Reducing drilling risks through enhanced reservoir characterization for safer oil and gas operations
1 Nigeria LNG Limited, Nigeria.
2 Independent Researcher; Nigeria.
3 Independent Researcher, Houston Texas, USA.
Review Article
GSC Advanced Research and Reviews, 2024, 19(03), 086–101.
Article DOI: 10.30574/gscarr.2024.19.3.0205
Publication history:
Received on 01 May 2024; revised on 08 June 2024; accepted on 10 June 2024
Abstract:
Reducing drilling risks is paramount to ensuring safer oil and gas operations, and enhanced reservoir characterization plays a critical role in this endeavor. This review delves into the multifaceted approach of utilizing advanced technologies and methodologies for better understanding subsurface conditions, thereby mitigating drilling hazards Reservoir characterization encompasses the comprehensive analysis and interpretation of geological, geophysical, and petrophysical data to create an accurate model of the subsurface environment. By employing high-resolution seismic imaging, well logging, core sampling, and advanced computational modeling, geoscientists and engineers can delineate the structural, stratigraphic, and lithological features of the reservoir. This detailed insight into the subsurface heterogeneities, such as fault systems, fracture networks, and varying rock properties, is crucial for predicting and managing drilling risks. One of the primary risks in drilling operations is the unanticipated encounter with high-pressure zones, which can lead to blowouts and well control incidents. Enhanced reservoir characterization aids in identifying these high-pressure zones and in planning appropriate drilling mud weights and casing programs to counteract such risks. Additionally, understanding the spatial distribution of reservoir properties allows for the optimization of well trajectories, reducing the likelihood of penetrating problematic formations or encountering unexpected fluid contacts. Advanced seismic techniques, including 3D and 4D seismic surveys, have significantly improved the resolution and accuracy of subsurface imaging. These techniques help in identifying subsurface anomalies and discontinuities that could pose risks during drilling. Furthermore, integration of seismic data with real-time drilling data through techniques such as seismic-while-drilling (SWD) provides dynamic updates to the subsurface model, enabling proactive risk management. The use of machine learning and artificial intelligence (AI) in reservoir characterization has also emerged as a powerful tool. By analyzing large datasets from various wells and fields, AI algorithms can predict potential drilling hazards and recommend mitigation strategies. This predictive capability is particularly valuable in complex geological settings where traditional methods may fall short. Moreover, geomechanical modeling, which involves the study of rock mechanical properties and in-situ stresses, is essential for understanding wellbore stability. Enhanced reservoir characterization allows for accurate geomechanical models that predict the response of the subsurface to drilling activities, helping to prevent wellbore collapse, stuck pipe incidents, and other mechanical failures. Collaboration between geoscientists, drilling engineers, and data scientists is vital to maximizing the benefits of enhanced reservoir characterization. By integrating multidisciplinary expertise, the oil and gas industry can develop more robust drilling plans and contingency strategies, ultimately leading to safer and more efficient operations. Enhanced reservoir characterization is a cornerstone of reducing drilling risks in oil and gas operations. Through the integration of advanced seismic imaging, real-time data analytics, machine learning, and geomechanical modeling, the industry can achieve a deeper understanding of subsurface conditions. This comprehensive approach not only mitigates drilling hazards but also enhances operational efficiency and safety, ensuring the sustainable development of hydrocarbon resources.
Keywords:
Drilling risks; Reservoir characterization; Oil and gas; Operations
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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
