The structure of production at the field, the structure of data obtained, as well as the features of their interpretation and modeling, were investigated. A mathematical model was developed for predicting oil production and transportation processes under conditions of uncertainty based on the results of an analysis of production equipment. The existing data model for individual oil field facilities was analyzed. An algorithm for the operation of a neural network model was proposed to predict an important characteristic of an objectprofitability. The algorithm was improved based on the optimization block, which served to classify and identify features in existing data based on the p-criterion. The proposed algorithms are designed to make decisions when performing various types of operations in the field. The proposed model for predicting oil production and transportation processes under conditions of uncertainty showed the efficiency of profitability projection at the level of 73.5%.