In geothermic studies, the thermal conductivity (TC) is an essential parameter needed to calculate heat flow in rocks. It is obtained generally with high accuracy from laboratory measurements. The methodology proposed in this paper is to estimate the TC, which depends on many parameters; such as mineralogy, porosity, shape of voids and nature of contact between grains, based on linear and nonlinear relationships. In order to predict the thermal conductivity from other petrophysical parameters in the Hamra Quartzites reservoir, we have measured porosity, density and permeability, for dry samples taken from core wells. The correlation coefficients (R) were calculated between thermal conductivity and other petrophysical parameters for all samples. The results show that, the correlation coefficient is moderate between TC and the porosity, weak between TC, density and permeability. To improve these correlations, samples were classified into cemented and uncemented sets. A minor improvement on the correlation coefficients is noted between TC, density and porosity in uncemented samples, with values equal 0.51 and 0.73, respectively. The application of Radial Basis Function (RBF) neural networks, using density, porosity and permeability as inputs and thermal conductivity as output, permit us to predict the thermal conductivity with high precision. The correlation coefficient between TC estimated by the RBF neural network is the same as that measured in laboratory equaling 0.983.