A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network

Hou, Peng; Yi, Xiaojian; Dong, Haiping

doi:10.3390/en13030685

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…However, difficulty arises when selecting proper tools to measure the risk and the actual method to capture the risk level of inspected pipelines. An option to overcome the relevant difficulties can be developing a Risk-based inspection using a risk matrix and generating data through inline Inspection as reported by [20]. The study [21] shows that the RBI is essential to replace the existing time-based Inspection.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of risk-based inspection of 28-years-old subsea sales gas pipelines to support the energy demand

Soedarsono

Wijaya

Aditiyawarman

et al. 2023

EEJET

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In the oil and gas industry, maintaining the integrity of production equipment is critical to ensuring the industry’s sustainability. Failure to maintain the integrity of production equipment can result in financial losses for the business. The management of production equipment nearing the end of its design life faces an increasing cost of Inspection, Maintenance, and Repair (IMR). As a result, a strategy to improve the efficiency of IMR is essential. Recent IMR management practices include predictive Risk-Based Inspection (RBI), which is more efficient than Time-Based Inspection (TBI). The research intends to evaluate the 28-year-old subsea sales gas pipeline using API 581 standard quantitative methodology by utilizing the Inline Inspection (ILI). Specifically, the study focuses on measuring the Probability and Consequence Failure of inspected pipelines. The inspection interval is determined based on the minimum allowable thickness. The risk calculation indicates that 12 pipeline segments are at a medium risk level (3 segments, 1D and 1E, and 2C). The remaining nine segments remain at lower risk (1C). Based on the result, segment nine is accepted as the highest PoF value of 1.04E-4 failures per year due to high depletion values due to the higher CoF value at the leak location. The calculation of the inspection interval indicates that the forthcoming Inspection will be due 20 years post the previous assessment. Another method using the Estimated Repair Factor (ERF) thickness limit approach produces the same results. However, assessment using ASME B31.8S provides different results of 10 years intervals when using the same ILI inspection method. This work can be used as a standard guideline to assess the risk of pipelines over a decade in service

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of risk-based inspection of 28-years-old subsea sales gas pipelines to support the energy demand

Soedarsono

Wijaya

Aditiyawarman

et al. 2023

EEJET

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“…[14] established that the pipe age influenced sewer pipe condition and found that poor pipe sewer condition is higher for pipes more than 50 years. "Most of the condition prediction models developed in previous studies show that pipe age has a significant relationship with deterioration of sewer pipes" [15].…”

Section: Physical Factorsmentioning

confidence: 99%

Development and Comparison of Prediction Models for Sanitary Sewer Pipes Condition Assessment Using Multinomial Logistic Regression and Artificial Neural Network

2022

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Sanitary sewer pipes infrastructure system being in good condition is essential for providing safe conveyance of the wastewater from homes, businesses, and industries to the wastewater treatment plants. For sanitary sewer pipes to deliver the wastewater to the treatment plants, they must be in good condition. Most of the water utilities have aged sanitary sewer pipes. Water utilities inspect sewer pipes to decide which segments of the sanitary sewer pipes need rehabilitation or replacement. The process of inspecting the sewer pipes is described as condition assessment. This condition assessment process is costly and necessitates developing a model that predicts the condition rating of sanitary sewer pipes. The objective of this study is to develop Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) models to predict sanitary sewer pipes condition rating using inspection and condition assessment data. MLR and ANN models are developed from the City of Dallas’s data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. The ANN model is trained, validated, and tested. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and length. Soil type is the environmental factor that influences sanitary sewer pipes condition rating. The accuracy of the performance of the MLR and ANN is found to be 75% and 85%, respectively. This study contributes to the body of knowledge by developing models to predict sanitary sewer pipes condition rating that enables policymakers and sanitary sewer utilities managers to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.

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“…Pipelines have become the main way of liquid transportation because of their advantages in economy and transportation capacity [1]. However, during the long-term operation of pipelines, they inevitably leak due to corrosion, third-party destruction and natural disasters, causing environmental contamination, fires or explosions if measures are not taken in a timely way [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Study on Optimization of Active Control Schemes for Considering Transient Processes in the Case of Pipeline Leakage

Zhang

Shen

et al. 2020

Energies

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Pipeline leakage of crude oil, refined oil or other petroleum derivatives can cause serious damage to the environment, soil, and more importantly, pose a serious threat to personal safety. The losses can be minimized to a degree by active control. Therefore, timely and effective control measures should be taken to minimize the leak volume whenever a pipeline leaks. However, the complexity of pipeline hydraulic systems makes it difficult to optimize control schemes for pipeline hydraulic devices under leak conditions, and existing studies rarely consider complex transient processes. This paper aims to establish a mixed integer linear programming model considering transient processes, hydraulic constraints, equipment constraints and flow constraints, and develop a detailed control scheme of the devices by the branch and bound algorithm. Moreover, it is the objective of the model to figure out the most optimal control plan to minimize the leakage. Experiments on a real-world liquid pipeline have proved the practicability and high reliability of the model.

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A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network

Cited by 3 publications

References 39 publications

Development of risk-based inspection of 28-years-old subsea sales gas pipelines to support the energy demand

Development of risk-based inspection of 28-years-old subsea sales gas pipelines to support the energy demand

Development and Comparison of Prediction Models for Sanitary Sewer Pipes Condition Assessment Using Multinomial Logistic Regression and Artificial Neural Network

Study on Optimization of Active Control Schemes for Considering Transient Processes in the Case of Pipeline Leakage

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