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Implementing Genetic Algorithms to Assist Oil and Gas Pipeline Integrity Assessment and Intelligent Risk Optimization

Implementing Genetic Algorithms to Assist Oil and Gas Pipeline Integrity Assessment and Intelligent Risk Optimization

Gustavo Calzada-Orihuela, Gustavo Urquiza-Beltrán, Jorge A. Ascencio, Gerardo Reyes-Salgado
Copyright: © 2017 |Volume: 7 |Issue: 4 |Pages: 20
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781522513285|DOI: 10.4018/IJOCI.2017100104
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MLA

Calzada-Orihuela, Gustavo, et al. "Implementing Genetic Algorithms to Assist Oil and Gas Pipeline Integrity Assessment and Intelligent Risk Optimization." IJOCI vol.7, no.4 2017: pp.63-82. http://doi.org/10.4018/IJOCI.2017100104

APA

Calzada-Orihuela, G., Urquiza-Beltrán, G., Ascencio, J. A., & Reyes-Salgado, G. (2017). Implementing Genetic Algorithms to Assist Oil and Gas Pipeline Integrity Assessment and Intelligent Risk Optimization. International Journal of Organizational and Collective Intelligence (IJOCI), 7(4), 63-82. http://doi.org/10.4018/IJOCI.2017100104

Chicago

Calzada-Orihuela, Gustavo, et al. "Implementing Genetic Algorithms to Assist Oil and Gas Pipeline Integrity Assessment and Intelligent Risk Optimization," International Journal of Organizational and Collective Intelligence (IJOCI) 7, no.4: 63-82. http://doi.org/10.4018/IJOCI.2017100104

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Abstract

Oil and gas industry, worldwide, needs to monitor, control and assess the elements that are involved in the general oil transportation and production processes. However, these processes are not risk free. The project proposes an intelligent support system that provides optimized projections for effective risk management. The project focuses on the development of a set of Genetic Algorithms (GAs), a branch of AI systems that assists to optimize the usage and distribution of resources. GAs will reduce the latent risks and potential dangers as much as possible. The main purpose is to minimize the risk levels in a pipeline segment based on their condition and by detecting optimal variable configurations: their Risk of Failure (RoF), Probability of Failure (PoF), Consequence of Failure (CoF), and their sub elements (threats and impacts). The heuristic results generated by this set of GAs show a significant reduction on the risk assessment measures, by finding “optimized” configurations of these variables.

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