The risk assessment of a crude oil pipeline using fuzzy and bayesian based bow-tie analysis

Cross country pipelines are one of the major modes of transportation of petroleum products. Due to external and internal factors, the failure probability of pipeline is increasing over the past decades. The failure of pipelines can cause fires, which can result in environmental damage, loss of materials and further resulting into casualties. Effective risk assessment is vital to prevent and mitigate such potential incidents. In this work, an efficient tool that makes use of Bayesian approach along with Bow-tie analysis is used to obtain the failure frequencies of all the identified causes that may lead to failure of the selected pipeline located in Kerala, India. Bayesian networks can perform effective dynamic risk analysis by considering the conditional dependencies between various basic events leading to pipeline failure. The fuzzy logic and expert elicitation method are incorporated to determine the prior failure frequencies of all the identified causes of pipeline failure. The effectiveness of Bayesian network in performing forward analysis to determine the probabilities of pipeline failure consequences is demonstrated. This study also identifies the weak links associated with the occurrence of particular consequences, so that adequate measures can be taken to rectify them.

Life Prediction of Crude Oil Pipeline to Mitigate Leakages: A Case Study of an NPDC Major Pipeline in OML30, Nigeria

Pipeline leak or failure is a dreaded event in the oil and gas industries. Top events such as catastrophes and multiple fatalities have occurred in the past due to pipeline leak or failure especially when loss of contents was met with fire incidents. It is therefore imperative that the causes of pipeline failure are tackled to prevent or mitigate leak incidents. This is expedient to curb the menace that goes with leak incidents, such as destruction of the environment and ecosystem; loss of assets, finance, lives and property; dangers to workers and personnel, production downtime, litigation and dent to company’s reputation. This work focuses on the investigation of the actual cause of sudden pipeline failures and frequent pipeline leaks that often result to sectional pipeline replacement before the expiration of their anticipated life cycle in OML30 oil and gas field. The pipeline material selected, the standard of the minimum wall thickness of the material, the corrosive nature of the pipeline content and the observed internal corrosion rate were probed. An analysis of the rate of thinning and diminution of the internal wall of the pipeline by monitoring the interior rate of corrosion was used to forecast the remaining life of a crude oil pipeline and predict the life expectancy of a newly replaced or installed pipeline or installed pipeline.

Corrosion in underground infrastructures

There is a significant loss due to corrosion of buried infrastructure. Many pipes have failed due to mistreatment happening within them all around the world. Different soil aeration leads to macro corrosion cells that cause critical levels within the path corrosion leading to a loss of structural integrity of the buried pipes underground. This review paper seeks to address and presents a predetermined model developed by using software COMSOL Multiphysics to identify and characterize the areas experiencing a high rate of corrosion beneath the surface due to differential aeration. The pipe surfaces experience electrochemical reactions and reactant transport mechanisms in the soil and the pipes. Porosity and degree of saturation make the closed-form equations used to create the mass transport properties and electrical properties that constitute three-phase medium using standard soil parameters. The current model enables the study of soil property variations and conditions from the external environment pipeline corrosion. The model results conclude and agree well with the literature and case studies done at pipeline failure sites. The model used in this review will then enable water utilities to develop forecasting tools that may be useful for assessment.

Failures of Pipelines

This article discusses the failure analysis of several steel transmission pipeline failures, describes the causes and characteristics of specific pipeline failure modes, and introduces pipeline failure prevention and integrity management practices and methodologies. In addition, it covers the use of transmission pipeline in North America, discusses the procedures in pipeline failure analysis investigation, and provides a brief background on the most commonly observed pipeline flaws and degradation mechanisms. A case study related to hydrogen cracking and a hard spot is also presented.

Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems

Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects. In this paper, a taxonomy of existing pipeline failure detection techniques and technologies was created to comparatively analyse their respective advantages, drawbacks and limitations. This effort has effectively illuminated various unaddressed research challenges that are still present among a wide array of the state-of-the-art detection methods that have been employed in various pipeline domains. These challenges include the extension of the lifetime of a pipeline network for the reduction of maintenance costs, and the prevention of disruptive pipeline failures for the minimisation of downtime. Our taxonomy of various pipeline failure detection methods is also presented in the form of a look-up table to illustrate the suitability, key aspects and data or signal processing techniques of each individual method. We have also quantitatively evaluated the industrial relevance and practicality of each of the methods in the taxonomy in terms of their respective deployability, generality and computational cost. The outcome of the evaluation made in the taxonomy will contribute to our future works involving the utilisation of sensor fusion and data-centric frameworks to develop efficient, accurate and reliable failure detection solutions.

Prediction of the Financial Losses of Subsea Pipeline Failure

The consequences of subsea crude pipeline failure drastically affect the continuity of oil supply and the ability of operating companies to meet market demand. Moreover, it reduces clients’ confidence and reliance on operating companies. There are mainly two types of failures that severely affect the integrity and operability of a pipeline. These are the leakage caused by gradual thinning of the pipeline due to corrosion and the leakage caused by a rupture pressure. The first form of failure results in a constant slow leakage that go over time without being noticed. While the second form of failure takes place without warning which makes the restoration effort extremely difficult and time consuming. Both forms of failure result in delayed production, environmental damage, legal claims as well as financial losses that operating companies could incur. Moreover, leaked oil products adversely impact other industries which bring these industries into a halt. Such industries may include fishery, maritime transportation, and tourism industries. This paper presents a method for predicting the financial losses that a company could incur in the event a corroded pipeline undergoes the abovementioned failures. Basically, this method can be used as a standard estimating tool for predicting the financial losses if such events occur.