A conceptual model for cost-effective seismic acquisition in complex offshore environments through data optimization and technological innovation
1 TotalEnergies Exploration and Production Nigeria Limited, Nigeria.
2 Shell Nigeria Gas (SEN/ SNG), Nigeria.
3 Shell Petroleum Development Company, Lagos, Nigeria.
4 Shell Energy Nigeria PLC.
Review Article
Magna Scientia Advanced Research and Reviews, 2021, 02(02), 100-117
Article DOI: 10.30574/msarr.2021.2.2.0052
Publication history:
Received on 14 May 2021; revised on 25 June 2021; accepted on 27 June 2021
Abstract:
Seismic acquisition in complex offshore environments poses significant challenges, including high costs, intricate subsurface structures, and operational risks. This paper presents a conceptual model for achieving cost-effective seismic acquisition by leveraging data optimization techniques and cutting-edge technological innovations. The model integrates advanced geophysical methods, machine learning algorithms, and real-time data analytics to optimize survey design, streamline data acquisition processes, and enhance imaging accuracy. Key components of the proposed model include adaptive sampling strategies, automated quality control systems, and hybrid acquisition technologies that combine ocean-bottom nodes (OBN) with towed streamers. By employing machine learning models trained on historical seismic data, the approach predicts optimal survey parameters and identifies potential data gaps, minimizing redundant data collection and operational costs. Additionally, the integration of real-time data processing ensures swift adjustments to acquisition strategies, reducing downtime and improving data quality in challenging conditions. This model emphasizes the use of eco-friendly technologies, such as autonomous seismic nodes powered by renewable energy, to align with sustainability goals while maintaining operational efficiency. The proposed framework also incorporates risk management protocols to mitigate environmental and technical risks, ensuring compliance with regulatory standards and industry best practices. A case study simulation demonstrates the model's effectiveness in reducing acquisition costs by up to 30% while achieving high-resolution subsurface imaging in a geologically complex offshore basin. The findings underscore the potential of data optimization and technological innovation to revolutionize seismic acquisition in offshore environments, enabling resource-efficient and sustainable exploration practices. This conceptual model provides a pathway for industry stakeholders to balance cost-efficiency, technological advancement, and environmental stewardship in seismic exploration. Future research directions include the integration of artificial intelligence for real-time decision-making and the development of advanced visualization tools for subsurface interpretation.
Keywords:
Seismic Acquisition; Offshore Environments; Data Optimization; Machine Learning; Cost Efficiency; Ocean-Bottom Nodes; Hybrid Technologies; Real-Time Analytics; Sustainable Exploration; Risk Management
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0