SN Applied Sciences (Dec 2023)
A review on the advancements and challenges of artificial intelligence based models for predictive maintenance of water injection pumps in the oil and gas industry
Abstract
Abstract This paper provides a comprehensive review on the Artificial Intelligence (AI) based models for predictive maintenance (PdM) of water injection pumps (WIPs) in the oil and gas industry (OGI). The review encompasses the selection of algorithms, data requirements, and optimization strategies, offering insights into advancements, challenges, and theoretical foundations including data pre-processing and feature selection. This review highlights AI-based PdM developments for WIPs, focusing on techniques and algorithms that enhance water injection pump performance and accurately predict maintenance needs. It emphasizes the effectiveness of algorithms in capturing pump data patterns and anomalies for proactive maintenance. Additionally, the review offers valuable insights for future research directions and practical implementations of AI in pump maintenance. This comprehensive assessment serves as a beacon for OGI experts in the selection of AI methods for pump maintenance, enabling them to refine their procedures, enhance efficiency, and reduce operational interruptions. A prominent highlight is the importance of data quality and interpretability, which play a pivotal role in facilitating well-informed decision-making during the integration of AI technologies into maintenance processes. This article focuses on the theoretical foundations of AI in the context of pump maintenance, providing a contribution to OGI industry. By integrating theoretical perspectives with real-world evidence, it offers insights for guiding future research and enhancing maintenance techniques. As a resource, it holds relevance for researchers, practitioners, and decision-makers within the OGI sector, contributing to the ongoing advancement of this field. Article Highlights Artificial Intelligence (AI) optimizes Oil & Gas pump maintenance for efficiency and reliability. Real-world case studies validate cost and time reductions, improving pump performance. Challenges in data management and ethical AI implementation require careful consideration.
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