基于NOA 优化随机森林算法的砂岩孔隙度预测

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中图分类号：P631 文献标识码：A DOI：10. 13810/j. cnki. issn. 1000-7210. 20240333

Porosity prediction of sandstone based on NOA optimized random forest algorithm

LU Yangdi1，DENG Rui1，WANG Yi2 ，ZHANG Cheng’en2，DUAN Hongzhen2 ollege of Geophysics and Petroleum Resources，Yangtze University，Wuhan，Hubei 430100，China； 2. China National Logging Corporation，Xi’an，Shaanxi 710077，China）

Abstract：In oil and gas exploration and development，porosity is an important parameter to evaluate reservoir physical properties，especially in flooded well evaluation. Accurate porosity prediction is the key significance to the evaluation of remaining oil and subsequent production and development. The conventional linear porosity model has limitations in prediction accuracy，and the random forest regression model often faces the problems of low optimization efficiency，complex parameter adjustment，and large consumption of computing resources when traditional parameter optimization methods are employed. To improve the accuracy and efficiency of po⁃ rosity prediction，this paper proposes a new method to optimize the random forest regression model based on the nutcracker optimization algorithm（NOA）. This method is inspired by the foraging，storage，and food re⁃ trieval behavior of the North American bird nutcracker. In this study，the random forest regression model is sub⁃ jected to hyperparameter optimization through NOA with the acoustic time difference，compensated density， and compensated neutron curve as the input features of the model and the core porosity as the target value， which avoids locally optimal solutionand thus determines globally optimal parameter combination. Compared with the traditional grid search method，NOA shows higher efficiency in hyperparameter optimization. The re⁃ sults of data analysis and model prediction show that this method not only speeds up the training speed of the ran⁃ dom forest model but also effectively improves the fitting effect and prediction accuracy of the porosity model. Keywords：NOA，random forest，porosity，flooded wells

芦杨笛，邓瑞，汪益，等 . 基于 NOA 优化随机森林算法的砂岩孔隙度预测［J］. 石油地球物理勘探，2025，60（3）：576‑586.

LU Yangdi，DENG Rui，WANG Yi，et al. Porosity prediction of sandstone based on NOA optimized random forest algorithm［J］. Oil Geophysical Prospecting，2025，60（3）：576‑586.

0 引言

在原油开采过程中，随着采出原油越多，油藏内部的压力会下降，导致原油的流动性逐渐降低，自然驱动力不足；或者当油藏地质条件复杂时，单纯依靠自然驱动无法有效开采油藏，注水开发成为了油田开发到一定阶段后的必然手段。（剩余11776字）