It is estimated that 30% of clastic hydrocarbon reservoir in the world are thinly bedded. Limited vertical resolution of logging tools, particularly deep resistivity tools, often causes ambiguity in log interpretation across interbedded rock formations. This situation routinely gives rise to inaccurate assessments of in-place hydrocarbon reserves. To gain better understanding of tool measurement behavior in interbedded sequences due to limitations of vertical resolution (resolution approximately equal to 1 foot), a new joint inversion procedure is introduced in this thesis that effectively combines borehole measurements of density and induction resistivity. The objective of the inversion is to reduce shoulder-bed effects on the interpretation of porosity and hydrocarbon saturation across inter-bedded rock formations and to reduce non-uniqueness in the estimation of porosity and hydrocarbon saturation.We undertake the assessment of inter-bedded clastic formations with two radically different procedures depending on whether the beds are thinner or thicker than 1 foot. For the case of beds thicker than 1 foot, bed boundaries are defined from the inflection point of a high vertical-resolution log. Instead of inverting for formation resistivity and density separately, a new method is introduced in this thesis to jointly estimate the porosity and water saturation of the flushed and virgin zones. For the case of beds thinner than 1 foot (thin beds), a new Bayesian statistical inversion approach is presented. This new method delivers global statistical properties of the laminated sequence, including probability density functions of net-to-gross ratio, porosity, hydrocarbon saturation, and hydrocarbon reserves. The joint inversion method has been successfully applied to synthetic and field data, thereby considerably improving the petrophysical estimates obtained with traditional log interpretation methods. We appraise the advantages of the combined inversion of density and resistivity measurements with respect to standard log interpretation procedures on both synthetic and field data sets. These tests confirm the reliability of the joint inversion procedure and its applicability to field studies.