☆ 4.7 Review

Advances in corrosion growth modeling for oil and gas pipelines: A review

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2023)

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Article Computer Science, Artificial Intelligence

A transfer-learning approach for corrosion prediction in pipeline infrastructures

Giuseppe Canonaco et al.

Summary: The use of Machine Learning (ML) techniques in predicting internal corrosion in pipelines is explored, leveraging Transfer Learning (TL) to build models using supervised information from other pipelines. The methodology will be experimentally validated on real-world pipelines.

APPLIED INTELLIGENCE (2022)

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Article Computer Science, Interdisciplinary Applications

Data-driven predictive corrosion failure model for maintenance planning of process systems

Rioshar Yarveisy et al.

Summary: This study applies extreme value analysis (EVA) to predict corrosion progress and prioritize inspection and maintenance of extreme pits. The results show that the peaks over threshold (POT) method has a higher predictive capacity for assessing degradation progress compared to the block maxima (BM) method, although it leads to an increase in falsely categorized failures.

COMPUTERS & CHEMICAL ENGINEERING (2022)

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Review Engineering, Mechanical

Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review

Afzal Ahmed Soomro et al.

Summary: The use of machine learning techniques in evaluating the integrity of damaged oil and gas pipelines is favored by researchers. Hybrid models, combining the strengths of various constituent models, have been found to outperform standalone models in predicting pipeline integrity. Current research mostly utilizes field data, simulation data, and experimental data, with input characteristics such as temperature, pH, pressure, and velocity being crucial in corroded pipeline integrity assessment.

ENGINEERING FAILURE ANALYSIS (2022)

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Article Engineering, Chemical

A risk assessment framework considering uncertainty for corrosion-induced natural gas pipeline accidents

Xinhong Li et al.

Summary: This paper develops a methodology to assess the risk of corrosion-induced natural gas pipeline accidents considering the uncertainties in incident escalations. The established approach estimates the corrosion failure probability of the pipeline through limit state functions and models the pipeline accident using empirical models. Monte Carlo method is utilized to solve the established models, and sensitivity analysis is conducted.

JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES (2022)

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Article Computer Science, Interdisciplinary Applications

A novel boosting ensemble committee-based model for local scour depth around non-uniformly spaced pile groups

Iman Ahmadianfar et al.

Summary: This study combines the prediction powers of Gaussian process regression, random forest, and M5 model tree using a novel ensemble committee-based data intelligent technique to accurately estimate local scour depth around non-uniformly spaced pile groups. The ensemble model significantly outperformed existing empirical models with high correlation coefficient, low root mean square error, and mean absolute percentage of error. Sensitivity analysis showed that pile diameter is the most influential variable in estimating the scour depth.

ENGINEERING WITH COMPUTERS (2022)

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Article Engineering, Environmental

Operational subsea pipeline assessment affected by multiple defects of microbiologically influenced corrosion

Mohammad Yazdi et al.

Summary: This paper presents a systematic approach to evaluate the optimal maintenance strategy time interval for a subsea process system influenced by Microbiological Influenced Corrosion (MIC) within multiple defects. The proposed method incorporates modeling of defect generation, pit depth growth, and maximum pit depth, along with simulating maintenance strategies to identify the optimal strategy.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2022)

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Article Engineering, Environmental

Research on equipment corrosion diagnosis method and prediction model driven by data

Jianfeng Yang et al.

Summary: Equipment corrosion management is crucial for process safety in the oil refining industry, with corrosion diagnosis playing a significant role in supervision and protection. This paper introduces a data-driven solution for comprehensive evaluation and prediction of corrosion safety state, utilizing algorithms like Borderline-SMOTE and Random Forest (RF) to improve accuracy. The results show promising improvements in critical mechanism identification and corrosion state classification, indicating potential for enhancing equipment safety management.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2022)

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Article Engineering, Environmental

A probabilistic framework for risk management and emergency decision-making of marine oil spill accidents

Xinhong Li et al.

Summary: This paper proposes a new methodology based on Bayesian networks and Influence Diagram for risk management and emergency decision-making of marine oil spill accidents. The methodology integrates risk management and emergency response, balancing risk and cost to achieve optimal decision-making.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2022)

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Article Engineering, Environmental

Main control factors and prediction model of flow-accelerated CO2/H2S synergistic corrosion for X65 steel

Min Qin et al.

Summary: This study investigated the corrosion effects of H2S and CO2-containing medium flow on submarine pipelines and explored the corrosion mechanisms. The results are of great significance for improving the integrity management of submarine pipelines.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2022)

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Article Engineering, Environmental

Deeppipe: Theory-guided neural network method for predicting burst pressure of corroded pipelines

Yunlu Ma et al.

Summary: This paper proposes a theory-guided neural network model-based method to predict burst pressure of corroded pipelines, incorporating physical principles into the deep learning framework. Validation using experiments and finite element simulations shows the model's robustness and adaptability to different data. The model outperforms other models in accuracy, with a correlation coefficient of 0.9945, root mean square error of 0.562, and mean absolute percentage error of 2.65%.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2022)

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Article Engineering, Industrial

A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines

Xinhong Li et al.

Summary: This paper presents a novel data-driven model for corrosion degradation prediction of offshore oil pipelines, which integrates KPCA and BRANN techniques to improve the robustness and accuracy of the prediction.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2022)

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Review Engineering, Industrial

A review of risk-based decision-making models for microbiologically influenced corrosion (MIC) in offshore pipelines

Mohammad Yazdi et al.

Summary: Microbiologically influenced corrosion (MIC) is a critical integrity threat in marine and offshore sectors, and it should be considered for risk-based decision-making and asset integrity management. However, there is a lack of reliable knowledge of risk-based decision-making models for MIC in these sectors. This study aims to identify the gaps, needs, and challenges of MIC in risk-based decision-making models through a systematic analysis of literature.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2022)

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Article Energy & Fuels

Risk analysis and maintenance decision making of natural gas pipelines with external corrosion based on Bayesian network

Yun-Tao Li et al.

Summary: A risk analysis and maintenance decision-making model for natural gas pipelines with external corrosion is proposed based on a Bayesian network in this study, aiming to provide effective maintenance strategies for pipeline external corrosion scenarios, which is verified through a case study.

PETROLEUM SCIENCE (2022)

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Article Energy & Fuels

Resilience assessment of a subsea pipeline using dynamic Bayesian network

Mohammad Yazdi et al.

Summary: The study introduces a probabilistic methodology to assess the resilience of subsea systems under microbiologically influenced corrosion (MIC), using a dynamic Bayesian network approach to model system resilience as a function of time. An industry-based application study on subsea pipelines demonstrates the efficiency and effectiveness of the proposed methodology in resilience assessment, helping decision-makers consider resilience in system design and operation.

JOURNAL OF PIPELINE SCIENCE AND ENGINEERING (2022)

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Review Engineering, Multidisciplinary

Applications of machine learning in pipeline integrity management: A state-of-the-art review

Andika Rachman et al.

Summary: Pipeline integrity management (PIM) is a set of activities aimed at reducing the risk of failure due to degradation and maintaining the functionality and safety of pipelines. Machine learning (ML) has been widely applied in managing and processing data generated from PIM activities.

INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING (2021)

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Review Energy & Fuels

A review of machine learning in building load prediction

Liang Zhang et al.

Summary: This paper reviews the application of machine learning techniques in building load prediction, covering tasks T, performance P, and experience E aspects. It discusses the modeling algorithms that improve performance, as well as data engineering for modeling, and concludes with identified gaps and future trends in machine learning application for building energy systems.

APPLIED ENERGY (2021)

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Article Engineering, Chemical

Dynamic probability assessment of urban natural gas pipeline accidents considering integrated external activities

Xinhong Li et al.

Summary: This study presents a dynamic risk analysis of external activities to urban gas pipelines, integrating unintentional and intentional damage in a unified framework. A Bayesian network mapping from the Bow-tie model is used to represent pipeline accidents initiated from intentional and unintentional hazards. The model allows for assessment of dynamic probabilities of consequences and identifies the most credible contributing factors to risk, capturing both data and model uncertainties.

JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES (2021)

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Article Engineering, Industrial

A hybrid population-based degradation model for pipeline pitting corrosion

Roohollah Heidary et al.

Summary: This paper presents a novel algorithm for developing a population-based corrosion degradation model for oil and gas pipelines. It eliminates the need for defect-matching procedures for non-critical pits and uses a hierarchical Bayesian model to combine uncertain in-line inspection data and physics of failure knowledge. The algorithm successfully predicts pipeline degradation levels with high accuracy.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

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Article Engineering, Environmental

Recent Advances in Sensing and Assessment of Corrosion in Sewage Pipelines

Sahar Foorginezhad et al.

Summary: Corrosion, causing gradual destruction of materials, is crucial for sewage pipelines due to its significant financial, health, and safety costs. Research efforts have focused on understanding effective parameters leading to corrosion in sewer pipes and monitoring infrastructure conditions, resulting in reduced equipment damage. Microbiologically induced corrosion and data analysis techniques have also been explored.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2021)

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Article Engineering, Environmental

Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines

Mohamed El Amine Ben Seghier et al.

Summary: The main goal of this study is to develop accurate frameworks for estimating the maximum pitting corrosion depth in oil and gas pipelines using data-driven techniques and artificial intelligence models. Different advanced approaches were applied to analyze the relationship between pitting depths and various factors that influence the corrosion process. Comparisons were made using statistical analyses to determine the efficiency and accuracy of the proposed AI-models for predicting maximum pitting depth in oil and gas pipelines.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2021)

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Article Engineering, Civil

Failure risk analysis of pipelines using data-driven machine learning algorithms

Ram K. Mazumder et al.

Summary: This study utilizes machine learning to develop an alternative to computationally intensive analytical approaches for evaluating failure risk of steel oil and gas pipelines. By generating a pipeline database and evaluating true burst failure pressure probabilistically, pipelines are classified based on their risk probability, with XGBoost algorithm recommended for future analysis. Machine learning algorithms show higher computational efficiency in failure risk analysis compared to physics-based models, with XGBoost being the optimal choice for prediction.

STRUCTURAL SAFETY (2021)

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Article Computer Science, Interdisciplinary Applications

A Perturbed Markovian process with state-dependent increments and measurement uncertainty in degradation modeling

M. Oumouni et al.

Summary: A new statistical, data-driven state-dependent and perturbed model is proposed in this study to consider state dependence in temporal increments and measurement uncertainty for more accurate prediction in degradation modeling.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

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Article Materials Science, Multidisciplinary

Corrosion prediction for bulk carrier via data fusion of survey and experimental measurements

Z. Wang et al.

Summary: Accurate corrosion predictions are essential for safe and optimized marine asset designs. This paper introduces an interpretable machine learning algorithm based on data fusion of ship survey and experimental measurements to improve prediction accuracy by at least 82% compared to traditional empirical models, with a mean absolute error reduced to 0.10 mm.

MATERIALS & DESIGN (2021)

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Article Engineering, Marine

Microbiologically influenced corrosion (MIC) management using Bayesian inference

Mohammad Yazdi et al.

Summary: Microbiologically influenced corrosion (MIC) is a significant cause of hazardous hydrocarbon release and fires. A new MIC management methodology using Continuous Bayesian Network (CBN) technique with Hierarchical Bayesian Analysis (HBA) is proposed in this paper to accurately monitor MIC activity and develop effective strategies. The integration of HBA and CBN helps overcome limitations and uncertainties in the Bayesian network, providing precise parameters values for failure probability and MIC occurrence rate.

OCEAN ENGINEERING (2021)

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Article Engineering, Environmental

A probabilistic model to estimate microbiologically influenced corrosion rate

Abdul-Waris Dawuda et al.

Summary: This study introduces a new probabilistic methodology and model for estimating microbiologically influenced corrosion (MIC) rate. The results indicate that the metabolism of iron-oxidizing bacteria and methanogens are the most probable contributors to the corrosion rate.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2021)

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Article Engineering, Environmental

Evolution of Safety and Security Risk Assessment methodologies towards the use of Bayesian Networks in Process Industries

Priscilla Grace George et al.

Summary: With the evolution of safety risk assessment methodologies, the emergence of Bayesian Networks has provided a dynamic tool for handling safety and security risk assessments in process industries. Traditional quantitative risk assessment techniques are limited in dynamic environments, whereas Bayesian Networks have the advantage of handling multi-state variables and representing conditional dependence between events.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2021)

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Article Engineering, Marine

Probabilistic fatigue failure assessment of free spanning subsea pipeline using dynamic Bayesian network

Xinhong Li et al.

Summary: The probabilistic modeling of free spanning subsea pipeline fatigue failure is a challenging task due to its dynamic characteristics and uncertain information. This paper proposes a dynamic probabilistic methodology to capture the uncertainty and time dependence of fatigue failure scenario. By using Dynamic Bayesian Network, the model can estimate the probabilities of basic causations and assess the dynamic probability of fatigue failure at different time slices.

OCEAN ENGINEERING (2021)

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Article Engineering, Environmental

A data-driven corrosion prediction model to support digitization of subsea operations

Xinhong Li et al.

Summary: Corrosion prediction is crucial for subsea process system in the industry 4.0 environment. A hybrid data-driven model integrating PCA, ABC, and SVR was proposed for corrosion degradation modeling. The PCA-ABC-SVR model demonstrated superior prediction accuracy and robustness.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2021)

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Article Engineering, Environmental

Data-driven operational failure likelihood model for microbiologically influenced corrosion

Mohammad Zaid Kamil et al.

Summary: The paper discusses the threat of microbiologically influenced corrosion (MIC) and the complexity of current MIC risk assessment models. A model utilizing available data for learning is proposed to assess the likelihood of MIC, aiding in decision-making to control and mitigate MIC threats.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2021)

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Article Energy & Fuels

General corrosion vulnerability assessment using a Bayesian belief network model incorporating experimental corrosion data for X60 pipe steel

Solomon Tesfamariam et al.

Summary: The study developed a Bayesian belief network (BBN) model coupled with time marching simulation to quantify corrosion defects and failure pressure capacity of pipelines, and identified critical parameters for corrosion rate (CR) through a parametric study. The proposed BBN model is shown to be beneficial in integrating experimental and analytical models to improve the reliability of pipelines under various conditions.

JOURNAL OF PIPELINE SCIENCE AND ENGINEERING (2021)

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Article Energy & Fuels

Cross-country pipeline inspection data analysis and testing of probabilistic degradation models

Faisal Khan et al.

Summary: This paper examines ILI data of a cross-country pipeline and presents stochastic models and parameter definitions crucial for pipeline risk and reliability studies. Through detailed data processing and statistical analysis, it provides important references for establishing the reliability and risk assessment of pipeline safe operation.

JOURNAL OF PIPELINE SCIENCE AND ENGINEERING (2021)

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Review Energy & Fuels

Global and local parameters for characterizing and modeling external corrosion in underground coated steel pipelines: A review of critical factors

Changkyu Kim et al.

Summary: The study discusses critical factors and methods for external corrosion assessments of pipeline systems, including time, space, laboratory testing, and theoretical prediction. A multiscale approach is used to integrate factors from various sources, emphasizing the importance of macro parameters and spatial distribution.

JOURNAL OF PIPELINE SCIENCE AND ENGINEERING (2021)

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Quantitative Assessment of Failure Probability of Underground Natural Gas Storage Wells Using an Integrated Bow-Tie Bayesian Network Approach

Arun Agarwal et al.

Summary: The storage of natural gas in underground reservoirs is crucial for managing supply and demand cycles, but leakage can have severe safety and environmental consequences. This study introduces an integrated approach using Bow-tie and Bayesian Network models to assess the probability of gas release and visualize operational hazards for safe management. The Bow-tie model identifies hazards and barriers to prevent or mitigate gas release events, while the Bayesian Network model quantifies the failure probabilities of identified barriers for wellhead and sub-surface systems. Sensitivity and value of information analyses were conducted to further understand the risk factors.

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Integrated pipeline corrosion growth modeling and reliability analysis using dynamic Bayesian network and parameter learning technique

Wei Xiang et al.

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Corrosion defect modelling of aged pipelines with a feed-forward multi-layer neural network for leak and burst failure estimation

Chinedu Ossai

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Prediction of maximum pitting corrosion depth in oil and gas pipelines

Mohamed El Amine Ben Seghier et al.

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Pitting corrosion modelling of X80 steel utilized in offshore petroleum pipelines

Ehsan Arzaghi et al.

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Fang Shi et al.

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Microbiologically influenced corrosion: a review of the studies conducted on buried pipelines

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External corrosion pitting depth prediction using Bayesian spectral analysis on bare oil and gas pipelines

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A Framework for Risk-Based Integrity Assessment of Unpiggable Pipelines Subject to Internal Corrosion

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A stochastic defect growth model for reliability assessment of corroded underground pipelines

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Assessment on external corrosion rates for API pipeline steels exposed to acidic sand-clay soil

L. M. Quej-Ake et al.

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

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Developing a dynamic model for pitting and corrosion-fatigue damage of subsea pipelines

Ehsan Arzaghi et al.

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Neural network modelling of high pressure CO2 corrosion in pipeline steels

Muhammad Hashim Abbas et al.

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Modeling localized corrosion of pipeline steels in oilfield produced water environments

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A review on pipeline corrosion, in-line inspection (ILI), and corrosion growth rate models

H. R. Vanaei et al.

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Accelerated pitting corrosion test of 304 stainless steel using ASTM G48; Experimental investigation and concomitant challenges

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Sensitivity of system reliability of corroding pipelines to modeling of stochastic growth of corrosion defects

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Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines

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