Advanced analysis of cuttings (ACA) provides qualitative measurements of the gamma ray spectrum and allows for quantifying of mineral elements for each sample in mud logging. The advancement in technology permits the ACA measurements to be conducted in near real time, which enables integration with the conventional log measurements for the purpose of constructing robust, multi-mineral petrophysical model. The result of the data integration prevails in the petrophysical mineral model. The ability to acquire samples at a higher sampling rate, proper sample treatment and oxides correction, had improved the quality of data acquired from ACA when compared to the initial trials. ACA sample resolution had been improved immensely when compared to the first trial, because it reached the level where it can resolve the layering of the reservoir in the targeted formations. LWD measurements are essential in real-time petrophysical evaluation but slim neutron spectroscopy (NS) tool is not available, and only some contractors have slim spectral gamma ray (SGR). ACA was used to complement the previously mentioned deficiency in LWD technology. A reliable carbonate multi-min petrophysical model was successfully built and implemented in near real time evaluation using ACA and LWD data integration. In addition, the ACA has the advantage of acquiring the SGR and NS in high temperature (>320°F) slim (<4 inches) hole. As a result, the combination of ACA and wireline logs were used to evaluate a deep reservoir. The ACA is more cost effective when compared to similar measurements from conventional logs. This paper discusses the ACA and its integration with conventional log data for petrophysical interpretation of the carbonates and clastics formations, respectively. The availability of SGR and NS are very important in complex lithology evaluation. This case study shows that advanced mudlogging data integrated with standard logging measurements, assisted in building enhanced petrophysical model, where the SGR and NS logging measurements were absent.