Prediction of minimum miscibility pressure for CO2 flooding based on the IMA-AmMLP model
The minimum miscibility pressure(MMP)is a critical parameter that determines whether a reservoir can be explored by miscible flooding.To accurately predict the MMP,the multi-layer perceptron(MLP)prediction model was optimized using an im-proved mayfly algorithm(IMA).The attention mechanism was used to extract the factors affecting MMP;the optimization capabili-ty of IMA was enhanced by incorporating chaotic Sobol sequences,nonlinear inertia weights,and reverse learning methods.These improvements can provide optimal weights and thresholds for the MLP,leading to the establishment of the IMA-AmMLP model for MMP prediction.The model was validated by the case study of a block in Jilin oilfield.The results demonstrate that the IMA-AmMLP model exhibit a higher degree of fitting between the predicted and actual values,with a mean absolute error(MAE)of 1.036 MPa,a mean absolute percentage error(MAPE)of 0.024,and a root mean square error(RMSE)of 0.835,and the values were all superior to those of the original model.This indicates that the IMA-AmMLP model can more accurately predict MMP,providing a valuable reference for the exploitation and management of reservoirs using CO2 flooding in fields.
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