Abstract: Partial least squares regression analysis establishes a regression model by extracting the components containing the original data variation information from the independent variable and dependent variable data table,which can solve the problem of multiple collinearity due to the high correlation between the independent variables in the regression modeling process. With oil field operation cost as the research object, and operating cost as the dependent variable, we analyze the partial data of the DX Oilfield's variables through partial least squares regression analysis by using SIMCA-P software. The regression prediction model is established and tested. The results show that the independent variable index has an explanatory power of 0.99902, and the model has very high reliability. This research shows that partial least squares regression method is applicable to the prediction of oilfield operation cost, and can be used for reference in other research objects.

Key words: partial least squares regression, regression model, regression prediction model, oilfield operating cost, SIMCA-P software