Enhancing operational efficiency in Nigerian oil exploration: The impact of real-time monitoring technologies on non-productive time

Alexander Aneke; Eteyen Oboho

doi:10.14254/jsdtl.2024.9-2.4

Alexander Aneke ⁽¹⁾ , Eteyen Oboho ⁽²⁾

(1) Enugu State University of Science and Technology , Nigeria

(2) Universidad Catolica San Antonio , Nigeria

https://doi.org/10.14254/jsdtl.2024.9-2.4

Issue
Vol. 9 No. 2 (2024)

Keywords:

Artificial Intelligence, Internet of Things, Real-Time Monitoring, Non-Productive Time, Oil Exploration

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Abstract

Purpose: This study investigates the advancements in real-time monitoring technologies aimed at reducing Non-Productive Time (NPT) in oil exploration in Nigeria, employing systematic content analysis as the research design. Methodology: The study uses systematic content analysis to evaluate recent literature on emerging digital technologies, including Artificial Intelligence (AI) and the Internet of Things (IoT), and their impact on operational efficiency in oil exploration. Results: The findings indicate that AI and IoT in real-time monitoring can enhance predictive maintenance, optimize drilling parameters, and facilitate immediate responses to operational anomalies, reducing NPT by up to 30%. Theoretical Contribution: This study contributes to the discourse on technological innovations in the oil and gas industry, providing actionable insights for stakeholders aiming to enhance operational efficiency in Nigeria's exploration activities. Practical Implications: The study highlights the necessity for investment in digital infrastructure and training, advocating for a strategic approach to modernize Nigeria's oil exploration practices in alignment with global best practices.

Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action; SDG 8: Decent Work and Economic Growth; SDG 7: Affordable and Clean Energy

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References

Ali, M., Patel, R., & Khan, S. (2023). Technological advancements in real-time monitoring for oil and gas operations. Journal of Petroleum Engineering and Technology, 18(1), 45–61.
Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. Nursing Plus Open, 2, 8–14. https://doi.org/10.1016/j.npls.2016.01.001
Dekker, M. & Thakkar, A. (2018) Digitalization–the next frontier for the offshore industry, in: Offshore Technology Conference. OnePetro, 2018, April https://doi.org/10.4043/28815-MS
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
Ghobakhloo, M. (2020) Industry 4.0, digitization, and opportunities for sustainability, J. Clean. Prod. 252. 119869. https://doi.org/10.1016/j.jclepro.2019.119869
Jain, S. (2022) Network connectivity: the new era for oil & gas [online] automation.com. Available at: https://www.automation.com/en-us/articles/september-2020/network-connectivity-the-new-era-for-oil-gas. (Accessed 30 May 2022)
John, A., & Igimoh, J. (2017) The design of wireless sensor network for real time remote monitoring of oil & gas flow rate metering infrastructure, Int. J. Sci. Res. 6 (2) 425–429.
Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). SAGE. https://doi.org/10.4135/9781071878781
McMahon, R., Patel, S., & Turner, L. (2023). Managing delays and downtime in offshore oil exploration: New strategies for productivity. Journal of Petroleum Technology, 75(2), 98–112.
Mousa, A., Shah, M., & Turner, L. (2022). Reducing non-productive time in offshore drilling: A review of real-time monitoring systems. Journal of Energy Technology, 25(3), 98–112.
Neuendorf, K. A. (2017). The content analysis guidebook (2nd ed.). SAGE. https://doi.org/10.4135/9781071802878
Rayavarapu, N. (2022). The role of frontier connectivity technologies in the oil and gas industry. Energy and Communication Technologies Journal, 8(1), 62–75.
Santos, L., Oliveira, F., & Pereira, A. (2021). The role of AI and IoT in enhancing operational efficiency in offshore oil exploration. Journal of Offshore Engineering and Technology, 19(1), 101–118.
Santoso, T., Ridwan, H., & Widjaja, T. (2022). Cost analysis of non-productive time in oil and gas exploration projects. International Journal of Energy Studies, 14(1), 45–62.
Shah, M., Patel, R., & Soni, K. (2022). Real-time monitoring technologies in oil and gas: Enabling better operational outcomes. Journal of Petroleum Engineering and Technology, 15(1), 45–58.
Shah, V., Shah, J., Dudhat, K., Mehta, P., & Shah, M. (2022) Big data analytics in O&G industry, in: Emerging Technologies for Sustainable and Smart Energy, CRC Press, Boca Raton and London, , pp. 37–55. https://doi.org/10.1201/b23013-3
Zhang, Y., Wang, L., & Hu, J. (2021). AI-driven optimization in oil and gas drilling: Improving performance through machine learning. Journal of Petroleum Science and Technology, 16(2), 78–92.

Authors

Alexander Aneke

Enugu State University of Science and Technology

nazaaneke@gmail.com (Primary Contact)

Eteyen Oboho

Universidad Catolica San Antonio

Author Biography

Eteyen Oboho, Universidad Catolica San Antonio

Mr. Eteyen Oboho is a MBA postgraduate from the aforementioned university

Aneke, A., & Oboho, E. (2024). Enhancing operational efficiency in Nigerian oil exploration: The impact of real-time monitoring technologies on non-productive time. Journal of Sustainable Development of Transport and Logistics, 9(2), 43–52. https://doi.org/10.14254/jsdtl.2024.9-2.4

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