###### Abstract
In summary, the above experimental methods for determining porosity and permeability mentioned above are expensive, with a limited number of test points, which is not conducive to analyzing the porosity and permeability characteristics of regional and vertical reservoirs. Indirect porosity interpretation requires a combination of multiple logging curves, such as acoustic time-difference logging (AC) and neutron logging (CNL), corrected with laboratory porosity test data to obtain a more accurate representation of logging porosity; then, permeability is calculated from the indirect porosity by analyzing pore-permeability relationships in the core to obtain a regional longitudinal and planar porosity and permeability logging evaluation [17; 18; 19; 20]. In addition, many scholars have analyzed the linear or nonlinear relationship between experimentally measured porosity and permeability data and geophysical logging parameters to realize multi-angle and multi-method quantitative prediction of formation porosity and permeability parameters, which can then be applied to the actual production process of oilfields [21; 22; 23; 24]. Among them, machine learning, with its sophisticated algorithms, feature engineering, and unique advantage of processing large-scale data to discover trends in the data as well as patterns of correlation between the data, has achieved numerous applications in the oil and gas industry. [PERSON] et al. (2018) comparatively investigated the applicability of support vector machine, linear regression, and neural network methods in predicting rock properties, which show good predictive and generalization abilities [25]. [PERSON] et al. (2019) compared the predictive ability of eight algorithms in predicting formation property parameters, such as porosity and permeability, and found that linear random forest showed good superiority [26]. [PERSON] et al. (2021) concluded that when the dataset is limited, SVM has better reservoir prediction ability than artificial neural networks [27]. In view of this, the application of the random forest regression, support vector machine algorithm, and integrated learning algorithm, combined with a variety of hyper-parameter optimization methods as well as neural network-based and deep learning methods to predict the porosity and permeability have achieved significant prediction results [28; 29; 30; 31]. Compared to traditional linear relationship models, machine learning methods can better solve complex multidimensional nonlinear problems, have strong fault tolerance and reliability, and also provide new ideas for predicting porosity and permeability. Therefore, numerous optimization methods and model applications are also an indispensable part of modeling. The XGBoost algorithm has demonstrated superior performance in prediction and optimization. It has been utilized across numerous applications, where it consistently outperformed other existing algorithms [32; 33; 34]. This paper combined logging and core porosity and permeability data from Qingyang Gas Field in the Ordos Basin, learned experimental test data, established the XGBoost porosity and permeability prediction model, and analyzed the applicability for the study area. Then, the innovative application of homogeneous clustering (K-means) method was used to optimize the XGBoost method and to comprehensively determine the XGBoost prediction model for porosity and permeability that is suitable for the stratigraphy in the study area. Finally, the final prediction model of porosity and permeability was applied in the plane to analyze the distribution of porosity and permeability, which provides a reference basis for the exploration and development of favorable zones.