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Keywords: machine learning
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Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19333
... monitor the rectifier and sensor outputs. The rectifier and sensor outputs are transferred from the RMCU/RMU via cloud to the RMU/RMCU company's website where we can retrieve the data. well integrity produced water discharge subsurface corrosion artificial intelligence machine learning...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19464
... corrosion inhibition h2s management subsurface corrosion north america government machine learning corrosion management united states government oilfield chemistry performance corrosion sensor classification corrosivity category material protection publication conductance iso 9223...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19039
... prioritization of mitigating and remediating measures while balancing system risk and system budget, resulting in safe, cost effective and efficient integrity decisions. Artificial Intelligence (AI) and Machine Learning (ML) technologies have been applied for years in many industrial fields...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19209
... prediction: data analytics, semimechanistic approaches and/or other advanced analytic tools such as machine learning artificial intelligence. Therefore, we proposed to explore and test (data permitting) existing methodologies reported in the public domain or literature and/or new methodologies and validate...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19404
... midstream oil & gas subsurface corrosion materials and corrosion interaction united states government riser corrosion artificial intelligence flowline corrosion north america government corrosion defect association machine learning adjacent defect defect interaction...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19412
..., and steam. Predictive models for the oxidation rate constant were developed using machine learning and analyzed to provide insights into the leading factors producing corrosion resistance in these materials. INTRODUCTION High-temperature service places severe constraints on materials selection due...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19410
... well integrity upstream oil & gas production chemistry pipeline corrosion h2s management subsurface corrosion united states government materials and corrosion artificial intelligence corrosion management riser corrosion oilfield chemistry machine learning material protection...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19289
... thickness. A robust pipe wall thickness estimation method based on conventional (i.e., non-machine learning) processing methods has been proposed by the authors [1]. However, the method fails to provide robust thickness measurements in cases with non-uniform wall thickness, which is typically the case when...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19393
... ABSTRACT Integrated External Corrosion Management (IECM) is a novel framework developed for pipeline operators to model, identify, and optimize external corrosion risk and costs using a data-driven approach. Over the last decade, Machine Learning (ML) has transformed industries from consumer...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19310
... ABSTRACT Machine learning as a tool for automation has grown exponentially in the past two decades. Growth has come from innovations in hardware, such as powerful graphics processing units (GPUs) and cloud computing. Along with hardware advances, there has been an explosion in software...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19416
... the corrosion data and the meteorological data. Machine learning through feature selection and regression approaches was used to identify leading meteorological factors that quantitatively control extent of corrosion. Key features determined to have a quantitative effect on corrosion rate and mass loss per unit...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19320
... materials and corrosion h2s management subsurface corrosion artificial intelligence riser corrosion metals & mining machine learning corrosion inhibition defect frequency impedance association performance voltage accuracy steel mining ampp responsibility publication rights oilfield...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19300
... government pipeline corrosion artificial intelligence flowline corrosion responsibility ampp europe government machine learning material protection deep learning materials and corrosion segmentation permission epoch confusion matrix performance detection rights association background...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19106
... corrosion management h2s management asia government scientific discovery saudi arabia government corrosion inhibition association oilfield chemistry machine learning material protection upstream oil & gas downstream oil & gas creativity & intelligence production...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-18783
... amounts of measurement data. With this approach, the user can overcome the large data requirements of machine learning while building tailored models that outperform traditional analytical and statistical tools. The effectiveness of this modeling framework in helping scientists reduce uncertainty early...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-18821
... performance ampp association riser corrosion materials and corrosion flowline corrosion publication well integrity artificial intelligence machine learning mechanism control chart rights linear regression corrosion rate utm figure responsibility permission refinery latest revision Paper...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-19564
... on visual interpretation and comparison with reference photos. Using new machine learning techniques coupled with advances in artificial intelligence, the depth of knowledge and experience required to accurately judge the classification of a water-jetted substrate can be greatly reduced. Determining through...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-18981
... corrosion inhibition responsibility pipeline corrosion ampp association geologist riser corrosion subsurface corrosion saudi arabia government machine learning material protection performance inlet temperature permission literature brown publication co 2 rights steel algorithm...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-18995
... ABSTRACT The aim of this work is to define, implement, test, and validate an AI methodology using existing machine learning (ML) algorithms to predict sand erosion in 90° elbows for a broad range of multiphase operating conditions. Based on information obtained from the experimental UT wall...
Proceedings Papers

Paper presented at the AMPP Annual Conference + Expo, March 19–23, 2023
Paper Number: AMPP-2023-18927
... ABSTRACT This study focuses on applying machine learning algorithms to predict the corrosion depth of facility station piping assets, as well as comparing the computational accuracy of the predicted corrosion depth based on various machine learning algorithms. Simulated corrosion testing data...

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