## 3 Experimental Data Analysis  
In this paper, the sample data were real data of the Tarim Oilfield in Western China. A cumulative total of 2800 datasets were assembled, encompassing various logging parameters such as shared photoelectric index (PE), density (DEN), sonic time difference (AC), gamma (GR), 2.5 m bottom gradient resistivity (R25), neutron (CNL), and high-resolution array induced polarization resistivity (M2R1). These parameters constitute fundamental physical measurements obtained from logging instruments. Utilized to ascertain the properties of underground formations, these data facilitated the analysis of stratigraphic composition and attributes. The collected porosity data mainly stemmed from laboratory measurements and interpretations derived from well-logging data. Laboratory measurements were conducted through lithological sampling methods, starting with the use of a core drill to perform coring operations and obtain core samples. These samples were then sent to the laboratory for porosity analysis. Well-logging interpretation used techniques such as natural gamma logging, neutron logging, and sonic logging to measure the physical properties of strata and model the physical characteristics of subsurface rocks. To ensure data accuracy and reliability, we corrected and processed the collected logging data. Finally, these logging data were integrated with established physical property models to interpret the structure and characteristics of the strata. PE describes a material's ability to absorb photons during the photoelectric effect [32]. It includes the mass photoelectric absorption cross-section index and the volume photoelectric absorption cross-section index of rocks, representing the average photoelectric absorption cross-section of photon interactions with an electron in the rock. Through the photoelectric absorption cross-section index, it is possible to predict and assess the porosity of rocks. The DEN logging technique estimates the density of geological formations by quantifying radiation absorption [33]. The DEN logging apparatus comprised a radiation emitter and corresponding detectors, calculating density by measuring changes in radiation intensity after passing through the formation, thereby determining the physical properties of the rock layers. AC logging evaluated the porosity of rock formations by measuring the propagation speed of sound waves in the rock formations [34]. The logging tool's acoustic sensor emits sound waves, recording their travel time. Using these data and the established sound velocity, the formation's acoustic transit time difference was determined. GR logging is a method for evaluating rock properties by measuring the natural gamma radiation intensity of rock layers [35]. Since rocks contain varying amounts of radioactive elements that continuously emit radiation, these measurements allow us to delineate the geological profile of a borehole, determine the shale content in sandstone and shale profiles, and qualitatively assess the porosity of rock layers. R25 reflects the resistivity characteristics of fluids in a formation. Its measurement is based on the resistivity of the rock, and by analyzing the resistivity differences between the fluids and the rock matrix, the formation's porosity can be estimated. CNL Logging measures the absorption of fast and thermal neutrons by rock formations, to reflect the hydrogen content, indirectly assessing the porosity of the formation [36]. A high hydrogen content usually indicates higher porosity, as hydrogen is a major component of fluids (such as water or hydrocarbons) in the rock, which typically occupy the pore spaces. M2R1 logging uses multiple receiver and transmitter combinations to measure the resistivity of formations at different radial depths, providing high-resolution resistivity data that, when combined with geological and geophysical models, can predict the porosity of formations. Next, we analyzed the distribution of the raw data through statistical analysis.