Reservoir rock porosity is usually determined by core analysis, but the cost of this method is huge, in order to establish a more accurate and stable reservoir porosity prediction model. Using the logging data of the existing work area, we optimize the neural network using the improved sine-cosine algorithm, add nonlinear weights to the position change of the sine-cosine algorithm (SCA) to correct the individual position and improve the convergence accuracy of the algorithm, incorporate the Levy flight to improve the SCA algorithm to strengthen the local search ability, and optimize the parameters of the BP neural network using the improved sine-cosine algorithm to construct the IISCA-BP reservoir pore size prediction model. Porosity prediction model. The results of the IISCA-BP model are compared with the evaluation results of the BP model, and the model is applied to test the X oilfield in Changling Depression. The results show that the absolute relative error of the IISCA-BP model is 1.996%, and the average absolute relative error is only 0.324%, which is more accurate and more stable than the BP model. The IISCA-BP model is well applied in the field, and the results of the scheme have higher consistency with the core data, so that the scheme of this paper has practicability, validity, and generalizability.
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