Principles and Applications of Well Logging in Hydrocarbon Exploration and Development: A Review

Abstract

Hydrocarbon exploration and development have long relied on the indispensable technique of well logging for subsurface formation evaluation. As a cornerstone of petroleum geoscience, well logging provides continuous, in-situ records of the physical, chemical, and mechanical properties of rocks and fluids encountered during the drilling of oil and gas wells. These measurements enable geoscientists and petroleum engineers to characterize subsurface formations, estimate hydrocarbon potential, and make informed decisions throughout the life cycle of a reservoir—from exploration and appraisal to development and production. This review paper offers a comprehensive analysis of the principles, methods, and practical applications of well logging within the context of the oil and gas industry. The paper explores the foundational physical principles that underpin various logging techniques and highlights their evolving role in deciphering subsurface geology and reservoir architecture. Key logging tools—including resistivity, gamma-ray, neutron, density, and sonic logs—are examined in detail, along with advanced methods such as nuclear magnetic resonance , borehole imaging, and logging while drilling technologies. These tools are critically assessed for their capacity to identify lithology, estimate porosity and permeability, detect hydrocarbons, and delineate reservoir boundaries. In addition to addressing traditional logging workflows, the review delves into the interpretation and integration of petrophysical data, underscoring the importance of precision, calibration, and cross-validation in well log analysis. The study also examines recent innovations and emerging technologies that have enhanced the accuracy, resolution, and real-time application of logging systems, particularly in unconventional and complex reservoir environments. These include advancements in machine learning, high-resolution imaging, and downhole sensor networks. Challenges and limitations related to bor

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