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.