Abstarct: Total Organic Carbon (TOC) is one of the most important parameters in shale gas reservoirs. There are several approaches to estimate TOC from Petrophysical data. Two important approaches including neural network and ∆logR are implemented in this dissertation. Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms are used for training neural network. Generally, these algorithms belong to swarm intelligence. Another key parameter for shale gas reservoirs is Gas In Place (GIP). GIP in shale gas reservoirs consists of free gas and adsorbed gas. Laboratory measurements are necessary to determine amount of adsorbed gas. In this thesis, volumetric method is used to estimate free gas in Kockatea shale formation in Perth basin among several ways such as decline Curve Analysis (DCA) and type curve matching. Multi-Mineral approach is used to estimate free gas. Cut-off values are used to determine net thickness of reservoir for free gas in place calculation. Total GIP is summation of calculated free and adsorbed gas.