STATISTICAL CALIBRATION OF PIPELINE IN-LINE INSPECTION DATA
This paper describes a statistical methodology for the calibration of in-line inspection (ILI) tools which is based on the comparison of the ILI readings with field-measurements results. The systematic and random errors that affect the ILI and the field tools are estimated and from this information, an unbiased estimation of the true depth of the defects detected by the ILI tool is produced. The influence of the number of field verifications on the reliability of the calibration process is addressed. The methodology is tested through Monte Carlo simulations and illustrated using a real-life ILI dataset produced by an UT tool. Introduction: Today in-line inspection (ILI) tools used to detect, locate and size pipeline anomalies such as dents, cracks and corrosion metal loss are based on the magnetic flux leakage (MFL) and ultrasonic (UT) principles [1]. The information provided by ILI tools consists of geometrical data describing each detected anomaly, e.g. its length, depth, width and orientation. This information is affected by built-in measurement errors, both systematic and random, that have to be considered when the ILI data is used to conduct integrity studies and fitness-forpurpose investigations. Pipeline integrity analysts and ILI vendors are now aware of the fact that in assessing the severity of a defect, the key issue is how accurately its geometry has been measured. In this sense, the two important parameters that ILI vendors provide are the probability of detection (POD) and the sizing accuracy of the tool. The sizing accuracy associated with an ILI tool is quoted as an accuracy level and a percent confidence. The depth sizing accuracy associated with corrosion metal loss of today high resolution (HR) MFL tools is typically claimed to be ±10% of the pipe wall thickness (wt) with a confidence level of 80%. In the case of extra high resolution (XHR) MFL tools, the sizing accuracy is ±5% wt at 80% confidence. On the other hand, today HR and XHR UT tools are claimed to have a sizing accuracy of ±0.6 mm and ±0.3 mm at a confidence level of 80%, respectively [1]. Pipeline operators assess the accuracy of a pipeline inspection through the statistical comparison of the metal loss sizes predicted by the ILI tool with the sizes obtained through field inspections. (a) 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
Multi-MFL Measurement Assessment using Gaussian Mixture Model
Magnetic flux leakage (MFL) is the most popular in-line inspection (ILI) technique to inspect pipeline corrosion. However, the random measurement error of MFL cannot be negligible due to measurement variations. Since the ILI is usually conducted with two different types of MFL devices, data from two MFL systems are compared to assess the measurement result in this paper. Because the difference between two measurements consists of both random and systemic errors associated with individual MFL system, measurement conversion is conducted first to eliminate the influence of the systemic error. In this paper, a Gaussian probability density function is employed to represent the measurement data because of the randomness of measurement error. Then, the process to eliminate the systemic error between two systems becomes the alignment of two Gaussian mixtures. Besides, the L2 distance is employed as the distance measure to align Gaussian mixtures and assess the measurement confidence. The validity of proposed method is proven in the numerical example.
Routine quantitative phase analysis of niobium-bearing lateritic ores
Routine quantitative analyses by Rietveld full-pattern fitting have been carried out on more than one thousand niobium ore samples originating from four pyrochlore deposits: Lueshe and Bingo (D.R. Congo), Mabounie (Gabon), and Sokli (Finland). The ore samples were characterized by a highly variable mineral content, the presence of mineral phases with a wide range of compositions and a high amorphous content. An analytical procedure was established which addressed several problems: creation of an appropriate crystallographic parameter database, specimen preparation, check for preferred orientation and measurement of the amorphous content by the internal standard method.