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Exploring the Role of Big Data in Petroleum Exploration: Using Advanced Analytics for More Efficient Decision-Making in Exploration Projects.
Subject area: Science,Engineering and Technology · Area of research: Big Data Analytics
Abstract
The integration of big data analytics into petroleum exploration is reshaping how decisions are made in the upstream oil and gas industry. By leveraging large-scale, high-dimensional datasets?including seismic records, well logs, production data, and environmental factors?exploration teams can enhance their ability to identify hydrocarbon-rich zones with greater precision and efficiency. Advanced analytical tools such as machine learning algorithms, predictive modeling, and real-time data streaming allow geoscientists and engineers to uncover hidden geological patterns, forecast reservoir performance, and mitigate exploration risks. Furthermore, the fusion of structured and unstructured data from multiple sources improves situational awareness and supports more informed decision-making across exploration workflows. As companies strive to reduce costs and increase operational efficiency, the strategic application of big data enables dynamic modeling of reservoirs, optimization of drilling locations, and integration of historical data into future exploration strategies. This review explores current methodologies, key innovations, industry applications, and future directions of big data in petroleum exploration, emphasizing its transformative role in driving smarter and faster exploration decisions in a highly competitive energy sector.
Keywords
Big Data Analytics; Petroleum Exploration; Machine Learning; Predictive Modeling; Exploration Decision-Making; Seismic Data; Data Integration; Oil and Gas Industry; Real-Time Analytics; Reservoir Characterization
How to cite this paper
@article{1709675,
author = {Nyaknno Umoren, Malvern Iheanyichukwu Odum, Iduate Digitemie Jason, Dazok Donald Jambol},
title = {Exploring the Role of Big Data in Petroleum Exploration: Using Advanced Analytics for More Efficient Decision-Making in Exploration Projects.},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {4},
number = {1},
pages = {270-279},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709675.pdf},
abstract = {The integration of big data analytics into petroleum exploration is reshaping how decisions are made in the upstream oil and gas industry. By leveraging large-scale, high-dimensional datasets?including seismic records, well logs, production data, and environmental factors?exploration teams can enhance their ability to identify hydrocarbon-rich zones with greater precision and efficiency. Advanced analytical tools such as machine learning algorithms, predictive modeling, and real-time data streaming allow geoscientists and engineers to uncover hidden geological patterns, forecast reservoir performance, and mitigate exploration risks. Furthermore, the fusion of structured and unstructured data from multiple sources improves situational awareness and supports more informed decision-making across exploration workflows. As companies strive to reduce costs and increase operational efficiency, the strategic application of big data enables dynamic modeling of reservoirs, optimization of drilling locations, and integration of historical data into future exploration strategies. This review explores current methodologies, key innovations, industry applications, and future directions of big data in petroleum exploration, emphasizing its transformative role in driving smarter and faster exploration decisions in a highly competitive energy sector.},
keywords = {Big Data Analytics; Petroleum Exploration; Machine Learning; Predictive Modeling; Exploration Decision-Making; Seismic Data; Data Integration; Oil and Gas Industry; Real-Time Analytics; Reservoir Characterization},
month = {July},
}