The paper has analyzed the issues of pricing policy of the oil and gas industry and the properties of the market for oil products. An improved model for forecasting the price of Urals crude oil on the Russian market has been worked out. The model is based on the statistical approach and involves trend determination, correlation and regression analysis. The existing dependencies between the following pairs of variables were analyzed: Urals price and demand for crude oil; Urals price and Brent price; Urals price and crude oil supply; Urals price and oil production capacity of an oil refinery; Urals price and saturation of the crude oil market. A trend line has been built and the crude oil prices have been forecasted. The correlation coefficient has been determined within the regression statistics as well as the Fisher, Durbin-Watson, Breusch-Godfrey and Student criteria. The coefficients of the equation have been calculated. Some recommendations for improving the pricing and forecasting methods in the oil and gas company with an example of JSC Lukoil have been made. The results for building the model for forecasting oil prices are positive. The pricing of the two grades of oil has a linear relationship, and therefore, this factor will improve the predictive ability of our model. Calculating the coefficients of the regression equation yielded the target price for Urals oil in 2016 in the amount of $42.05 per barrel (and $31 per barrel if calculated including the speculative factor). The necessity of consideration of taking into account the prices on the equivalent commodities, namely the price of Brent crude oil, has been emphasized as a basic recommendation on the pricing policy of JSC Lukoil.