Study on the Application of Machine Learning Technology in the Oilfield Investment Decision Making Process and the Long-term Impact on Economic Benefits

Oilfield investment planning is a large and complex systematic project which is an important part of oilfield development and production process management. This paper establishes a mathematical model for oilfield investment decision optimization based on the traditional Markowitz MV model and the factors of yield and economic cost that need to be considered in the oilfield investment decision process. The mathematical model is combined with the FT-QPNN model, and a finite time q -power neural network is introduced into the model to obtain the optimization model of oilfield investment decision-making based on improved machine learning technology. The long-term economic benefits in the oilfield investment decision-making process are analyzed using the economic benefit evaluation model. After using the model to optimize the investment decision of the LJ oilfield development project, the oil production forecast in 2027 is elevated to 25.09 MMbbl while reducing the operating cost of the oilfield project by 2,377,300 Yuan. In addition, after the optimization of the model in this paper, the average payback period of this oilfield project is reduced to 2.70 years, and the average financial internal rate of return of the project unit can be increased to 35.88%. This paper provides a decision-making basis for the realization of oilfield benefit maximization, which is a deepening of oilfield refinement management and is of general guiding significance for improving the level of oilfield development decision-making.