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

Soomro, Afzal Ahmed; Mokhtar, Ainul Akmar; Kurnia, Jundika C.; Lashari, Najeebullah; Lu, Huimin; Sambo, Chico

doi:10.1016/j.engfailanal.2021.105810

Cited by 94 publications

(32 citation statements)

References 79 publications

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“…Te deepest corrosion depth thus obtained is highly random, and the deepest corrosion depth of multiple locations cannot be simply averaged, and the deepest corrosion depth cannot be used as a standard for life prediction. Te most reasonable method is to statistically process the detected data, obtain statistical parameters λ and α, and then calculate the probability that the deepest corrosion depth does not exceed a certain value according to formula (1).…”

Section: Buried Corrosion Pipeline Remaining Life Prediction Method: ...mentioning

confidence: 99%

“…As a typical application of green synthesis and metal oxide composite materials, mechanical damage such as scratches, scratches, pits, and mussel eyes of the pipeline itself are internal factors that afect its service life, while soil corrosion, bacterial corrosion, and stray current corrosion in primary batteries are external factors that afect its service life. In order to inhibit the corrosion of the outer wall of the pipeline by the surrounding environment, the long-distance pipeline generally adopts the dual protection measures of anticorrosion layer and cathodic protection [1][2][3]. However, due to the limited construction conditions on site, in the construction process of the pipeline, the quality of the pipeline anticorrosion coating cannot be guaranteed [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Li¹,

Li²,

Ma³

et al. 2023

Journal of Chemistry

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Green synthesis and metal oxide composites have attracted much attention from researchers of industry and academia. As a typical application of green synthesis and metal oxide composites, the continuous change of industrial technology and the continuous improvement of the social and economic level, the demand for oil and gas are also increasing. However, the spatial gap between the place of origin and the place of demand for oil and gas resources is large, so the long-distance oil and gas pipeline came into being. However, under the action of time, coupled with the corrosion effect of the soil due to deep burial, some pipelines have serious aging and corrosion phenomena. Therefore, in order to give corresponding guarantees for economic development, we need to conduct in-depth research and analysis of the corrosion of oil and gas long-distance pipelines and give effective solutions. In this paper, the corrosion rate prediction of buried oil and gas pipelines is studied in Changqing gas field. By improving the inertial weights and learning factors of the traditional particle swarm algorithm, the parameters of the generalized regression neural network are optimized and selected, and the corrosion rate prediction model of buried pipelines is finally constructed. Comparative analysis with other swarm intelligence algorithms shows that the improved particle swarm algorithm has stronger convergence ability and higher prediction accuracy than the BP model and SVM model. In addition, based on the detection data collected at the site of the gathering and transportation pipeline in Changqing gas field, this paper uses the extreme value distribution theory and the local corrosion progress formula to establish a prediction model for the residual life of corrosion of buried pipelines. The model established in this paper can effectively determine the risk pipe segment of buried pipeline and provide a decision-making basis for pipeline management departments. The work provides an important application guidance to green synthesis and metal oxide composites.

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Section: Buried Corrosion Pipeline Remaining Life Prediction Method: ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Li¹,

Li²,

Ma³

et al. 2023

Journal of Chemistry

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show abstract

“…The interface separating the two classes comprises a small portion of the original dataset, known generally as support vectors, which tend to maximize the gap between the classes. SVM predicts by analyzing input data as a linear function, but it can also be extended to a nonlinear model class, generally referred to as a kernel hypothesis class, suited particularly for nonlinear, and multidimensional small‐size dataset having local minima (Donti & Kolter, 2021; Soomro et al, 2022). Some of the mapping functions employed by SVM for handling nonlinear dataset include radial basis functions (RBFs), polynomials, and sigmoid.…”

Section: Overview Of Orc Plant and Data‐driven Modeling Approachmentioning

confidence: 99%

Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

Oyekale

Oreko

2023

WIREs Energy & Environment

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The organic Rankine cycle (ORC) is widely acknowledged as a sustainable power cycle. However, the traditional approach commonly adopted for its optimal design involves sequential consideration of working fluid selection, plant configuration, and component types, before the optimization of state parameters. This way, the design process fails to achieve an optimal design in most cases, since the process relies heavily on empirical judgments. To improve the design process, researchers have been exploring lately the suitability of machine learning techniques. It is however not clear yet if data‐driven designs of ORC plants are practically viable and accurate. To bridge this gap, this article reviews literature studies in the field. Overviews were first presented on the ORC technology and machine learning modeling approaches. Next, studies that applied machine‐learning methods for the design and performance prediction of ORC plants were discussed. Furthermore, studies that focused on ORC machine learning optimizations were discussed. The artificial neural network (ANN) approach was observed as the technique most frequently applied for ORC design and optimization. Additionally, researchers agree in general that machine‐learning methods can achieve accurate results, with significant reductions of computational time and cost. However, there is the risk of using inadequate data size in the machine learning design approach, or insufficient data set training time, all of which can affect accuracy. It is hoped that this effort would spur the practical implementation of machine learning techniques in the future design and optimization of ORC plants, toward the achievement of more sustainable energy technology. This article is categorized under: Sustainable Energy > Energy Efficiency Sustainable Energy > Other Renewables

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“…Machine learning (ML) has been employed in various sectors, such as oil and gas and the environmental sector. Different ML like artificial neural networks (ANN), group methods of data handling (GMDH), adaptive neuro-fuzzy inference systems (ANFIS), function networks (FN), support vector machines (SVM), Gaussian process regression (GPR), random forest (RF), regression tree ensembles, deep neural networks, convolution neural networks (CNN), long short-term memory (LSTM) network, etc., can be used to predict certain parameters from easily available data without incurring additional costs. − Due to the fact that the capacity of coal to sequestrate CO 2 depends on the wettability of the formation, which is measured by contact angle (CA), ref predicted the CA of coal formation toward CO 2 wetness, implying that CO 2 sorption capacity increases as the CA angle increases. The ML techniques used were ANN and ANFIS.…”

Section: Wettability Alteration During Co2-ecbm Technology Applicationmentioning

confidence: 99%

Recent Advances in Carbon Dioxide Sequestration in Deep Unmineable Coal Seams Using CO₂-ECBM Technology: Experimental Studies, Simulation, and Field Applications

Mwakipunda,

Wang,

Mgimba

et al. 2023

Energy Fuels

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CO 2 -enhanced coalbed methane (CO 2 -ECBM) technology helps to store CO 2 while producing a clean source of energy (CH 4 ) through the sorption process. This technique can sequester much CO 2 at low temperatures and pressures while recovering CH 4 , which will help offset the associated costs, such as capturing injection gases, drilling and completion infrastructure, compression, and injection expenses. This review paper critically analyzes the CO 2 sequestration potentiality in deep unmineable coal seams. The results revealed that, despite several researchers' insights from proposed concepts, experimental data, modeling and simulations, and pilot tests, there are no reported full field CO 2 -ECBM technology applications for CO 2 sequestration and CH 4 recovery because implementing the projects is uneconomical. Also, CO 2 sequestration and CH 4 recovery effectiveness on coal seams depend on wettability changes of the CO 2 −H 2 O−coal system. The identified research gaps and challenges in this paper are going to help various researchers and shareholders in conducting extra investigations toward full field application of CO 2 sequestration while simultaneously producing a clean source of energy (CH 4 ) in deep unmineable coal seams to meet the Paris climate summit agreement to achieve net zero emissions by 2050 and a maximum global temperature rise of 1.5 °C.

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

Cited by 94 publications

References 79 publications

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

Recent Advances in Carbon Dioxide Sequestration in Deep Unmineable Coal Seams Using CO₂-ECBM Technology: Experimental Studies, Simulation, and Field Applications

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

Cited by 94 publications

References 79 publications

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field

Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

Recent Advances in Carbon Dioxide Sequestration in Deep Unmineable Coal Seams Using CO2-ECBM Technology: Experimental Studies, Simulation, and Field Applications

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Product

Resources

About

Recent Advances in Carbon Dioxide Sequestration in Deep Unmineable Coal Seams Using CO₂-ECBM Technology: Experimental Studies, Simulation, and Field Applications