MFL pipeline inspection tools
In the field, a device that travels inside a pipeline to clean or inspect it is typically known as a "PIG". While there is no history to back up this term as an acronym as it was first derived from the squealing pig-like sound made as the tool passed by, it could be referred to in this case as a Pipeline Inspection Gauge. In some countries a pig is known as a "Diablo," literally translated to mean "the devil" relating to the shuttering sound the tool would make as it passed beneath people’s feet. The pigs are built to match the diameter of a pipeline and use the very product being carried to end users to transport them. Pigs have been used in pipelines for many years and have many uses. Some separate one product from another, some clean and some inspect. An MFL tool is known as an "intelligent" or "smart" inspection pig because it contains electronics and collects data real-time while travelling through the pipeline. Sophisticated electronics on board allow this tool to accurately detect features as small as 1 cm by 1 cm.
Typically, an MFL tool consists of two or more bodies. One body is the magnetizer with the magnets and sensors and the other bodies contain the electronics and batteries. The magnetizer body houses the sensors that are located between powerful "rare-earth" magnets. The magnets are mounted between the brushes and tool body to create a magnetic circuit along with the pipe wall. As the tool travels along the pipe, the sensors detect interruptions in the magnetic circuit. Interruptions are typically caused by metal loss and which in most cases is corrosion. Mechanical damage such as shovel gouges can also be detected. The metal loss in a magnetic circuit is analogous to a rock in a stream. Magnetism needs metal to flow and in the absence of it, the flow of magnetism will go around, over or under to maintain its relative path from one magnet to another, similar to the flow of water around a rock in a stream. The sensors detect the changes in the magnetic field in the three directions (axial or circumferential) to characterize the anomaly. An MFL tool can take sensor readings based on either the distance the tool travels or on increments of time. The choice depends on many factors such as the length of the run, the speed that the tool intends to travel, and the number of stops or outages that the tool may experience.
The second body is called an Electronics Can. This section can be split into a number of bodies depending on the size of the tool. This can, as the name suggests, contains the electronics or "brains" of the smart pig. The Electronics Can also contains the batteries and is some cases an IMU (Inertial Measurement Unit) to tie location information to GPS coordinates. On the very rear of the tool are odometer wheels that travel along the inside of the pipeline to measure the distance and speed of the tool.
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Signal Analysis
The primary purpose of an MFL tool is to detect corrosion in a pipeline. To more accurately predict the dimensions (length, width and depth) of a corrosion feature, extensive testing is performed before the tool enters an operational pipeline. Using a known collection of measured defects, tools can be trained and tested to accurately interpret MFL signals. Defects can be simulated using a variety of methods.
Creating and therefore knowing the actual dimensions of a feature makes it relatively easy to make simple correlations of signals to actual anomalies found in a pipeline. When signals in an actual pipeline inspection have similar characteristics to the signals found during testing it is logical to assume that the features would be similar. An anomaly is often reported in a simplified fashion as a cubic feature with an estimated length, width and depth. In this way, the effective area of metal loss can be calculated and used in acknowledged formulas to predict the estimated burst pressure of the pipe due to the detected anomaly.
After an inspection, the collected data is downloaded and compiled so that an analyst is able to accurately interpret the collected signals. Most pipeline inspection companies have proprietary software designed to view their own tool’s collected data. The three components of the MFL vector field are viewed independently and collectively to identify and classify corrosion features. Metal loss features have unique signals that analysts are trained to identify.
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