AMPP Store - Products tagged with 'corrosion prediction'

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51318-10798-Development of a Practical Model for H2S Corrosion Prediction for Upstream Oil and Gas Applications

Product Number: 51318-10798-SG

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

Publication Date: 2018

$20.00

An improved sour corrosion model was developed based on: • A bare steel sour electrochemical corrosion model, derived from published literature • Mechanisms that affect scale performance and trigger localised corrosion • A specific elemental sulphur degradation mechanism • Corrosion mitigation strategy

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A Damage-Based Approach and Predictive Modeling of Corrosion in Substrate/Coating Systems Using Artificial Neural Networks

Product Number: 51324-21150-SG

Author: Victor Ponce; Walter Tarr; Allison Mahood; Heather Eich; Homero Castaneda

Publication Date: 2024

$40.00

The long-term performance of three different automotive surface coatings (physical barrier, sacrificial, and hybrid) was predicted using electrochemical impedance spectroscopy (EIS). Corrosive conditions faced by vehicles in the field, such as deicing, can be simulated using accelerated methods. The coating/metallic substrate interface experiences various degradation mechanisms during exposure to harsh conditions. In this work, real-time measurements were performed via EIS testing to characterize the degradation and corrosion mechanism of coating and substrate. After the real-time measurements, a mathematical framework based on mechanistic and machine-learning concepts was developed. Phase angle plots from EIS were utilized to monitor the state of the coating during steady-state conditions and train the Artificial Neural Network (ANN) as an arrangement of Time Series Prediction (TSP). The transport processes, activation, and interface interaction with the corrosive environments were analyzed as a corrosion mechanism and were predicted via the ANN model. The ANN has predicted the coating performance for several years, and the experimental results have been validated by employing scanning electron microscopy (SEM) imaging. Each coating condition has been validated via SEM imaging at the initial state and when the coating protection is activated.

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AMPP Guide 21555-2024, Water Analysis for Corrosion Prediction - Sampling Analysis and Interpretation

Product Number: AMPP Guide 21555-2024

Publication Date: 2024

$109.00

This Guide focuses on those water chemistry parameters that are key for corrosion prediction. Additional parameters may be required for other materials and corrosion evaluations and decisions. This Guide does not provide a procedure for how, when, and where to take water samples or how to preserve samples.

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An Artificial Intelligence Based Application to Predict Internal Corrosion Risk in Produced Gas Flowlines

Product Number: MECC23-20098-SG

Author: Mohammed S. Alqahtani; Muhammad Sohaib Khan

Publication Date: 2023

$20.00

Corrosion in pipes is a major challenge for the oil and gas industry which leads to expensive failures, production loss and safety issues. The corrosion problems in gas producing operations are complex and depends on lot of factors including but not limited to process stream chemistry, material of construction and operating conditions.

CO2 corrosion of carbon steel (C-steel) materials is one of the main corrosion mechanisms encountered in the oil and gas industry.

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Atmospheric Corrosion Through the Eyes of a Computer Simulation

Product Number: 51321-16394-SG

Author: Agnieszka Franczak/Leslie Bortels/Christophe Baeté /Bart Van den Bossche

Publication Date: 2021

$20.00

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Corrosion and Risk Modelling Analytics

Product Number: 51324-20833-SG

Author: B. R. Shivadhanush; N. J. Laycock; M. Geeta; F. Samie; P. Panchami

Publication Date: 2024

$40.00

The CARMA (Corrosion and Risk Modelling Analytics) project aims to provide improved corrosion prediction capability by using Machine Learning techniques. For the selected system, the initial CARMA model is trained by using a combination of online process monitoring data as the input, combined with online corrosion monitoring data as the output. Once trained and validated, the resulting model can then be used to predict corrosion rates in similar systems or in other parts of the same system, without the need to install corrosion monitoring devices or carry out routine physical inspections at the same frequency as in the past. This paper provides an overview of the model development process for one example system, incorporating data from both online monitoring in the plant and from laboratory experiments.

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Guidelines for Using Water Analysis for Corrosion Prediction- Sampling Analysis and Data Interpretation

Product Number: 51323-19421-SG

Author: Y. Zheng, J. Sonke, B. Brown

Publication Date: 2023

$20.00

Various corrosion prediction tools for CO₂/H₂S corrosion have been developed in the past thirty years. For corrosion analysis in oil and gas production, the water chemistry largely determines the corrosion rate which is mainly driven by in-situ pH.

The in-situ water or brine is pressurized with acid gases (CO₂/H₂S) which results in a decrease in pH and typically an increase in the corrosion rate.

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High Temperature Corrosion Quantification in Renewable Diesel and Sustainable Aviation Fuel Applications

Product Number: MECC23-20056-SG

Author: Sridhar Srinivasan, Gerrit Buchheim

Publication Date: 2023

$20.00

The authors have developed and introduced a molecular mechanistic model that quantifies and predicts simultaneous naphthenic acid and sulfidation (SNAPS) corrosion rates. This was subsequently presented as a definitive mechanistic corrosion prediction framework describing the molecular basis of the model’s reactions, kinetics, and mass transport of reactive organic sulfur compounds (ROSC) to vessel walls . In this molecular model, sulfidation corrosion is calculated for direct heterolytic reaction of ROSC with solid surfaces. As recently reported, % total S and ppm mercaptans are used as input for the ROSC reactions in the model (Figure 1).

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Improved Unit Economics, Crude Blending and Throughput Management Using a Novel High Temperature Corrosion Prediction Framework

Product Number: MECC23-20054-SG

Author: Sridhar Srinivasan, Gerrit Buchheim

Publication Date: 2023

$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. 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. 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.

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Incorporating Monte Carlo Analysis into a State-of-the-Art Corrosion Prediction System

Product Number: 51323-18910-SG

Author: Andrew Simm, Daniel Thomas, Gareth John, Steve Hodges

Publication Date: 2023

$20.00

Over the years there have been several different corrosion modelling software packages developed to provide predicted (estimated) corrosion rates for use in the oil & gas industries. Many are based on the original work of DeWaard & Milliams which provided a best-fit statistical model to corrosion rates measured in flow loop laboratory tests conducted at the IFE (Institutt For Energiteknikk) in Norway ; covering (initially) just partial pressure of CO2, temperature, liquid flow velocity and pH (typically as bicarbonate and dissolved CO2).

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IR 4.0 Integrity Management Using Data Analytics

Product Number: MPWT19-15487

Author: Dr. Haaken Ahnfelt, Dr. Luis Caetano, Dr. Hilde Aas Nøst, Dr. Knut Nordanger, Reidar Kind, Dr. Zeeshan Lodhi, Dr. Lay Seong Teh

Publication Date: 2019

$0.00

One of the pillars of the fourth industrial revolution (4IR) is to let machines make decisions on behalf of humans; this paper describes new technology that allows machines to decide inspection programs and field validation and testing of results. The technology described is a part of integrity management, and uses data, statistics and expert decisions to design inspection programs. These inspection programs are an important part of the safeguarding of equipment to maintain production and safety.
This technology is a data-driven predictive model of material loss from corrosion, based on domain expert input and historical data in the form of non-destructive testing (NDT) tests. The technology trends is based on historical data and SME input, while accounting for uncertainties in NDT measurements, with uncertainties in historical trends and uncertainties in future trends. This produces a more realistic failure prediction to enhance existing RBIs and adds safety by improving on early detection of trends in data. In total, this enables the machine to update inspection plans autonomously, reducing the number of inspections significantly.
The paper also describes how the technology can be developed further to use production data and integrity operating windows to improve predictions, deal with localised corrosion and assess if the test points on a corrosion circuit are sufficient, can be reduced in number or should be manually evaluated by adding more test points.

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Prediction Of The Mitigated Corrosion For Inhibitor Applications In Oil And Gas Operations

Product Number: 51321-16704-SG

Author: J. Sonke; Y. Zheng; J. Moloney

Publication Date: 2021

$20.00

Products tagged with 'corrosion prediction'

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