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ABSTRACT In-line inspection of underground pipelines for corrosion damage using smart pigs is now quite common. With the advent of high-resolution pigs that can identify large numbers of potential anomalies, more sophisticated methodologies are required for interpreting the results of an in-line inspection. Of particular interest is the probability that the depth of corrosion in a particular location exceeds a critical depth defined by the local pipe characteristics and maximum operating pressure. In this paper, a Bayesian statistical methodology for determining the probability that corrosion exceeds critical magnitude is presented. The estimated probabilities (from the posterior distribution) are based on an assumed pit depth distribution (the prior distribution), the pig call data produced by the in-line inspection (the data), and the detection and depth accuracy performance characteristics of the pig utilized (the data model). The resulting exceedance probabilities can be used with or without corrosion consequences to make inspection/maintenance policy decisions. INTRODUCTION In-line inspection of underground pipelines for corrosion damage using smart pigs is now quite common. Smart pigs identify the locations of potential anomalies along a pipeline and often provide information such as the maximum depth and axial/circumferential extent of corrosion. If the number of potential anomalies identified is small, it is possible to consider excavating all potential anomalies for direct investigation. With the advent of high-resolution pigs that can identify large numbers of potential anomalies, more sophisticated methodologies are required for interpreting the results of an in-line inspection. Of particular interest is the probability that the depth of corrosion in a particular location exceeds a critical depth defined by the local pipe characteristics and maximum operating pressure. This critical depth can be defined to be the depth at which a leak or rupture is expected to occur or, alternatively, a depth that provides for a remaining factor of safety. The methods presented in this paper are based on excavation data and the data iiom a single run of a high-resolution ultrasonic in-line inspection tool big) through the entire Trans-Alaska Pipeline System. The objectives of the corrosion risk assessment research summarized here were to: develop a method based on pig data for determining the probability that corrosion on underground pipe exceeds a critical magnitude as a fiction of location, l Incorporate pig performance characteristics such as detection and depth accuracy performance directly into the method, and l Incorporate an estimated pit depth distribution that characterizes the extent of corrosion on the underground pipe directly into the method. PROCEDURES The first steps in developing a corrosion risk assessment method were to create a pig performance data set and to subsequently characterize the detection and depth accuracy characteristics of the pig utilized. Using these pig performance characteristics along with the distribution of pit depths for excavated pipe and the distribution of pig call depths for the entire run, an assumed distribution of corrosion pit depths was then developed. Finally, this assumed corrosion pit distribution and the pig performance characteristics were combined to develop methods for making corrosion inferences for pipe with no pig calls and pipe containing pig calls of specified depths. Details of these procedures are given in the following sections. Pig Performance Data Set In order to evaluate an excavated area of corrosion, a grid of corrosion depth measurements is taken in the field using a 1-inch spacing. The size of the
- North America > United States > Texas (0.28)
- North America > United States > Alaska (0.24)
ABSTRACT The ability of a corrosion pig to reliably detect, measure, and assess corrosion that could adversely affect pipeline integrity is its most important performance attribute. Objective knowledge of the performance limitations of in-line inspection tools (commonly referred to as pigs) under real operating conditions is the key to subsequent decision making based on pig results. INTRODUCTION Alyeska Pipeline Service Company (APSC) operates the Trans Alaska Pipeline System transporting crude oil 800 miles (1,288 km) from Prudhoe Bay to Valdez, Alaska. Approximately 380 miles (612 km) of the pipeline is below ground while the remaining 420 miles (676 km) is above ground. This pipeline is comprised of 48-inch (1,219 mm) diameter pipe, 0.462- and 0.562-inch and 14.3-mm wall thicknesses, and API 5L grades of X60, X65, and X70. Nominal joint lengths are either 40- or 60-feet (12.2- or 18.3 m). Annual in-line inspection surveys of the pipeline system have been conducted since 1979 with second generation inspection tools being utilized since 1989. These inspections have utilized both magnetic flux leakage (MFL) and ultrasonic (UT) technologies. These surveys identified areas most affected by corrosion resulting in the replacement of 8 miles (12.9 km) of pipe in the Atigun food plain and the rehabilitation of below ground sections in the Chandalar flood plain. The second generation tools have indicated external corrosion on other portions of the below ground pipe which is believed to be less severe and less concentrated than that found in the Atigun and Chandalar flood plains. In response to these in-line inspections over 450 excavations have been carried out exposing portions of over 580 joints of pipe covering approximately 20,000 linear feet (6,096 m). Detailed corrosion measurements were made during these excavations to assess the structural integrity of the pipe. Results of the annual in-line inspection surveys and the information obtained at the 450 excavations in response to these surveys have produced an invaluable source of data. These data provide a rational basis for assessing and prioritizing as-yet uninvestigated indications (pig calls). Presented herein is an approach for evaluating pig performance characteristics based on a comparison with detailed field measurement data and then using them for analyzing the severity of uninvestigated pig calls for the 380 miles (612 km) of below ground pipe. These analyses address only the below ground sections of the pipeline since no external corrosion has been identified on the above ground sections of the pipeline. The performance characteristics discussed herein are detection and depth accuracy. Pig performance bounds on detection and accuracy are then used to assess the severity of uninvestigated pig calls. The results of the analyses presented are based on five pig runs conducted between 1991 and 1994 where two pig runs were conducted in 1992. Both magnetic flux leakage (MFL) and ultrasonic (UT) technologies were utilized during this time period. These pig runs are identified as Pig A, Pig B, etc. without reference to the technology, vendor, or year of the inspection.
- Well Completion > Well Integrity > Subsurface corrosion (tubing, casing, completion equipment, conductor) (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Facilities Design, Construction and Operation > Pipelines, Flowlines and Risers > Materials and corrosion (1.00)