The problem of oil displacement was solved through neural networks. The Buckley-Leverett model was chosen, which describes the process of displacing oil with water. It consists of the equation of continuity of oil and water phases and Darcy's law. The task is to optimize the problem of oil displacement. Optimization is carried out at three levels: vectorization of calculations; implementation of the algorithm using neural networks. The peculiarity of the method proposed in the work is the identification of the method with high accuracy and minimal errors, the solution with the help of neural networks. The study is also one of the first to compare neural and recurrent neural networks. As a result of the study, gradient enhancement classifiers and neural networks showed high accuracy, 99.99% and 97.4%, respectively. To achieve this goal, more than 67,000 data sets from 10th grade were created. These data are important for solving the problem of oil displacement in porous media. The proposed method provides a simple and sophisticated way to introduce oil knowledge into neural networks. This eliminates two of the most important disadvantages of neural networks: the need for large data sets and the reliability of extrapolation. The proposed principles can be summarized in countless ways in the future and should lead to the creation of a new class of algorithms for solving direct and reverse oil problems.