By Grant Coleman

Maintaining pipeline integrity is the key to cost effective operation of any pipeline system. Several nondestructive methods are available for pipeline inspection, each having their own applications, advantages, and limitations. However, when it comes to inline corrosion inspection, MFL technology is the most widely accepted and applied. The MFL measurement principle is based on the fact that when strongly magnetizing a steel tube using an internal magnet, some of the flux will leak out of the tube. Flaws in the tube that reduce the wall cross-section alter the leakage pattern. The shape of the flux leakage is dependent upon the defect’s geometry.

Figure 1: Shown is the Circumferential component of the complex MLF—magnetic flux leakage—signals simulated using the Vector Fields software. Magnetic simulation is reducing the time required to design MFL pipeline inspection tools.

BJ’s industry-unique Tri-Axial sensors are used to accurately determine defect parameters using MFL. The number and polarity of the peaks are the same for any corrosion defect; however, the shape and size of the signals change depending on the size of the defect. The axial signal has one positive peak with two smaller negative peaks, with the positive one between the two negative peaks. The radial signal has two peaks, with the positive one first and the negative one second. The circumferential signal has four peaks, two positive and two negative, in a rectangular-like direction (see Figure 1, above). To determine the severity of a defect, the length, width, and depth of the corrosion are calculated by measuring various parameters of the above three signals. These parameters include peak-to-peak amplitudes, signal widths, signal lengths, and bias levels. Neural network and statistical equations are applied to these parameters to determine the defect size.

BJ Pipeline Inspection Services is currently working with a major pipeline operator to inspect over 3,000 km of NPS 36 and NPS 42 mainline and NPS 4 to NPS 24 lateral piping over a two-year period. The pipeline operator required that the MFL tool used on the project provide high magnetic saturation levels to accurately detect and characterize anomalies in heavier wall pipe. Another requirement was that the tool and speed control system be designed to allow product to bypass the tool while maintaining the inspection speed at an optimum level.

The requirements that the tool saturate the pipe and provide maximum gas bypass are inherently conflicting. Saturating the pipe requires a large area of steel inside the body of the tool to provide a return path for the magnetic flux. On the other hand, achieving maximum gas bypass requires a large bore in the center of the tool.

The traditional approach to designing this tool would involve developing a concept design based on engineering rules of thumb and experience. A large artificial defect set is created by electrochemically machining defects into pipe spools, with typically 20 to 40 defects per spool piece. A prototype MFL tool based on the concept design is then tested in this defect set to determine its ability to generate the required magnetic flux levels and provide gas bypass. Nearly always, the initial prototype does not meet the requirements, so additional prototypes must be built and tested. Using this approach, MFL tool design is a long and expensive process.

To reduce the time required to design and calibrate MFL tools and improve their accuracy and performance, BJ engineers magnetically modeled the tools and defects to meet the pipeline’s requirements using OPERA-2D and OPERA-3D electromagnetic simulation software from Vector Fields. We selected these software packages to drive the design process because they offer outstanding technical depth and breadth, a graphical user interface that greatly reduces the time required to set up the analysis, and a very robust solver that converges to a solution in even the most complex geometries.

We began the tool design process by performing a 2D static analysis of the cross section of many 2D designs (see Figure 2, below). The analysis provided graphical output including graphs and histograms of the solution and contour plots that showed the magnetic field values superimposed on the surfaces of the model. Computational times are very short in 2D static simulation, making it possible to quickly evaluate a large number of designs. The 2D static cases were used to select likely candidates, which we then modeled using the 2D dynamic solver. The candidates that looked promising were then simulated with the 3D dynamic solver.

Figure 2: BJ engineers began the tool design process by performing a 2D static and velocity analysis of the inspection tool. Successful models are carried into 3D.

Once we optimized the magnetic design, we used simulation to determine where the sensors should be located to best capture variations in the magnetic fields. When we were satisfied with the design, we built it and tested it by pulling it through a spool. As is normally the case, we found that the electromagnetic simulation had predicted the performance of the tool within about 5 to 10 percent, which means that we got the design right the first time.

Evaluating potential designs in simulation is so much faster that we are able to evaluate 10 times as many concept designs in only 80 percent of the time required for the previous build and test approach. We are also able to evaluate the performance of the tool under more conditions than is practical with prototype testing. For example, we can easily check the tool’s ability to handle an extra wall thickness.

The next step was calibrating the neural network used with the tool so that it is accurately able to measure the geometry of each defect. Because defect width and depth are not directly correlated to the signal parameters, it is a complicated procedure to determine the best equations to use to determine defect size most accurately.

Using the traditional build-and-test method, the prototype MFL tool is pulled past artificial defects and the signal parameters are extracted and correlated with the actual defect size. This is a time-consuming and expensive process. The wide range of factors that affect defect response means that it is extremely hard to find the relationship between the factors and the defect response using the conventional pull-through method. Accuracy is limited by the fact that only a limited number of defects can be introduced into the spool pieces.

Electromagnetic simulation greatly reduces calibration time by providing the ability to change one parameter at a time and determine its effect on the defect signal. For example, we simulated a wide range of potential defects in a recent study. We modeled 5 mm x 5 mm, 10 mm x 10 mm, 20 mm x 20 mm, and 30 mm x 30 mm internal and external defects. We varied the depth of each defect from 5 to 100 percent of wall thickness in 5 percent increments. The signal parameters were extracted and plotted against defect depth for all sizes and for internal and external defects.

Figure 3: MLF—magnetic flux leakage—technology is used to detect and size metal loss features in the pipeline; the inertial navigation system aboard the inspection tools allow for the precise location of each feature on the pipeline to be provided.

The results showed that for a fixed diameter the radial peak amplitude increases as a function of defect depth. However, the peak amplitude is also strongly dependent upon the width of the defect, as well as its location. To achieve the same results using the traditional method, 146 defects would have to be electrochemically machined and the tool would have to be pulled through the pipe numerous times. Obviously, electromagnetic simulation makes it possible to calibrate the tool to a higher level of accuracy in a shorter period.

The inspection of the mainline sections of the pipeline is finished, and the inspection of the smaller diameter interconnect and lateral system is mostly done. Besides using MFL technology to detect metal loss, the internal navigation system aboard the inspection tools has allowed the precise location of each defect on the pipeline to be provided as well (see Figure 3, above). The pipeline operator implemented a data quality assurance program that included excavating the pipeline, physically measuring defects detected by the tool, and comparing them to the MFL predictions. This validation provided confidence in the MFL data.

The magnitude and requirements of this program required that optimized custom tools be developed to measure defects over the enormous extent of the pipeline accurately. Electromagnetic simulation made it possible to develop highly accurate custom tools that met all of the customer’s requirements within the project timeline at a reasonable cost.


Grant Coleman is the Research and Development Project Manager for BJ Pipeline Inspection Services Calgary, Alberta. Send your comments about this article through e-mail by clicking here. Please reference “Pipeline Inspectors, June 2006” in your message.

Company Information
BJ Services Co.
Houston, TX

Vector Fields Ltd.
Aurora, IL