Research on High-precision Density Prediction Method Based on SSA-XGBoost

[Background]: Complex lithology well sections demand high precision in density logging data, which traditional computational models fail to adequately meet. [Purpose]: This study aims to improve the precision of density logging curves utilizing machine learning regression prediction models. [Methods]: Firstly, Monte Carlo N-Particle transport code of dual-detector density logging tool instrument was utilized to obtain stratigraphic data of varying density, which served to validate the predictive effectiveness of the model.

Then, SSA algorithm was adopted to enhance XGBoost, resulting in the development of the SSA-XGBoost density prediction model. By optimizing the parameters of SVR, RFR, and LSTM employing 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 applying quantitative evaluation metrics and Taylor diagram models.