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Article Type

Review Article

Corresponding Author

Nidal Yousef Ramadan

Abstract

The continuous global demand for energy resources necessitates innovative methods to locate subsurface hydrocarbon accumulations efficiently. Traditional exploration techniques often face limitations in cost, accuracy, and environmental impact, driving the need for advanced alternatives. Microbial Prospecting for Oil and Gas (MPOG) addresses this challenge by leveraging the symbiotic relationship between hydrocarbon seepage and specialized microbial populations. This indirect geochemical exploration method is grounded in a hypothesis that light hydrocarbons migrate vertically from reservoirs to the surface, where they are metabolized by microorganisms, leading to detectable biological anomalies over the oil and gas deposits. The evolution of MPOG began in the 1930s with early methods detecting hydrocarbons in soil and air samples. Subsequent decades witnessed refinements in measuring bacterial activity through radioactive isotopes, serological assays, and genetic tools. Recent advancements focus on molecular techniques, such as DNA sequencing and gene-specific analyses, enabling rapid identification of microbial biomarkers linked to the hydrocarbon metabolism.

In the past decade, the integration of artificial intelligence (AI) and machine learning (ML) with metagenomic data has revolutionized MPOG, transforming it into a predictive, data-driven discipline. These technologies enable the analysis of complex microbial patterns and enhance the accuracy of hydrocarbon detection. Future prospects include the adoption of multi-omics approaches (proteomics, metabolomics) and real-time monitoring systems, positioning MPOG as a cornerstone of next-generation exploration strategies.

In conclusion, MPOG has emerged as a vital tool in petroleum exploration, offering a cost-effective, rapid, and accurate approach with field-proven accuracy rates exceeding 90%. The integration of microbiological, geochemical, and genetic methodologies has significantly enhanced seepage detection sensitivity by 3–5× compared to conventional geochemical surveys, positioning MPOG as a critical component of modern exploration strategies.

Keywords

Hydrocarbon seepage, Microbial hydrocarbon metabolism, Surface geochemical indicators, Biomarkers for oil and gas, Metagenomics, Machine learning, Functional gene analysis.

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