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Quantitative interpretation in petrophysics is an essential component in the exploration and development of hydrocarbon reservoirs. This paper delves into the theoretical foundations of petrophysical properties, such as porosity, permeability, and fluid saturation, and examines the mathematical models employed for data interpretation. It highlights the significance of integrating various types of petrophysical data, including core samples, well logs, and seismic surveys, to develop a comprehensive understanding of subsurface formations. Emphasis is placed on ensuring data quality and managing uncertainties through rigorous calibration and validation processes. The paper further explores the advanced techniques used in log and seismic interpretation, showcasing how these methods contribute to accurate reservoir characterization. The integration of machine learning and artificial intelligence in petrophysical analysis is discussed, underscoring their role in enhancing data interpretation and reducing uncertainties. The applications and implications of quantitative interpretation are examined, particularly its impact on hydrocarbon exploration, which aids in identifying potential reservoirs, and reservoir characterization, which provides detailed insights into reservoir properties and performance. Overall, the paper illustrates how quantitative interpretation in petrophysics improves the accuracy and reliability of subsurface evaluations and optimizes hydrocarbon recovery and management. By leveraging advanced computational techniques and integrating diverse data sources, quantitative interpretation drives innovation and efficiency in the hydrocarbon industry, ensuring sustainable and effective resource utilization.
Iriogbe et al. (Sat,) studied this question.