No abstract

Pipeline in-line inspections (ILI) are one of the primary methods used to assess the integrity of operating oil and gas pipelines. Conventional ILI technology is based on ultrasonic testing (UT) or magnetic flux leakage (MFL) sensors. Although these technologies are suitable for most pipeline inspections, there remains an opportunity to expand ILI technology and application. ExxonMobil and Innospection Ltd. are working to develop a new ILI sensor technology based on a combination of Magnetic Eddy Current (MEC) and multi-differential eddy current. This new technology provides the potential to detect small volumetric features, inspect heavy wall gas pipelines, and inspect pipelines with corrosion resistant alloy (CRA) or non-metallic liners. Initial feasibility trials were conducted with a prototype ILI MEC tool. Tests were conducted on an 8.625” (219 mm) X65 carbon steel pipe lined with 0.118” (3 mm) of Inconel 825 pipe. Four types of defects were machined into the pipe to represent natural defects anticipated in service: • Metal loss features from 3 to 24 mm in diameter on the external surface of the carbon steel base pipe • Erosion on the internal layer of the CRA liner • Internal girth weld crack-like defects • Metal loss defects at the interface of the CRA and carbon steel Over 80 pull tests were conducted to determine the detection capabilities and speed sensitivities of the tool. Defects were detected by the sensors including the very small (<10 mm) pinhole-type features. Signals were analyzed by a preliminary sizing algorithm to demonstrate proof of concept. Detection performance was not affected at speeds up to 0.75 m/s. Since detection capabilities exceeded expectations, future development will continue based on the current prototype.

The paper describes which aspects of tool accuracy are important for the choice of ILI tools and design of new ones. It is discussed what constitutes an inspection run comparison. What kind of work is carried out, especially if several ILI vendors are involved? What kind of integrity statement can be deduced and how is this influenced by tool accuracy? Finally, the paper will discuss how some aspects of fracture mechanics affect the design of crack detection pigs. What are the minimum requirements for an ILI tool to allow for reasonable defect assessment? In addition, relation between companies performing In-Line Inspection (ILI) and those providing defect assessment is discussed. To what degree should an inspection company also deal with issues of pipeline integrity?

Inline inspection of pipelines by means of intelligent pigs usually results in large amounts of data that are analyzed offline by human experts. In order to increase the reliability of the data analysis process as well as to speed up analysis times methods of artificial intelligence such as neural networks have been used in the past with more or less success. The basic requirement for any technique to be used in practice is that no relevant features should be overlooked while keeping the false call rate as low as possible. For the task of automated analysis of in-line inspection data obtained from ultrasonic metal loss inspections, we have developed a two-stage approach. In a first step (called boxing), any defect candidates exceeding the specified size limits are recognized and described by a surrounding box. In the second step, all boxes from step 1 are analyzed yielding basically a relevant/non relevant decision. Each feature considered to be relevant is then classified according to a given set of feature classes. In order to efficiently perform step 2, we have adapted the SVM (support vector machines) algorithm which offers some important advantages compared to, for example, neural networks. We describe the approach applied, and examples as obtained from in-line inspection data are presented.