Robotics and artificial intelligence in unconventional reservoirs: Enhancing efficiency and reducing environmental impact.
1 Department of Electrical and Electronic Engineering, Petroleum Training Institute, Effurun Delta State, Nigeria.
2 Department of Mechanical Engineering, Univesity of Benin, Edo State, Nigeria.
3 Department of Geography, Obafemi Awolowo University, Nigeria.
4 Department of Electrical and Electronics Engineering, Nile University of Nigeria, Abuja, Nigeria.
5 Department of Agricultural and Environmental Engineering, Obafemi Awolowo University, Nigeria.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(01), 2060–2068
Publication history:
Received on 09 September 2024; revised on 15 October 2024; accepted on 18 October 2024.
Abstract:
Shale, tight gas, and coal bed methane reservoirs are unconventional reservoirs that have geometrically complicated geological formations with low permeability, rendering their extraction operations difficult, expensive, and environmentally sensitive. On the other hand, recent developments in Robotics and Artificial Intelligence continue to transform how exploration, production, and maintenance activities are carried out in these reservoirs. This review paper encompasses how Robotics and AI technologies are applied to these unconventional reservoirs, improving operation efficiency, increasing the recovery rate and minimizing environmental damage. It updates on the latest status of autonomous drilling, AI-driven reservoir characterization and robotic fracturing. The paper also discusses future opportunities: emerging technologies, integration of AI with predictive analytics, and innovations toward sustainability. These are the challenges that come with possible solutions discussed in the paper: high costs, technical integration, and data quality. The review thereby presents the thought that robotics and AI will take a transformative role in the unconventional reservoir sector during the next few years, both in economic performance and environmental sustainability.
Keywords:
Robotics; Artificial Intelligence; Unconventional Reservoirs; Efficiency; Sustainability.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0