基于SSA-XGBoost模型的高精度密度预测方法研究

bstract [Background]: Complex lithology well sections require high precision in density logging data, and traditional computational models cannot adequately meet the high precision requirements in these cases. [Purpose]: This study aims to improve the precision of density logging curves using machine learning regression prediction models. [Method]: Firstly, SSA algorithm was used to improve XGBoost, leading to the development of the SSA-XGBoost density prediction model. Then, Monte Carlo N-Particle transport codeMCNP of dual-detector density logging tool instrument was used to obtain stratigraphic data of different densities to validate the predictive effectiveness of the model. By optimizing the parameters of SVR, RFR, and LSTM using the SSA, the SSA-SVR, SSA-RFR, and SSA-LSTM models were constructed to predict the simulated formation density. The predictive performance of each model was compared and analyzed using quantitative evaluation metrics and Taylor diagram models. Finally, the performance of different prediction models on actual density logging data was analyzed. [Result]: In the comparative analysis and processing of actual well density logging data with various models, the SSA-XGBoost model showed smaller errors between predicted and actual density, demonstrating high density accuracy and validating the precision of the method. [Conclusion]: The SSA-XGBoost model demonstrates higher predictive accuracy than traditional spine-ribs plot, showing great potential for applications in the processing of actual density logging data.