Formation bulk density is one of the critical parameters for formation evaluation. As a safe and environmentally friendly method, the pulsed neutron-gamma density (NGD) measurement is emerging as an alternative to the traditional chemical source-based gamma-gamma density (GGD) measurement. However, in the NGD measurement, the initial energy spectrum, source intensity, and spatial distribution of the secondary inelastic gamma-ray source vary with formation components. Moreover, the energy of inelastic gamma rays is at the MeV level, leading to a non-negligible pair production effect. All these factors affect the accuracy of the bulk-density measurement. In addition, the impact of borehole configuration on NGD is evident, and correction for borehole effects is essential. To improve density accuracy: first, we forgo exploring an explicit theoretical formula for density calculation and instead treat the NGD mathematically as a regression problem and introduce the machine learning regressor, a powerful and popular tool for solving regression problems, into the NGD for the first time; second, we select features less affected by changes in formation chemical composition as input features. Our results show that the final three tuned machine learning regressors selected from the 39 candidate regressors outperform the optimized polynomial model (an optimization of conventional density calculation models) in both accuracy and generalization ability. They complete density prediction and borehole correction in one step, avoiding complex exploration of borehole effects as in the optimized polynomial model and dramatically simplifying the borehole correction process. Moreover, the GaussianProcessRegressor can complete borehole correction without the standoff information and perform well, which is impossible for the optimized polynomial model, broadening application scenarios.

地层体积密度是地层评价的关键参数之一。作为一种安全环保的方法，脉冲中子-伽马密度 (NGD) 测量正在成为传统基于化学源的伽马-伽马密度 (GGD) 测量的替代方法。然而，在NGD测量中，二次非弹性伽马射线源的初始能谱、源强度和空间分布随地层成分而变化。此外，非弹性伽马射线的能量在 MeV 水平，导致不可忽略的对产生效应。所有这些因素都会影响体积密度测量的准确性。此外，井眼构型对NGD的影响是明显的，对井眼效应的修正是必不可少的。提高密度精度：首先，我们放弃探索用于密度计算的明确理论公式，而是在数学上将 NGD 视为回归问题，并首次将机器学习回归器（一种用于解决回归问题的强大且流行的工具）引入 NGD；其次，我们选择受地层化学成分变化影响较小的特征作为输入特征。我们的结果表明，从 39 个候选回归量中选择的最后三个经过调整的机器学习回归量在准确性和泛化能力方面都优于优化多项式模型（传统密度计算模型的优化）。他们一步完成密度预测和井眼校正，避免在优化多项式模型中复杂的钻孔效应探索，并显着简化钻孔校正过程。此外，GaussianProcessRegressor可以在没有距离信息的情况下完成井眼校正，性能良好，这是优化后的多项式模型无法做到的，拓宽了应用场景。