Forecasting Surface Facilities Investment Based on Factor Analysis and Multiple Regression Analysis

Surface facilities investment is a critical component of engineering investment estimation, which holds a relatively significant proportion of the total investment. Reliable engineering investment estimates are essential in supporting decision-making for overseas oil and gas projects. However, commonly used engineering investment estimation methods suffer from relatively large errors and have limited applicability in cost prediction based on market conditions. To address this issue, this study proposes a surface engineering investment estimation prediction model by combining factor analysis with multivariate regression analysis. Specifically, the model is constructed using data from a certain oil field in the South America, where the following factors are selected for factor analysis: international steel price index, equipment price index, bulk material price index, oil price, gas price, and US dollar index. The resulting factors are then used as independent variables, while the investment amount for each well’s surface facilities is used as the dependent variable, to obtain the regression function. The proposed model is validated, and the results demonstrate that its use improves the accuracy of engineering investment estimation.