Safety analysis of plugging and abandonment of oil and gas wells in uncertain conditions with limited data

Well plugging and abandonment are necessitated to ensure safe closure of a non-producing offshore asset. Little or no condition monitoring is done after the abandonment operation, and data are often unavailable to analyze the risks of potential leakage. It is therefore essential to capture all inherent and evolving hazards associated with this activity before its implementation. The current probabilistic risk analysis approaches such as fault tree, event tree and bowtie though able to model potential leak scenarios; these approaches have limited capabilities to handle evolving well conditions and data unavailability. Many of the barriers of an abandoned well deteriorates over time and are dependent on external conditions, making it necessary to consider advanced approaches to model potential leakage risk. This paper presents a Bayesian network-based model for well plugging and abandonment. The proposed model able to handle evolving conditions of the barriers, their failure dependence and, also uncertainty in the data. The model uses advanced logic conditions such as Noisy-OR and leaky Noisy-OR to define the condition and data dependency. The proposed model is explained and tested on a case study from the Elgin platform's well plugging and abandonment failure

Pipe burst diagnostics using evidence theory

Copyright © IWA Publishing 2011.The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics Volume 13 Issue 4, pp. 596–608 (2011), DOI: 10.2166/hydro.2010.201 and is available at www.iwapublishing.com.This paper presents a decision support methodology aimed at assisting Water Distribution System (WDS) operators in the timely location of pipe bursts. This will enable them to react more systematically and promptly. The information gathered from various data sources to help locate where a pipe burst might have occurred is frequently conflicting and imperfect. The methodology developed in this paper deals effectively with such information sources. The raw data collected in the field is first processed by means of several models, namely the pipe burst prediction model, the hydraulic model and the customer contacts model. The Dempster–Shafer Theory of Evidence is then used to combine the outputs of these models with the aim of increasing the certainty of determining the location of a pipe burst within a WDS. This new methodology has been applied to several semi-real case studies. The results obtained demonstrate that the method shows potential for locating the area of a pipe burst by capturing the varying credibility of the individual models based on their historical performance