Production prediction is the most important and comprehensive index to measure the effect of oilfield development, and it is also one of the most fundamental problems in oilfield dynamic analysis. However, the recovery prediction is often affected by many factors. Usually, the recovery is predicted by core experiments, numerical simulations, and mathematical models. The main problem is accurately predicting reservoir recovery based on existing data. This paper proposes a comprehensive prediction model for the problem of recovery. First, the correlation coefficients between 14 factors and recovery were calculated based on Pearson, Spearman, gray correlation, variance selection, univariate selection method, and tree model. Second, the weights of the factors were determined using entropy weighting, CRITIC, and hierarchical analysis to clarify the degree of contribution of different factors to the recovery. Finally, a comprehensive evaluation model was established based on the results of the weighting analysis. The results indicate that the correlation coefficient and weight of porosity, permeability, oil saturation, well spacing, cluster spacing, total fluid volume, and horizontal section length are the most relevant to the recovery. The error between the comprehensive evaluation model and the actual results is less than 3%. Therefore, the method can predict the production capacity of the tight reservoirs. The research results of this paper are of guiding significance for improving the recovery of tight reservoirs.
© 2024 The Authors. Published by American Chemical Society.