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| Format: | Recurso digital |
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Zenodo
2018
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| Online Access: | https://doi.org/10.5281/zenodo.14908528 |
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Table of Contents:
- <p>The oil and gas industry faces significant challenges in ensuring operational reliability and efficiency. Equipment failures and process inefficiencies can lead to financial losses, safety risks, and environmental concerns. This paper proposes a novel framework that integrates artificial intelligence (AI) with Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems to address these issues. The framework combines predictive maintenance, which anticipates equipment failures to reduce downtime, with process automation, which optimizes operational parameters in real time. By leveraging AI-driven models for failure prediction and automated process control, the proposed approach aims to enhance equipment reliability, streamline processes, and reduce operational costs. Potential evaluation metrics, such as prediction accuracy, lead time for failure detection, and process efficiency improvement, are discussed to outline the framework's theoretical effectiveness. This paper offers a conceptual framework tailored to the oil and gas industry, highlighting its potential to enable smarter, safer, and more cost-effective operations.</p>