Complex lithology well sections require high precision in density well logging data whilst traditional computational models are difficult to meet this high precision requirement.

This study aims to improve the precision of density logging curves utilizing machine learning regression prediction models.

Firstly, Monte Carlo N-Particle transport code (MCNP) was utilized to obtain stratigraphic data of varying density of dual-detector density logging tool instrument to validate the predictive effectiveness of the model. Then, sparrow search algorithm (SSA) was adopted to enhance XGBoost model, resulting in the development of the SSA-XGBoost density prediction model. Subsequently, the parameters of support vector regression (SVR), random forest regression (RFR), and long short-term memory (LSTM) were optimized by employing the SSA to construct the SSA-SVR, SSA-RFR, and SSA-LSTM models to predict the simulated formation density, and quantitative evaluation metrics and Taylor diagram models were applied to the comparison and analysis of the predictive performance of each model. Finally, the performance of different prediction models was evaluated on actual density logging data.

Results of the comparative analysis and processing of actual well density logging data with various models show that the SSA-XGBoost model exhibits smaller errors between predicted and actual density and its error in predicting formation density is 0.017 4 g?cm^-3, which is much lower than the traditional spine-ribs plots error of 0.028 4 g?cm^-3.

The SSA-XGBoost model demonstrates higher predictive accuracy than traditional spine-ribs plots and other models, showing great potential for applications in the processing of actual density logging data.