AMPP Store - Products tagged with 'prediction'

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51318-11223-Evaluation of Top-of-Line Corrosion Model for Multiphase Oil and Gas Environments

Product Number: 51318-11223-SG

Author: H. Li / K. M. Yap / S. Srinivasan

Publication Date: 2018

$20.00

The model was evaluated and effects of various parameters on corrosion rates are described. Corrosion rates obtained from the model are compared with actual field and lab testing data as a basis to quantify accuracy and efficacy.

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A Prediction and Inspection Technology for Detecting the Corrosion Under Insulation

Product Number: MPWT19-15162

Author: Ayman Amer, Ali Al Shehri, Vincent Cunningham, Ihsan Taie

Publication Date: 2019

$0.00

Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets for which the oil and gas industry is not immune. Over the last few decades, both downstream and upstream industry segments have recognized the magnitude of CUI and challenges faced by the industry in its ability to handle CUI risk-based assessment, predictive detection and inspection of CUI. It is a concern that is hidden, invisible to inspectors and prompted mainly by moisture ingress between the insulation and the metallic pipe surface. The industry faces significant issues in the inspection of insulated assets, not only of pipes, but also tanks and vessels in terms of detection accuracy and precision. Currently, there is no reliable NDT detection tool that can predict the CUI spots in a safe and fast manner. In this study, a cyber physical-based approach is being presented to identify susceptible locations of CUI through a collection of infrared data overtime. The experimental results and data analysis demonstrates the feasibility of utilizing machine-learning techniques coupled with thermography to predict areas of concern. This is through a simplified clustering and classification model utilizing the Convolutional Neural Networks (CNN). This is a unique and innovative inspection technique in tackling complex challenges within the oil and gas industry, utilizing trending technologies such as big data analytics and artificial intelligence.

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Advances In Digital Twin And AI To Predict And Manage External Corrosion - A Smart Tool For Decision Makers

Product Number: 51322-17991-SG

Author: Otavio Correa, Jorge Seleme Mariano

Publication Date: 2022

$20.00

The Brazilian cost of corrosion was estimated at 3% of the GPD in 2018, that percentage is equivalent to approximately $US 49 billion, according to an ABRACO⁽¹⁾ journal released in 2020.¹ It is estimated that from this cost $US 19 billion could have been saved through anticorrosive actions. In another research conducted by the EPRI⁽²⁾ the results showed that at least 22% of corrosion costs could be avoided through adequate mitigating actions.²

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Advances In Digital Twin And AI To Predict And Manage External Corrosion - A Smart Tool For Decision Makers

Product Number: 51322-17992-SG

Author: Otavio Correa, Jorge Seleme Mariano

Publication Date: 2022

$20.00

The Brazilian cost of corrosion was estimated at 3% of the GPD in 2018, that percentage is equivalent to approximately $US 49 billion, according to an ABRACO¹ journal released in 2020¹. It is estimated that from this cost $US 19 billion could have been saved through anticorrosive actions. In another research conducted by the EPRI² the results showed that at least 22% of corrosion costs could be avoided through adequate mitigating actions².

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An Enhanced Prediction Model For Simultaneous Naphthenic Acid And Sulfidic Corrosion Quantification

Product Number: 51322-17901-SG

Author: Winston Robbins, Sridhar Srinivasan, Abbey Wing, Gerrit Buchheim

Publication Date: 2022

$20.00

Sulfur and acidic impurities in crude oils pose serious hot oil corrosion problems in crude distillation units (CDU) and associated vacuum distillation units (VDU), especially with the increase in processing of lowquality, opportunity crudes.^1-4 In the range of 200-400˚C, reactive sulfur compounds cause sulfidation corrosion of ferritic carbon and chrome steels in CDU, VDU, and front ends of downstream units operating at hot oil temperatures.^5-7 Over the same temperature range, naturally occurring carboxylic acids in crudes can be so aggressive that higher alloy, austenitic stainless steels containing >2.5% Mo are required for processing high acid oils.^8-11 Although sulfidation and acid corrosion occur over the same temperature range, they differ in two significant ways. Sulfidation forms an iron sulfide solid that is semiresistant to further corrosion and relatively insensitive to flow velocity. Acids form oil soluble organic salts that can be washed away especially in areas of high turbulence.^12-14

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Combining RMU, Annual Survey, and Groundbed System Design Data to Predict Capital Replacement Needs

Product Number: 51324-20805-SG

Author: Matt Barrett; Mill Jawed; Dylan Bolch; William Maize; Tony da Costa

Publication Date: 2024

$40.00

We present a procedure to estimate the remaining service life of an impressed current groundbed using data analytics techniques to combine the various manually and remotely collected measurements, and rectifier groundbed metadata. Multiple data sources, including commissioning data, annual survey data, remote monitoring data were combined to create a consolidated dataset spanning over ten years for each impressed current cathodic protection (ICCP) rectifier groundbed. Using advanced analytics algorithms applied to ICCP rectifier groundbed sites, weighting factors have been calculated to determine the influence of each dataset on the estimated groundbed remaining service life. This work builds on previous presentations at AMPP Corrosion 2021 and 2022, where seasonality and historic trends in rectifier resistance measurements were modelled. By including additional datasets describing the location at the time of installation and the historic survey measurements, a more robust prediction can be made.

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Development Of A New Top-Of-Line Corrosion Model For Sweet Wet Gas Pipelines

Product Number: 51321-16812-SG

Author: Y. Zheng; J. Sonke; W. M. Bos

Publication Date: 2021

$20.00

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Enhanced Corrosion Prediction Model for Multiphase Oil and Gas Production Systems

Product Number: 51317--9419-SG

ISBN: 9419 2017 CP

Author: Hui Li

Publication Date: 2017

$20.00

A top-of-line corrosion (TLC) model integrated into a CO2/H2S corrosion prediction model. The TLC model determines the top of the line corrosion rate of carbon steel based on water chemistry and film-wise condensation rate. The effect of various glycols, such as Monoethylene Glycol, Diethylene glycol and Triethylene Glycol, are included.

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Predicting Corrosion Severity of Pipeline Steels in Supercritical CO2 Environments Using Supervised Machine Learning

Product Number: 51324-20803-SG

Author: Emily Seto; Meifeng Li; Jing Liu

Publication Date: 2024

$40.00

The importance of effective corrosion management in carbon capture, utilization, and storage (CCUS) networks has significantly increased. Captured CO2 is often transported in the supercritical state (s-CO2) and can contain impurities like H2O, O2, SOx, or NOx. While repurposing existing oil and gas pipelines for s-CO2 transport has been suggested, further testing and risk assessment is required to validate this strategy and its associated risks. Given the substantial amount of corrosion data available from recent corrosion studies, machine learning (ML) has emerged as a promising tool for corrosion prediction and management. This study aims to utilize supervised ML techniques to predict the corrosion severity of pipeline steels operating in s-CO2 systems. The selected algorithms, random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) were trained on a comprehensive data set of X-series pipeline steels which includes corrosion rates, impurity levels, temperatures, pressures, and exposure times. Additional testing data set and error and accuracy scores were used to determine the most accurate algorithm. An additional experimental testing was performed to verify the predictions of the model. It was found that the RF model had the best accuracy of 65.3% out of the three tested models and KNN had the worst accuracy of 59.2%. In multiple impurity environments the RF model was able to accurately predict corrosion severity but overestimated corrosion severity in environments with short exposure times.

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Predicting Hot Oil Corrosion: A Framework for Quantifying Reactive Organic Sulfur Compounds in Crude Unit Process Streams

Product Number: 51323-19105-SG

Author: Winston Robbins, Sridhar Srinivasan, Gerrit Buchheim

Publication Date: 2023

$20.00

In Corrosion/2021, the authors introduced a molecular mechanistic model that quantifies and predicts SNAPS corrosion rates. During Corrosion/2022, we presented the mechanistic corrosion prediction framework describing the molecular basis of the model’s reactions, kinetics, and mass transport of ROSC to vessel walls. In this molecular model, sulfidation corrosion is calculated for direct heterolytic reaction of ROSC with solid surfaces.

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SP0210-2010 (Spanish), Evaluación directa para confirmar la corrosión exterior de tuberías

Product Number: 21179-SG

ISBN: 1-57590-240-0

Publication Date: 2010

$179.00

Asociación Nacional de Ingenieros de Corrosión. (NACE). Evaluación confirmatoria directo de corrosión externa (ECCDA) para tuberías ferrosas enterradas.

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SP0210-2010-SG (Chinese) Pipeline External Corrosion Confirmatory

Product Number: 21161-SG

ISBN: 1-57590-240-0

Publication Date: 2010

$179.00

NACE external corrosion confirmatory direct assessment (ECCDA) process for buried onshore ferrous piping systems. The process is also predictive.

Products tagged with 'prediction'

Association for Materials Protection and Performance