The investment estimation of a systematic drilling engineering project is an important step for oilfield enterprises to strengthen investment control and enhance operation management. The quality of investment estimation does not only decide the feasibility and profitability of the development plan，but also has an important instruction influence on the implementation and operation performance of the approved engineering plan. This paper proposed a method for extracting engineering parameters of drilling investment estimation based on the natural language processing algorithm and the Monte Carlo simulation investment prediction model. These two techniques were introduced into the petroleum engineering estimation，and it was demonstrated via modelling and case studies that all selected control factors were significant and thus effective. Based on the above，the investment estimation was carried out. Natural language processing algorithms were required for parameter extraction and processing，with an accuracy of over 90%. Meanwhile，the Monte Carlo simulation investment prediction model was used for calculation to ensure that the error between the extreme investment and the existing economic evaluation results was less than 5%. This developed method has been successfully applied to 29 production capacity building projects in 2024，identifying and warning 8 projects with excessive investment. It improves the accuracy and efficiency of engineering parameter extraction，enhances the percent of pass for the internal rate of return of petroleum drilling engineering investment estimation，and is of great help in improving the digitalization level of the petroleum engineering estimation.