An important part of the geophysical analysis is seismic inversion, which provides insight into the Earth's structure. Conventional seismic inversion methods require enormous computational power, human intervention, long processing time, and uncertainties in predictions. With exponential growth in the Artificial Intelligence field, particularly in the Deep Learning field, there is a possibility to apply various Deep Learning and Machine Learning algorithms on multiple seismic inversion problems. Here Convolution Neural Networks were used to create a model to directly estimate porosities from seismic data. By teaching the model exact physics, the need for large labeled data was removed. The purpose of the thesis is to give a reader all the information to create the model without strong prior knowledge of exploration geophysics and deep learning algorithms.