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.