Pipeline Integrity Management Program Research Articles

Impact Analysis of Inline Inspection Accuracy on Pipeline Integrity Planning

Abstract Integrity planning methods and inline inspection (ILI) tool performance have a great impact on a pipeline integrity management program. In pipeline integrity planning, risk and integrity assessments are performed to schedule integrity activities like ILI for the purpose of reducing risks and ensuring reliable and safe operations. In this paper, a method is developed for analyzing the impact of ILI tool accuracy on pipeline integrity planning, which is of great importance but has not been systematically studied before. Crack inspection and threat of fatigue cracking are used as the working case for the analysis, although the approach could potentially be used for any pipeline threat type. The Paris' law degradation model is used for the crack growth and subsequent severity and risk assessment. We investigated the impact of ILI tool accuracy on the cost rate, as well as the associated inspection intervals. The impact on long-term cost rate was also investigated considering new defect generation and continuous growth. Sensitivity analyses were performed. The optimal inspection intervals and the corresponding total cost rates with respect to different ILI tool accuracy and different input parameters were obtained and compared. The proposed method can support integrity management program planning by linking risks with integrity plan costs associated with ILI accuracy and optimal re-assessment intervals. The contributions of this paper mainly include the investigation of the problem of how ILI tool accuracy impacts integrity planning, the development of the method for analyzing pipelines with cracks, and the verification and validation with the examples.

Matching pipeline In-line inspection data for corrosion characterization

In-line inspection (ILI) is critical to pipeline integrity management program in oil and gas industry. The corrosion leading to potential pipe leakage and failure needs to be detected through periodical ILI and trigger the alarm for further field investigation. The proactive management strategy relies on the capability of predicting the corrosion growth rate over time. To determine the growth of corrosion, the corroded areas on the pipeline need to be matched with the extracted features from the data acquired through the ILI performed at different times. However, manual matching is time consumption, labor intensive, and prone to errors. This paper proposes the automated methods to match multiple ILI data. Informative features are extracted from ILI data and input to the machine learning models. Both individual- and ensemble-based machine learning models were investigated in this study. The experimental results demonstrate an accurate matching can be achieved with the ensemble learning method. Thus, the characterization of matched corrosion will further contribute to pipeline integrity management and risk analysis.

IMPAKT: Oil and Gas Pipeline Integrity Management Program Assessment

AbstractPipelines are regarded as the most practical and cost-effective mode of transporting oil and gas (O&G) and also have been the most reliable part of global energy infrastructure. In North Am...

A review on pipeline integrity management utilizing in-line inspection data

Abstract Pipelines are widely used in transporting large quantities of oil and gas products over long distances due to their safety, efficiency and low cost. Integrity is essential for reliable pipeline operations, for preventing expensive downtime and failures resulting in leaking or spilling oil or gas content to the environment. Pipeline integrity management is a program that manages methods, tools and activities for assessing the health conditions of pipelines and scheduling inspection and maintenance activities to reduce the risks and costs. A pipeline integrity management program mainly consists of three major steps: defect detection and identification, defect growth prediction, and risk-based management. In-line inspections (ILI) are performed periodically using smart pigging tools to detect pipeline defects such as corrosion and cracks. Significant advances are needed to accurately evaluate defects based on ILI data, predict defect growth and optimize integrity activities to prevent pipeline failures, and pipeline integrity management has drawn extensive and growing research interests. This paper provides a comprehensive review on pipeline integrity management based on ILI data. Signal processing methods for defect evaluation for different types of ILI tools are presented. Physics-based models and data-driven methods for predicting defect growth for pipelines with different categories of defects are discussed. And models and methods for risk-based integrity management are reviewed in this paper. Current research challenges and possible future research trends in pipeline integrity management are also discussed.

Bayesian analysis of small probability incidents for corroding energy pipelines

This paper presents a new methodology for estimation of small posterior failure probabilities for underground energy pipelines, based on external corrosion inspection data. The analysis of the data is based on the BUS (Bayesian Updating with Structural reliability methods) that sets an analogy between Bayesian updating and a reliability problem. The structural reliability method adopted herein is Subset Simulation (SuS) and the whole analysis is referred to as BUS-SuS. Corrosion data obtained from multiple in-line inspections (ILI) of an underground natural gas pipeline are used to illustrate and validate the proposed methodology. The growth of the corrosion defects is modelled through an hierarchical Bayesian framework and the ILI associated measurement errors are comprehensively considered. Through this efficient method, it is ensured that the final samples have reached the posterior distribution. It is also more advantageous over other methods typically employed for Bayesian analysis of corroding pipelines, because it allows the estimation of small posterior failure probabilities directly within the same framework. The proposed methodology can be incorporated in a reliability-based pipeline integrity management program to assist engineers in selecting suitable maintenance strategies.

Recent Developments in Speed Control System of Pipeline PIGs for Deepwater Pipeline Applications

Pipeline infrastructures normally represent high cost of investment and the pipeline must be free from risks that could cause environmental hazard and potential threats to personnel safety. Pipeline integrity such monitoring and management become very crucial to provide unimpeded transportation and avoiding unnecessary production deferment. Thus proper cleaning and inspection is the key to safe and reliable pipeline operation and plays an important role in pipeline integrity management program and has become a standard industry procedure. In view of this, understanding the motion (dynamic behavior), prediction and control of the PIG speed is important in executing pigging operation as it offers significant benefits, such as estimating PIG arrival time at receiving station, planning for suitable pigging operation, and improves efficiency of pigging tasks. The objective of this paper is to review recent developments in speed control system of pipeline PIGs. The review carried out would serve as an industrial application in a form of quick reference of recent developments in pipeline PIG speed control system, and further initiate others to add-in/update the list in the future leading to knowledge based data, and would attract active interest of others to share their view points. Keywords—Pipeline Inspection Gauge (PIG), In Line Inspection Tools (ILI), PIG motion, PIG speed control system

New Technique for Studying Soil-Corrosion of Underground Pipeline

Pipeline integrity management programs address a wide range of threats to safe operations. Among these threats, external corrosion has been among the most dominant failure mechanism experienced by buried steel pipelines. External corrosion can be detected through a variety of assessment methods, including inline inspection (ILI) and External Corrosion Direct Assessment (ECDA). In the absence of data from inspection, the corrosion rate can be estimated based on the use of buried corrosion coupons, corrosion probes and corrosion rate modelling using data derived from soil analysis. The objective of the study was to design a methodology of the external growth modelling of corrosion on buried gas pipelines under various exposures to soil conditions. The technique can be used to generate field data so as to model empirically the corrosion dynamic in soil or for verification of corrosion data from laboratory testing. The potential model based on the proposed technique is highly potential to predict the likelihood of corrosion growth rate experienced by buried lines exposed to corrosive environment. Hence, can greatly assist operator to secure the integrity of their pipelines until the structure reaches its designed lifetime.

Potential Benefits of Data Integration for Pipeline Integrity Management Programs

Abstract Pipeline integrity management programs are a requirement under Annex N of CSA Z662.07. Annex N requires that data from many sources and details be collected, integrated and analyzed on a continuous basis. The stated goal is to provide guidance for developing, documenting and implementing pipeline integrity management programs, which provide safe, environmentally responsible and reliable service(1). If executed properly, there should be improved pipeline integrity with measurable improvements and benefits. An important step in reaching this goal is risk assessment, which requires continuous data input to maintain valid integrity assessments. This dynamic process of data collection and integration enables continuous analysis and potentially leads to a more proactive integrity management system. The biggest problem for most operating companies in creating a dynamic and proactive integrity management program (IMP), compliant with Annex N, is in handling the significant data issues. Indeed, the data collection, integration and analysis are the most daunting tasks of the program. A software solution that accepts data from the many activities involved in the lifecycle of a pipeline system would be potentially beneficial. Application of integrated software tools in the oil and gas industry will result in improved integrity management, greater confidence in our pipeline infrastructure and economic benefits. We will review some of the data requirements of Annex N of CSA Z662.07 and identify the components of the program that may differ from your existing management plan. The nature and detail of data to be managed will be presented. Finally, we will provide an example of the role that software tools can play to assist in meeting the requirements of this challenging regulatory initiative.