit assessment of the external condition of many of the parameters that have potential to threaten integrity. From an internal condition perspective, access to the bore will provide data on:-

Wall thickness Wall pitting due to corrosion General corroded areas Wax Scale Geometry Weld condition Cracks longitudinal and circumferential

It is true that many, if not all of these parameters could be determined from internal access.

Traditionally, the most expeditious means of measurement and assessment has been by mounting equipment on a series (or train) of pigs which are launched at one end of a pipeline and recovered at the other. Whilst this in itself poses many challenges, the

real trick is in analysing and interpreting the recovered data with a great amount of emphasis being put on the skill of interpretation. Several good technologies have already been established, including magnetic flux leakage and ultrasonic inspection utilising a carrier pig. The exponential growth in digital storage and the ever increasing battery life that supports digitisation of data have significantly improved that science. Nevertheless, current technology requires a multi-module approach to data acquisition. The adaptation of modern physics must surely have potential. There is therefore a real need for research into and development of new methods of identifying changes to the geometry, condition or surface contour of pipelines. The measurement of vibrational characteristics of a pig is such an emerging technology which, at worst, might give an indication of the occurrence of changes to the internal surface of a pipeline. During the course of operations with instrumented (smart) pigs it has been remarked that the signals recorded for vibration often show anomalous readings, particularly at relatively low frequencies (up to a few hundred Hertz). The nature of these anomalies is typically a local increase in the recorded vibration level over distances from a few meters up to a few hundreds of meters. Very often these anomalies remain the same from inspection run to inspection run over a time interval of months or years. From the earliest observations of this behavior, it has been speculated that the cause was the interaction between the pipe-wall and the pig disks, and that the anomalously high signals were due to some change in this interaction caused by local differences in the internal condition of the pipe-line.

CONCEPT AND PRINCIPLES OF OPERATION

It was considered that the use of vibration data acquired from a cleaning pig as the basis for inspecting and monitoring the condition of a pipeline would potentially allow the operator to monitor internal pipeline wall condition as the pipeline is being cleaned. This would be a radical departure from traditional inspection methods. The approach being suggested here can be summarized as using:

"the dynamic response of a cleaning pig to changes in the condition of a pipeline as a measure of that change"

Current approaches to pipeline inspection involve introducing a direct sensing device into the pipeline. This sensing device can then be used to provide a measure of for example, weld positions, a dent or internal corrosion. Sensors used in other sectors of the industry, such as calipers and ultrasonic probes have been directly applied to pipeline inspection. The developers of these tools look to the pig simply as the means of deploying these sensors into the pipeline. This has resulted in a variety of tools being developed ranging from the earliest electro-mechanical Caliper Pigs through to the state-of-the-art second generation Intelligent Pigs1,2,3. When considering the behavior of the carrier pig itself, the developers of these tools have tended to look upon this as a problem potentially compromising the quality of data from whatever sensor is being deployed. Pig behavior has not been looked upon as a valuable source of information in its own right. The tools that have evolved are

effectively NDT machines, generating vast quantities of data that require extensive data processing in order to extract results. Although these devices have arguably become reasonably accurate and reliable at detecting features, their use can sometimes fail to be either economically and/or technically feasible. As a result there are many lines in production in the North Sea and elsewhere where an Intelligent Pig inspection either cannot be justified or presents insurmountable technical difficulties. A further factor worth noting is that an inspection using an Intelligent Pig is only a 'snap-shot' in time. This means that when a survey is carried out and the condition established using codes such as ANSI B31G or RSTRENG, only very approximate assessments of future condition can be made using assumed corrosion rates4. This approach is subject to error, as future corrosion rates may vary with changes in fluid properties, field conditions, pipeline shape etc. There are presently restricted means of monitoring the internal condition continuously along the length of a pipeline between Intelligent Pig surveys. The method discussed here is fundamentally different to this traditional approach5,6. Rather than looking to deploy a sensor that directly measures wall thickness or the size of a dent, it instead treats the cleaning pig itself as the sensor. This idea arises as a result of observations made during ongoing test work on standard cleaning pigs. These observations suggest that the dynamic behavior of a cleaning pig should not change significantly unless the environment through which it is traveling is changing. Specifically it has been found that a pig supports a given differential pressure, generates a certain amount of noise and vibrates in a particular way unless the environment has changed, say, due to the presence of a dent, debris or internal corrosion. This suggests that instrumentation could then be fitted to the pig to monitor its dynamic behavior and look for these changes. Where these changes occur the data could then be analyzed and the cause identified.

VIBRATION SENSOR DESIGN

Previous smart utility pigs captured data at low rates recording averaged signal amplitudes across a (small) number of frequency bands. This process is equivalent to recording a very crude and approximate power spectrum using only a few frequency 'buckets'. Early prototype test work at laboratory scale suggested that significant amounts of detail in the response of the pig motion along the pipe were being lost in this process. A set of simple tests capturing the entire response of a pig at a sample rate of 30 KHz was performed. This revealed that as expected there was a fairly complex interaction between the vibration modes of the pig and the excitation caused by the pig motion along the line. Figure 1 illustrates the type of data that was acquired during the course of these tests. These tests were carried out using a modified version of a then standard current smart utility pig. In normal operation, accelerometers are used to measure the vibration. The frequency response of these instruments had been selected to give a stable flat response near DC. The response was known to resonate at around 1500-2000 Hz and to tail-off rapidly above this. For these reasons a design exercise to select a suitable accelerometer was required. Given that for this application the DC response of the

instrument was not critical, no especial difficulty was experienced in sourcing an accelerometer (Endevco 'Isotron') with a near-linear response from under 10Hz out to 50KHz. The region of interest was seen to lie in the excitation of higher modes of vibration of the pig in the corroded test-spool, presumably as a consequence of the interaction between the pig-disks and the roughened region of the pipe wall. In this initial test it is the presence of a peak at around 250Hz that is of interest. This peak was only observed to occur when testing with a corroded spool.

TEST LOOP DESIGN AND SPECIFICATION

General Test Loop Specification

The trials were conducted in a 10'' test facility (Figures 2, 3). The loop, which allows continuous pigging, includes a removable 6m spool-piece intended for use in trials such as these. The motive fluid is water driven round the loop by an electric pump. The flow-rate is controlled by regulating the pump rotation speed using an inverter. Flow rate can be controlled by this means between 0.1 and 1.2 m/s. The loop operates as two separate flow-paths, divided by a pair of ball valves. Pig-signaling devices are used to control the valves and maintain a near constant pigging velocity round the loop. The loop is 10" ID steel pipe, connected by flanged joints. The 90 bends are 5D in radius. The loop is designed to permit 6m long test sections to be placed immediately before the first of the bends. Test Spool Preparation

For the purpose of testing the prototype corrosion detection tool, it was necessary to define a range of test spools covering the range of conditions the tool was intended to detect. The following list was constructed:

Uncorroded Spool (control) Slight General Corrosion Spool (0.5 1.0 mm of metal loss across entire

internal surface) Severe General Corrosion Spool (5 mm of metal loss across entire internal

surface) Slight Channel Corrosion (1 mm of metal loss in a channel along the bottom

of the pipe. Severe Channel Corrosion (5mm of metal loss in a channel along the bottom

of the pipe. In order to achieve the specified corrosion of the test-pieces, a method for accelerating the progress of the corrosion process was required. Five identical spools of 10" diameter and 9.7mm WT were prepared. One was used, unaltered as the 'uncorroded' or control spool. Two others were propped at an angle of approximately 45 and filled with an electrolyte. The remaining two spools were kept horizontal and partially filled with

the electrolyte. A current was impressed through the metal resulting in a greatly increased rate of corrosion. Figures 4 and 5 illustrate this procedure. This method was only a partial success, with the corrosion rates being rather slower than predicted. This, of course, was not a problem in the case of the 'Uncorroded' test spool. The nominal specification was as indicated above, however, measurements on this spool suggest that the actual WT was only 8.9mm. For the 'Slight General Corrosion' test spool the metal loss achieved was at the bottom of the specified range, approximately 0.5mm average wall loss. The corrosion is unevenly spread with most metal loss at the bottom of the spool and furthest away from the upstream end of the spool. Figure 6 shows a contour representation of the loss achieved. For the 'Severe General Corrosion' spool, initial attempts to accelerate corrosion by the impressed current method using a length of 9.3mm WT pipe did not provide the necessary corrosion rate. For this reason a length of 6.3mm WT was used, and this was subsequently corroded by the impressed current method. An approximate metal loss of 0.6mm was achieved. Overall this was equivalent to a 3.6mm metal loss compared to the uncorroded spool. This was not particularly evenly distributed across the spool. Figure 6 shows that the first (1.5m) of the spool has substantially less loss, as does the left-hand side of the spool (between 12 Oclock and 6 Oclock). This is attributed to the 45 angle that the spool was raised to leaving a significant part of the upper internal surface of the pipe-spool dry during the process. For the 'Slight Channel Corrosion' the acceleration of corrosion by the impressed current and crevice method using a length of 9.3mm WT pipe was reasonably successful. Metal loss was created varying between 0.5 and 1.0mm in a very irregular channel varying between 20 and 80 wide. The deepest continuous channel was around the 7:30 position in the configuration in which the spool was eventually tested. Note that from Figure 6, this spool is rather closer to the Slight General Corrosion Spool in metal loss than would ideally have been the case. For the 'Severe Channel Corrosion' spool, again, initial attempts with the impressed current and crevice method using a length of 9.3mm WT pipe did not provide the necessary corrosion rate. For this reason the channel was fabricated by removing a section of the pipe and then welding a section of thinner walled pipe in its place. The channel portion of this pipe was then subsequently corroded by the impressed current method. This produced a spool with approximately 3.4mm metal loss in a channel 60 wide centered on the 6 o'clock position. Figure 7 shows a photograph of the inside of the spool. Figure 6 summarizes the metal loss achieved in each of the four (corroded) test spools. The contours are 0.1mm apart and represent the total metal loss achieved around the pipe. Measurements were made at eight locations along each spool and at eight positions around the pipeline (the 0130 and 1030 measurements were omitted for the channel corrosion spools). Radial positions are based on looking from the upstream of the test spool; distance is likewise from this end.

THE CARRIER PIG

A standard bi-directional pig design was used, as shown in Figure 8. This pig was made as close as possible in dimensions to a normal 10" pig. Of course, the pig itself was non-standard in that the flanged metal body was constructed as a pressure vessel suitable for carrying the instrumentation through the pipeline. In order to be able to test the response of the instrumentation to variations in pig configuration, a number of different disk sizes and pig configurations were tested. These included under-sized and over-sized sealing disks, thinner/thicker sealing disks, under-sized guide disks thin guide disks, and longer and shorter pig bodies. Table 1 summarizes the configurations tested.

TEST METHODOLOGY

The prototype tool collects data at two different sampling frequencies. The pressure, temperature and positioning data are recorded at a frequency of approximately 24Hz. The vibration sensor during these tests was sampled at approximately 31 kHz, and digitally anti-aliased at around 12 kHz, in order to suppress spurious effects from high frequencies. The slow (24 Hz) data was intended only to provide the same general pipeline data as the previous generation of the tool. In particular this data was used to track the passage of the pig through the test loop and establish the locations of the test-section in the data. The figures presented here are generated by performing a Discrete Fourier Transform (DFT) on a moving time window in the vibration data across the test section. A window of 8192 time-series values was used, each window overlapping the previous by 7168 samples. A Hamming window was used, giving more weight to samples close to the centre of the window. Each window represents a length of 10 cm at the slowest speed, rising to 30cm at the fastest speed. This calculation results in a set of frequency values at a range of times that can be used to represent the data. Once these spectrograms had been generated, the general form of the results was used to guide the development of tools to characterize the data from each of the test spools. The largest differences between tests were seen to lie in the frequency range under 500 Hz. All of the spools showed significant vibration below about 75 Hz, and all had a strong response around 350 400 Hz. For this reason, the frequency band between 75 and 300 Hz was used to characterize each test. For each pass through the test spool the percentage of power values in the spectrogram above a certain threshold value were calculated. This calculation was repeated for threshold values from 75dB up 120dB to create a simple 2-D plot of the response of the tool in each test section for each lap. This also provided an objective way in which to assess the repeatability of the tests. For most of the tests each lap has a relatively tight group of results for a given speed and threshold values. Table 2 lists the tests performed. Each test was repeated ten times at each of three speeds (0.4, 0.8 and 1.2 m/s)

TEST RESULTS

General Comments

Figure 9 shows spectrograms for a sample loop from Test 1. These are false color images of the frequency spectra described above. These values are in dB (20log10(Spectral Power)). The uppermost image shows the response for the entire lap across the entire frequency range. The test spool is at the far left hand side, ending at around 7 seconds into the loop. Clear events on this scale include the responses at the bends, where there is very little higher frequency vibration (7-15 seconds and 33 42 seconds). In both cases the response through the valve can be differentiated in the middle of the bend. The regions with very little response at all at 18 and 45 seconds are the locations where the valves change over and the pig momentarily loses drive pressure. A set of clear harmonic responses is visible in the straight sections of pipe, with some sections having more harmonics visible than others. The corroded test section has rather broader lines, and a wide response, in some locations out to around 5 kHz. Below this is an image showing only the low frequency response (under 500 Hz), again for the whole loop. Here the clearest feature is a strong peak at between 350 and 400 Hz, present in all of the straight sections of pipe. This represents the whistling noise made by the pig as it goes round the loop, and the frequency is believed to be more characteristic of the test-loop than the pig. It is also this resonance that generates the harmonics visible. The presence of a wide band response in the test-spool that is not present elsewhere is very clear in this figure, extending out to relatively high frequencies. Finally the last image shows the whole frequency response for the test-spool only. Again the pattern of resonances out to about 1200 Hz can be seen, together with the broad response. The responses right out to 5 kHz that can be seen near the beginning and end of the plot are the pig disks hitting the flanges at either end of the test-spool. Figures 10, 11 and 12 show a comparison of the detailed responses for three repetitions of the test in the "Severe General Corrosion" and "Uncorroded" test spools. These represent the Medium, Slow and Fast speeds respectively (0.8, 0.4 and 1.2 m/s approximately). All of these are to the same scale. Clear in all three of these plots is a region of approximately a quarter of the length of the spool at the start that has relatively little vibration. Examination of the test-spool has shown that this is related to the relative levels of corrosion in the pipe, due to the way in which the corrosion was generated. It is in fact the case that approximately the first 1.5m of the spool has only relatively light corrosion. Generally, the faster the pig was traveling the higher the vibration levels. On the other hand, the responses for the 'Uncorroded' spool show no significant signal in this case at any velocity. Based on these results, it was decided that a statistical analysis of the frequency region on these plots between 75 and 300 Hz was the best way to characterize the pig response in a relatively simple way. The lower frequency was chosen to exclude the 50 Hz response which is sometimes present in the uncorroded spool, and the upper

frequency to avoid the large peak usually present in all tests between 350 and 400 Hz. The technique used was to estimate the area on the spectrogram that lies above a given amplitude level. Overall the outcome of the tests was encouraging. The "Severe General Corrosion" spool has the highest response for almost all of the pig configurations and speeds. The exceptions are related to the use of pig that are 'marginal' in their ability to navigate the loop, where quite severe broad spectrum vibration could be experienced independent of the current test spool. All of the pig configurations show quite significant dependence on the pigging velocity, suggesting that knowledge of the velocity of the pig may play an important part in using this new technology in the field. There are also fairly significant differences in the response between the various pig configurations. It is clear, for example, that the seal diameter has a major effect on the vibration response of the pig. The outcome of the loop tests suggests that the smaller the seal oversize, the larger the difference in response between corroded and uncorroded test spools. Tests were performed in the "severe general", "sever channel" and "uncorroded" spools with a variety of pig configurations. A total of 10 configurations were used, as listed in the test matrix (Table 1), but of these, the configuration with thin seals was unable to get successfully transit the bends in test loop, and these tests were omitted from the program. Figures 13, 14 and 15 show a comparison of the responses for the "severe general corrosion" and "uncorroded" spools for each of the pig configurations at each of the three velocities tested. For the corroded spool at the medium and fast velocities the lines are grouped together quite tightly, indicating relatively little dependence on the pig configuration. This is particularly the case for the fast velocity. The only exceptions to this are the pig with the large spacer diameter, and the heavy pig, both of which show less response.

At slower speeds, the range or responses is much wider, although the large spacer pig still had the lowest response. Overall the pig with the small diameter seals gives the highest response.

The control spool shows the opposite behavior to the corroded spools, with the responses being tightly grouped at the lowest velocity, becoming widely spread at the highest. This is particularly true for the large seal pig, where the response is highly dependent on the speed, being greater than that found in the Severe General corrosion spool at the highest velocity. Work on the data with commercially available pattern-recognition software suggests that the use of a zero-crossing related method7 can distinguish between the 'severely', 'slightly' and 'uncorroded' spool with approximately 97% accuracy for a given velocity. The method used is to divide the data into a number of 'epochs' based on where the signal crosses zero. Each epoch is then given a code dependent upon its length (number of samples) and its complexity (number of extrema). The frequency with which pairs of codes occur constitutes a 'fingerprint' for the line condition. In

principle this fingerprint is independent of the pig velocity, and in fact not taking account of speed only reduces the accuracy achieved in recognizing spools by approximately 5%. Figure 16 shows fingerprints for three of the test spools.

FIELD TRIALS

Following the successful completion of the (first phase) of test-loop trials, a number of pipelines were identified as potential locations for field trials. The criteria employed were principally that recent intelligent pig data should be available to the project, and that as far as possible the pipeline conditions should reflect 'typical' values in at least some aspects. The first field trials were performed in a 16" offshore oil export line. This line produces a relatively low rate, equivalent to a pigging speed of only 0.15 0.20 m/s. (The line fluid is crude oil). This line was considered especially suitable for field trials, since it was known to have experienced very severe corrosion problems in the past, and a substantial part of the line had recently been replaced. The expectation was that the replacement line laid at this time would still be in a non-corroded state, while the ends of the line, which were still the original pipe, were known to have many thousands of defects from an IP survey performed immediately prior to the pipeline repair. This section covered approximately the first and last 5km of the pipeline. Unfortunately the prototype nature of the tool leads to certain limitations in the field situation. In particular, the test unit has power for only 27 hours of operation. The slow pigging speed (0.17 m/s) achievable in this line, in combination with the relatively short endurance available, meant that only the first 16km or so of the line could be inspected. This was considered acceptable as giving a satisfactory length of both the old and new pipe-work. This field trial was performed in 2003, and data was successfully gathered using essentially the same electronics platform as had been used in the loop-tests. Figure 17 shows a comparison between a spectrogram of a section of the new line and a section of the original line. The section of the pipeline that was surveyed was divided into pipe-joints, using the (very noticeable) indications of weld location in the vibration record. Each pipe-joint was characterized as uncorroded, slightly corroded or severely corroded using the vibration data. This classification was intended to correspond to the three corrosion classes in the loop test in terms of instrument response. It is clear that this type of classification cannot provide a quantitative measure of metal loss, and the implication is not that the test-loop and field-trial classifications are similar in value. For the pipeline under consideration, of course, almost all of the line could be considered to fall into either the 'no corrosion' or the 'severe corrosion' classification and this was reflected in the vibration data. Work has continued on more representative field trials, and results from these will be used to determine whether the technique is capable of discriminating between more subtle differences in internal pipeline condition. Some initial work with pattern recognition techniques suggests

that the presence of moderate quantities of loose debris in the pipeline can be distinguished from corrosion by differences in the vibration characteristics, although, of course, where large amounts are involved, these may disguise the underlying condition.

CONCLUSIONS

Pig vibration has been shown to be characteristic of pig disk-pipe wall interactions, at least in part. The effects of other parameters, such as pig speed, size and weight have also been considered. The effect of disk size has been shown to be of second order, but velocity effects can be significant, particularly in the amplitude of the effect. No consideration has been given directly at this stage to effects of fluid properties. This behavior is in line with theoretical studies of pig vibration modes8. This modeling work suggests that a number of bands of resonant frequencies exist. For these test, the lowest two bands seem to be of relevance. The lower is centered in the tens of hertz, and the second at around 150 to 200 Hz. The precise detail of disk oversize, stiffness etc has been shown to have a relatively small effect on these resonances. The field trial (16") and loop-trials (10") confirm these approximate frequency bands, and suggest that dependence on line diameter is also slow. The effects of velocity seem largely to be on amplitude rather than the frequency. For this reason the indications are that a suitable pattern recognition technique will be able to distinguish these effects from genuine pipeline condition changes. The test data from both sets of tests supports the idea that the increased surface roughness of the corroded pipe wall provides much more energy to the pig vibration, increasing the amount of the higher order vibration present in the frequency spectrum. Indications from the field trials are that the effects of fluid properties mean that it is probably not the case that a single fingerprint for corroded line vibration will emerge that is valid in all situations. Rather each pipeline will probably require its own baseline, against which changes may be monitored.

REFERENCES

1. J. Cordell, H Vanzant, All About Pigging, On-stream Systems Ltd, UK, 1999 2. A. Crouch, R. Anglisan, M. Jaarah, Quantitative Field Evaluation of Magnetic

Flux Leakage and Ultrasonic In-line Inspection, Pipes and Pipelines International, 1996

3. H. Willems, O.A. Barbian, Crack detection in Pipelines by Advanced Intelligent Pigging, Pipeline Technology , Elsevier Science, Volume 1 pp 223 234, 1995

4. W.O. Oluoch, I. Skinner, Rehabilitation of the 450km Mombassa to Nairobi Refined Products Pipeline, Pipeline Rehabilitation and Maintenance Conference, Prague, 2000

5. G. Short, M. Fletcher, The Role of Smart Cleaning Pigs in Pipeline Rehabilitation and Repair, Pipeline Rehabilitation and Maintenance Conference, Prague, 2000

6. D. Russell, B. Snodgrass, G.H. Smith, The Smart Acquisition and Analysis Module for Pipeline Inspection, ISOPE 2000, Seattle, 2000

7. T.G. Clarkson, etal, Speaker identification for security systems using reinforcement-trained pRAM neural network architecture., IEEE Transactions on Systems, Man and Cybernetics, Feb 2001

8. G.H. Smith, Vibration Model of a Pipeline Pig, unpublished, 2002

ACKNOWLEDGEMENTS

The authors would like to acknowledge the support of BP, ChevronTexaco, Kerr-McGee, Marathon Oil, Petro-Canada, Shell U.K. Limited, Talisman and Total E&P UK plc during the course of this work.

Figure 1 - Early Prototype Vibration Response

Figure 2 - Test Loop Schematic

Figure 3 - Test Loop

Figure 4 - Corrosion Acceleration Apparatus Figure 5 - Accelerated Corrosion

Figure 6 - Corrosion Pattern in Test Spools

Figure 7 - Artificial Channel Corrosion

Figure 8 - Test Vehicle

Figure 9 - Spectrograms of Sample Loop

Figure 10 - Corroded vs. Uncorroded Response at 0.4m/s

Figure 11 - Corroded vs. Uncorroded Response at 0.8m/s

Figure 12 - Corroded vs Uncorroded Response at 1.2m/s

Figure 13 - Effect of Pig Configuration at 1.2m/s

Figure 14 - Effect of Pig Configuration at 0.8m/s

Figure 15 - Effect of Pig Configuration at 0.4m/s

Figure 16 - Zero-crossing Method Coded Signals

Figure 17 - 'New' Pipeline vs. ''Old' Pipeline Response

Table 1 - Pig Configurations for Testing

ID Variable

Comments Test values

A Seal diameter Relative to line size Standard, large, small

B Seal thickness This will effect the bending stiffness Standard, large, small

C Distance between seals

Tested with variable K Standard, large, small

D Guide diameter Relative to line size Standard, small E Guide thickness This will effect the bending stiffness Standard, small F Distance between

guides Tested with variable E Standard, small

G Spacer diameter This will effect the bending of the discs Standard, large H Weight Standard, heavy

Table 2 - Test Matrix Test No. Spool Pig 1 General Severe Standard 2 Uncorroded Standard 3 General Slight Standard 4 Channel Severe Standard 5 Channel Slight Standard 6 General Severe Small Seal 7 General Severe Large Seal 8 General Severe Thin Seal 9 General Severe Thick Seal 10 General Severe Small Guide 11 General Severe Thin Guide 12 General Severe Long 13 General Severe Large Spacer 14 General Severe Heavy 15 General Severe N/A 16 General Severe N/A 17 General Severe N/A 18 Channel Severe Small Seal 19 Channel Severe Large Seal 20 Channel Severe Thin Seal 21 Channel Severe Thick Seal 22 Channel Severe Small Guide 23 Channel Severe Thin Guide 24 Channel Severe Long 25 Channel Severe Large Spacer 26 Channel Severe Heavy 27 Uncorroded Small Seal 28 Uncorroded Large Seal 29 Uncorroded Thin Seal 30 Uncorroded Thick Seal 31 Uncorroded Small Guide 32 Uncorroded Thin Guide 33 Uncorroded Long 34 Uncorroded Large Spacer 35 Uncorroded Heavy

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