A Machine Learning-Based Corrosion Level Prediction in the Oil and Gas Industry

Authors

Alok Madamanchi, Arkansas State University - Jonesboro
Fazla Rabbi, Arkansas State University - JonesboroFollow
Alexandr M. Sokolov, Arkansas State University - JonesboroFollow
Niamat Ullah Ibne Hossain, Arkansas State University - JonesboroFollow

Document Type

Conference Proceeding

Publication Title

Engineering Proceedings

Abstract

The oil and gas industries are facing several industry-specific barriers which include process improvements, ignorance of complex operations, equipment management, logistics operations, etc. However, the enormous amount of data that are being produced by this industry, and the implementation of analytics, can overcome these challenges. This project aims to put forth a step-by-step procedure to preprocess data and utilize data analytics tools to provide meaningful insights to make business intelligent decisions for companies. The results from this research will be presented in data preprocessing procedures, the use of analytics, and machine learning applications in the O&G industry.

DOI

10.3390/engproc2024076038

Publication Date

10-23-2024

Comments

Presented at the 1st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024), Regina, Canada, 27–29 June 2024.

Recommended Citation

Madamanchi, Alok; Rabbi, Fazla; Sokolov, Alexandr M.; and Hossain, Niamat Ullah Ibne, "A Machine Learning-Based Corrosion Level Prediction in the Oil and Gas Industry" (2024). Engineering & Construction Management Faculty Publications. 2.
https://arch.astate.edu/ecs-emcmfac/2