Abstract: Accurate prediction of porosity and permeability of carbonate reservoirs is of great significance to the evaluation of carbonate reservoirs. Fractures and dissolved pores are widely developed in carbonate reservoirs, and the prediction of reservoir porosity and permeability from logging curves based on the empirical formulas has large errors. Taking a carbonate reservoir in the Middle East as the research object, we selected 914 core wells to determine porosity and permeability. By using random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), and long short-term memory  (LSTM) machine learning methods and logging data, we predicted porosity and permeability, optimized machine learning parameters, and screened out logging porosity and permeability prediction methods suitable for carbonate reservoirs. The research results show that there is little difference between the four machine learning methods in predicting reservoir porosity. By adjusting the types of input parameters, the porosity and permeability prediction results can be further improved. When using neutron (NPHI), lithological density (RHOB) ) , and acoustic time difference (DT) logging parameter data as input, the prediction accuracy based on LSTM is the highest, the root mean square error (RMSE) of the porosity prediction result is 4.536 2, and the permeability prediction is poor due to the strong heterogeneity of carbonate reservoirs when using only NPHI as the machine learning input parameter, the RF-based reservoir permeability prediction has the highest accuracy, and its RMSE  is 45.882 3.

Key words: carbonate, logging, porosity, permeability, machine learning, prediction