Reliable assessment of in situ storage and flow properties of rocks from borehole geophysical measurements and core data is crucial for appraising subsurface fluid resources. Well-log analysis in the presence of thin beds, mud-filtrate invasion, and/or electrical anisotropy remains a challenge in formation evaluation. Conventional interpretation methods rarely consider shoulder-bed effects on well logs, spatial distributions of fluid saturation around the borehole due to invasion, and differences in the volume of investigation of the different borehole instruments involved in the interpretation. This dissertation develops new quantitative methods for the interpretation of borehole geophysical measurements and core data acquired in spatially complex rocks. First, analytical and Bayesian methods are developed to assess horizontal and vertical resistivities from logging-while-drilling resistivity measurements in the presence of electrical anisotropy, noise, and well deviation effects. Borehole measurements (eg, resistivity, density) are deconvolved into layer-by-layer physical properties with their associated uncertainty. Additionally, a new method is developed to calibrate and verify the reliability of core data and borehole measurements acquired under adverse geometrical conditions in formations with complex solid composition and thin beds. Numerical simulation of well logs based on high-resolution core data combined with rock typing and multi-well measurement analysis enable the detection of inconsistent, noisy, and inaccurate measurements, including cases of abnormal borehole environmental conditions causing biases in petrophysical interpretations. Finally, a new method is developed to quantify water saturation, residual hydrocarbon saturation, and permeability in the presence of deep mud-filtrate invasion, ie, when the radial length of invasion is greater than the depth of investigation of borehole instruments. This method combines the numerical simulation of well logs with the physics of mud-filtrate invasion to quantify the effect of petrophysical properties and drilling conditions on nuclear and resistivity logs. Based on core-calibrated petrophysical models, thousands of invasion conditions were numerically simulated for a wide range of petrophysical properties and drilling conditions, including time of invasion and overbalance pressure. Then, analytical and machine-learning (ML) models were combined to infer unknown rock properties. Synthetic examples verify the accuracy and reliability of the introduced interpretation methods and quantify the uncertainty of estimated rock properties due to noisy measurements. Successful field applications are also documented for (a) the estimation of water saturation in an electrically anisotropic sandstone formation offshore Australia penetrated by high-angle and horizontal wells,(b) assessment of the quality of well logs acquired in a shaly-sandstone formation in the North Sea, and (c) estimation of water saturation, residual hydrocarbon saturation, and permeability in a tight-gas sandstone formation invaded with water-base mud in the Middle East. Comparison of interpretation results against those obtained using conventional petrophysical methods confirm the effectiveness of the new quantification techniques introduced in this dissertation for the quantification of petrophysical properties across a variety of rock formations.