Impact Of Artificial Intelligence And Big Data On The Oil And Gas Industry In Nigeria

Ekeinde Bose Evelyn, Okujagu C Diepiriye, Dosunmu Adewale


This paper examines the concept of Artificial intelligence and Big Data as a field of study and its Impact on the oil and gas industry. Artificial Intelligence refers to the concept having of Computer systems that can perform tasks that would typically require human intelligence. Some such tasks are visual perception, speech recognition, decision-making and translation between languages, amongst others. “Big data” or Big Data analytics is a term often used to describe a huge or somewhat overwhelming data size that exceeds the capacity of both humans and the traditional software to process within an acceptable time and value. There is a big interface between the two concepts. AI does not stand alone; it requires big data for efficiency. AI and Big Data have brought about great impact across different industries and organizations. In the oil and gas industry, there have been an increasing installation of data recording sensors, hence data acquisition in exploration, drilling and production aspects of the industry. The industry is gradually making use of this huge data set by processing them using AI enabled tools and software to arrive at smart decisions that bring efficiency to operations in the industry. Some of such areas are analysis of seismic and micro-seismic data, improvement in reservoir characterization and simulation, reduction in drilling time and increasing drilling safety, optimization of pump performance, amongst others. Some of the solutions listed above have been successfully implemented in Nigeria, mostly by the international oil companies and some additional areas have also been impacted: managing asset integrity, tubular tally for drilling operations using RFID and the licensing and permit system by DPR. The industry has fully embraced the AI and Big Data concept, the future is very bright for more innovative solutions. However, there are still a few challenges especially in Nigeria. Some of these challenges include lack of local skilled manpower, poor data culture, security challenges in the industry’s operating areas, limited availability of good quality data, and understanding the complexity of the concept.


Artificial Intelligence, Big Data, Nigeria, Oil & Gas.

Full Text:



Adekoya, F (2019). The Guardian Newspaper, Leveraging Artificial Intelligence, big data in oil, gas exploitation. Retrieved from: intelligence-big-data-in-oil-gas-exploitation/arXiv: 1712.05889

Bernardone, C (2018). Artificial Intelligence and Big Data: A Perfect Match Retrieved from:

Bharadwaj, R (2019). AI for Exploration and Production (Upstream) in the Oil and Gas industry – Current Applications. Retrieved from: exploration-production-upstream-oil-gas-industry-current-applications.

Cockburn, IM; Henderson, R; Stern, S (2018). The impact of artificial intelligence on innovation. Working9 Paper No. w2444

Damilare, M (2019). Data, “The New oil”: How the Nigerian Oil and Gas Industry Can Capitalize on the Promise of the New ‘’Oil”. Retrieved from:

Dan Jerkens, Shell Energy Podcast (2020); Smart Energy: How Clever will AI Become? Retrievedfrom:

DeMauro, A; Greco, M; & Grimaldi, M (2016). A formal definition of Big Data based on its essential features. Library Review. 65. 122-135. 10.1108/LR-06-2015-0061.

Gerali, F (2016). When oil found momentum: the development of the Pennsylvanian Pattern. Research proposal presented at The Library Company of Philadelphia. Unpublished man-uscript.

Gerali, F., and Gregory, J. (2017). “Harsh oil: finding petroleum in early twentieth century Western Australia”. In Geological Society, London, Special Publications 442 (1), 425-436.

Jain, A (2016). "The 5 V's of big data". Watson Health Perspectives. Retrieved from:,traditional%20data%2Dprocessing%20application%20software.&text=Big%20data%20was%20originally%20associated,volume%2C%20variety%2C%20and%20velocity.

Joshi, P; Thapliyal, R; Chittambakkam, AA; Ghosh, R; Bhowmick, S; Khan, SN (2018). MS Big Data Analytics for Micro-seismic Monitoring, pp. 20-23.

Kannaiyan, GN; Pappula, B; and Veerubommu, R (2020). A Review on Graph Theory in Network and Artificial Intelligence. Journal of Physics: Conference Series, Volume 1831, International Conference on Robotics and Artificial Intelligence (RoAI) 2020 28-29 December 2020, Chennai, India

Kuhn, O (2004). Ancient Chinese drilling. CSEG Record 29(6):39–43

Ray, S (2018). History of AI, Towards Liu, J; Kong, X; Xia, F; & Bai, X; Wang, L; Qing, Q; & Lee, I (2018). Artificial Intelligence in the 21st Century. IEEE Access. PP. 1-1. 10.1109/ACCESS.2018.2819688.

McCarthy, J (1959). "Programs with Common Sense" at the Wayback Machine (archived October 4, 2013). In Proceedings of the Teddington Conference on the Mechanization of Thought Processes, 756–91. London: Her Majesty's Stationery Office

Mohammadpoor, M and Torabi, F (2020). Big Data analytics in oil and gas industry: An emerging trend, Petroleum, Volume 6, Issue 4, Pages 321-328,

Nordlinger, C (2019). The Marriage of AI and Data Analytics, Retrieved

from: a53fbba2c196

Ockree, K.G. Brown, J. Frantz, M. Deasy, R. (2018).Resources-Appalachia Integrating Big data analytics into development planning optimization SPE/AAPG East. Reg. Meet, Society of Petroleum Engineers, Pittsburgh

Popa, A; and Sean C (2019). Optimizing Horizontal Well Placement through Stratigraphic Performance Prediction Using Artificial Intelligence. Paper presented at the SPE Annual Technical Conference and Exhibition, Calgary, Alberta, Canada. doi:

Roberts, J (2016). Thinking Machines: The Search for Artificial Intelligence. Distillations. 2 (2): 14–23. Archived from the original on August 19, 2018. Retrieved March 20, 2018.

Seemann, D; Spe, MWS; Hasan, S. (2013), Improving Reservoir Management through Big Data Technologies. Aramco SPE 167482 pp. 28-30

Shi, F; Ning, H; Huangfu, W (2020). Recent progress on the convergence of the Internet of Things and artificial intelligence. IEEE Netw; 34(5): 8–15.

Sumbal, MS; Tsui, E; and See-to, EWK (2017). "Interrelationship between big data and knowledge management: an exploratory study in the oil and gas sector", Journal of Knowledge Management, Vol. 21 No. 1, pp. 180-196.

Turing, M. (1950). Computing Machinery and Intelligence, Mind, Volume LIX, Issue 236, October 1950, Pages 433–460.

Woo, E (2011). "John McCarthy dies at 84; the father of artificial intelligence". Los Angeles Times.

Wood, L (2020), Artificial Intelligence in the oil and gas industry, 2020 – 2025 – Upstream Operations to Witness Significant Growth –, retrievedfrom,corrosion%20or%20increased%20equipment%20usage.

Wu, W; Lu, X; Cox, B; Li, G; Lin, L; Yang, Q (2014). Retrieving Information and Discovering Knowledge from Unstructured Data Using Big Data Mining Technique : Heavy Oil Fields Example

Yergin, D. (2020). The new map: energy, climate, and the clash of nations. New York: Penguin Press.



  • There are currently no refbacks.

Copyright (c) 2022 Ekeinde Bose Evelyn

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.