XGBoost algorithm optimized by simulated annealing genetic algrithm for permeability prediction modeling of carbonate reservoirs

Abstract

Carbonate reservoir has strong heterogeneity, complex pore structure and poor correlation between porosity and permeability, so the traditional permeability model can not meet the needs of logging interpretation. Taking the carbonate reservoir of Longwangmiao Formation in Moxi block of central Sichuan as an example, this paper proposes to establish a permeability prediction model by using the XGBoost algorithm of simulated annealing genetic algrithm (SA-GA)hybrid optimization. Combined with core data, five permeability sensitive logging curves (CNL, DEN, DT, and GR) are optimized by calculating correlation coefficients, and the permeability prediction model is established based on XGBoost algorithm, and the XGBoost hyperparameters are optimized by using SA-GA. The method is applied to the evaluation of logging permeability in the study area. The results show that the prediction results of SA-GA-XGBoost algorithm are more consistent with the core data. The adjusted is 0.876, and the root mean square error (RMSE) is only 0.142. The prediction accuracy is better than the conventional permeability model and BP neural network model, which meets the industrial requirements of logging evaluation and provides a new idea for oil and gas exploration in carbonate reservoirs.