Ore prospecting method based on oil gas hydrocarbon micro-leakage alteration information identification and uranium mineralization association

Abstract

The invention belongs to the field of mineral resource exploration, and particularly relates to an oil-gas hydrocarbon micro-leakage alteration information identification and uranium mineralization association-based prospecting method, which comprises the following steps of: 1, selecting a research area and collecting basic data; 2, oil gas hydrocarbon micro-leakage alteration information is extracted; step 3, establishing an alteration information and uranium mineralization association model; 4, delineating a favorable uranium mineralization area; and 5, drilling verification and model optimization. According to the method, alteration information caused by oil gas micro-leakage is accurately extracted through multi-source data fusion, dynamic monitoring and intelligent modeling technologies, a correlation model of the alteration information and uranium mineralization is established, the exploration efficiency of the sandstone type uranium mine is effectively improved, and the exploration cost of the sandstone type uranium mine is reduced. The problems that deep alteration information is difficult to effectively recognize in a thick covering layer basin, oil gas micro-leakage and uranium mineralization mineralization are not clear in association, the utilization rate of multi-source data is low, the prospecting period is long, cost is high and the like in a traditional prospecting technology are solved.

Description

Technical Field

[0001] The invention belongs to the field of mineral resource exploration, and in particular relates to a prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization. Background Art

[0002] Sandstone-type uranium deposits are an important type of uranium resource worldwide, and their mineralization is usually closely related to the oxidation-reduction interface in sedimentary basins. Studies in recent years have shown that micro-seepage in oil and gas reservoirs can have a significant impact on the oxidation-reduction environment in the formation, thereby promoting the formation of uranium mineralization. Oil and gas hydrocarbon micro-seepage refers to the phenomenon that hydrocarbon substances (such as methane, ethane, etc.) in oil and gas reservoirs migrate upward through formation cracks or permeable formations. This process may cause local alteration and change the geochemical environment.

[0003] Traditional uranium prospecting methods mostly rely on surface geological, geochemical and geophysical surveys, but in thick overburden basins, deep alteration information is difficult to obtain directly, resulting in low prospecting efficiency. In the existing technology, there is a lack of a comprehensive prospecting method that can quickly identify oil and gas hydrocarbon micro-seepage alteration information and associate it with uranium mineralization.

[0004] Therefore, an innovative method is urgently needed to quickly identify favorable areas for uranium mineralization by combining oil and gas hydrocarbon microseepage, alteration information extraction and uranium mineralization mechanism. Summary of the invention

[0005] The purpose of the present invention is to provide a prospecting method based on the identification of alteration information of oil and gas hydrocarbon microseepage and the association with uranium mineralization. The method uses multi-source data fusion, dynamic monitoring and intelligent modeling technology to accurately extract the alteration information caused by oil and gas microseepage, establish a correlation model between alteration information and uranium mineralization, effectively improve the exploration efficiency of sandstone-type uranium deposits, and solve the problems that traditional prospecting technology is difficult to effectively identify deep alteration information in thick overburden basins, the association between oil and gas microseepage and uranium mineralization is unclear, the utilization rate of multi-source data is low, and the prospecting cycle is long and the cost is high.

[0006] The technical solution to achieve the purpose of the present invention is:

[0007] A prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and its association with uranium mineralization, the method comprising:

[0008] Step 1: Study area selection and basic data collection;

[0009] Step 2: Extracting oil and gas hydrocarbon micro-leakage alteration information;

[0010] Step 3: Establishment of a correlation model between alteration information and uranium mineralization;

[0011] Step 4: Delineation of favorable areas for uranium mineralization;

[0012] Step 5: Drilling verification and model optimization.

[0013] The research area selected in step 1 has the following characteristics: the geological background of oil and gas reservoirs or oil and gas seepage exists; the mineralization conditions of sandstone-type uranium mineralization exist; there may be alteration information under the thick overburden, but it is difficult to identify by traditional prospecting methods; the basic data collected include: geological data, oil and gas data, remote sensing data, geophysical data, geochemical data, and overburden thickness distribution data.

[0014] The step 2 includes: gas anomaly extraction, remote sensing information anomaly extraction, geochemical anomaly extraction, and geophysical data anomaly analysis.

[0015] The step 3 comprises:

[0016] Step 3.1: Multi-source data fusion, construction of 3D alteration zone distribution model, extraction of key features, and optimization of weight analysis;

[0017] Step 3.2: Analyze and quantify the impact of oil and gas microleakage on uranium mineralization and analyze the mineralization mechanism;

[0018] Step 3.3: Analyze the correlation between alteration information and uranium mineralization, build a machine learning model, and identify and model spatiotemporal characteristics.

[0019] The step 3.1 comprises:

[0020] Step 3.1.1, Refined processing and fusion of multi-source data: Use multi-scale spatial analysis technology to refine different data types to ensure the spatial resolution and temporal consistency of the data; use a multi-source data fusion algorithm based on Bayesian theory to dynamically adjust the weight of each data type to obtain fused multi-source data;

[0021] Step 3.1.2, constructing a three-dimensional alteration zone distribution model: Based on the fused multi-source data and combined with geological modeling software, construct a three-dimensional distribution model of the alteration zone; interpolate and predict the spatial distribution characteristics of the alteration zone through geostatistical methods;

[0022] Step 3.1.3, key feature extraction and weight analysis optimization: When extracting the key features related to uranium mineralization in the alteration zone, principal component analysis and feature selection algorithm are combined to screen out the characteristic variables that have the greatest impact on uranium mineralization; based on the weight analysis method of multi-source data, the hierarchical analysis method is introduced to optimize the weight distribution scheme.

[0023] The step 3.2 comprises:

[0024] Step 3.2.1. Effect of local reducing environment on uranium precipitation: Combine laboratory simulation and field measurement data to quantify the effect of local reducing environment caused by oil and gas micro-leakage on uranium precipitation;

[0025] Step 3.2.2, Dynamic simulation of oxidation-reduction interface migration: Use numerical simulation software to dynamically simulate the oxidation-reduction interface migration caused by oil and gas micro-leakage to quantify its impact on uranium mineralization;

[0026] Step 3.2.3 Comprehensive analysis of mineralization mechanism: Based on the dynamic simulation results and the distribution characteristics of the alteration zones, combined with the mineralization examples of typical uranium mineralization areas, the mineralization mechanism model of oil and gas microseepage and uranium mineralization is verified.

[0027] The step 3.3 comprises:

[0028] Step 3.3.1, Correlation analysis and data preprocessing: quantitatively analyze the correlation between alteration information and uranium mineralization, optimize the data preprocessing process; use statistical test methods to screen characteristic variables that are highly correlated with uranium mineralization;

[0029] Step 3.3.2, machine learning model construction and optimization: Use random forest and XGBoost algorithms to model and classify multi-source data, build a high-precision prediction model for alteration information and uranium mineralization; improve the prediction performance and stability of the model through hyperparameter optimization methods;

[0030] Step 3.3.3, deep learning modeling and spatial feature recognition: Use convolutional neural networks to automatically identify and model the spatial distribution characteristics of alteration information; combine long short-term memory networks to model the time series changes of alteration information.

[0031] The step 4 comprises:

[0032] Step 4.1: Based on the alteration information and uranium mineralization correlation model, the multi-source data are superimposed and analyzed to delineate the favorable areas for uranium mineralization;

[0033] Step 4.2: Generate a distribution map of favorable uranium mineralization areas based on the identified favorable uranium mineralization areas.

[0034] The step 4.1 comprises:

[0035] Step 4.1.1, multi-source data overlay analysis: Based on the alteration information and uranium mineralization correlation model, multi-source data are overlaid and analyzed to identify high potential areas of uranium mineralization; weighted overlay method is used to assign weights to different prospecting indicators;

[0036] Step 4.1.2, application of multi-objective optimization algorithm: when delineating favorable areas for uranium mineralization, a multi-objective optimization algorithm is used to weigh the prospecting indicators and determine the priority exploration areas;

[0037] Step 4.1.3: Dynamic adjustment and feedback optimization: According to the actual exploration results, dynamically adjust the parameters and weights of the delineation model to form a closed-loop process of "prediction-verification-optimization".

[0038] The step 5 comprises:

[0039] Step 5.1, Drilling verification: Drill holes are arranged in the identified favorable areas of uranium mineralization to verify the matching degree between alteration information and uranium mineralization; the mineralization mechanism is further verified by combining trace element analysis and secondary mineral identification of drilling samples;

[0040] Step 5.2, model optimization: According to the drilling verification results, adjust the oil and gas hydrocarbon micro-leakage alteration information extraction method and associated model parameters; use an adaptive optimization algorithm to dynamically adjust the model weights.

[0041] The beneficial technical effects of the present invention are:

[0042] (1) Multi-source data fusion and dynamic weight optimization: Innovatively integrate multi-source data such as gas geochemistry, remote sensing, geochemistry and geophysical exploration, combine dynamic weight analysis and Bayesian theory, and optimize the construction method of the three-dimensional distribution model of the alteration zone.

[0043] (2) Improving the accuracy of uranium mineralization prediction: Through the fusion of multi-source data and the construction of a three-dimensional alteration zone distribution model, the alteration information under the overburden is accurately extracted, which significantly improves the accuracy and reliability of uranium mineralization prediction.

[0044] (3) Revealing the mechanism of oil and gas micro-seepage and uranium mineralization: By introducing the dynamic simulation of oxidation-reduction interface migration and its effect on uranium precipitation for the first time, the intrinsic connection between the local reducing environment caused by oil and gas micro-seepage and uranium mineralization was clarified, providing new insights into the mineralization theory of sandstone-type uranium deposits.

[0045] (4) Innovative application of machine learning and deep learning: Machine learning algorithms such as random forest (RF) and XGBoost are used to model multi-source data, and convolutional neural networks (CNN) and long short-term memory networks (LSTM) are introduced to achieve automatic identification and modeling of the spatial distribution characteristics and time series changes of alteration information.

[0046] (5) Accelerate prospecting efficiency and reduce exploration costs: Use machine learning and deep learning technology modeling, combined with multi-objective optimization algorithms, to quickly identify favorable uranium mineralization areas, significantly shorten the prospecting cycle, reduce the need for large-scale drilling, and reduce exploration costs. DETAILED DESCRIPTION

[0047] The present invention is further described in detail below in conjunction with embodiments.

[0048] The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization provided by the present invention specifically comprises the following steps:

[0049] Step 1: Study area selection and basic data collection

[0050] Step 1.1: Study area selection

[0051] The study area should have the following characteristics:

[0052] The geological setting in which oil and gas reservoirs or oil and gas seeps exist;

[0053] The mineralization conditions for sandstone-type uranium mineralization are available, such as the development of oxidation-reduction interface;

[0054] There may be alteration information under thick overburden, but it is difficult to identify using traditional prospecting methods.

[0055] The Qaidam Basin in Qinghai has the above characteristics, and this invention takes the Qaidam Basin in Qinghai as the research area.

[0056] Step 1.2: Basic data collection

[0057] Collect geological, geophysical and geochemical data on the study area, including:

[0058] Geological data: collect stratigraphic distribution maps, structural maps, and sedimentary facies maps;

[0059] Oil and gas data: Obtain oil and gas reservoir distribution, oil and gas leakage characteristics and oil and gas accumulation history;

[0060] Remote sensing data: Obtain historical uranium mineralization exploration data using hyperspectral and thermal infrared remote sensing images;

[0061] Geophysical data: collect seismic profile data or drill hole information;

[0062] Geochemical data: Collect trace element distribution data of soil, sediment and water samples in the area;

[0063] Collecting the overburden thickness distribution data provides a basis for subsequent data correction.

[0064] Step 2: Extraction of oil and gas hydrocarbon microleakage alteration information

[0065] Step 2.1: Gas anomaly extraction

[0066] (1) Sampling point optimization:

[0067] Based on the oil and gas reservoir distribution and fault structure model, the sampling point layout is optimized, focusing on covering possible oil and gas micro-leakage channels.

[0068] 100-200 sampling points are arranged in the study area, and the sampling spacing is dynamically adjusted according to the thickness of the overburden and geological conditions (usually 300m-1km).

[0069] (2) Dynamic monitoring technology:

[0070] The soil gas dynamic monitoring technology is introduced to conduct time series analysis on gas components (methane, ethane, hydrogen, carbon dioxide) and identify the periodic changes of abnormal gases.

[0071] Use a portable gas analyzer to monitor the changes in gas concentration at the sampling point in real time and capture the dynamic characteristics of oil and gas leakage.

[0072] (3) Data analysis and delineation of abnormal areas:

[0073] Statistical methods (such as Kriging interpolation) are used to draw gas abnormal concentration distribution maps.

[0074] Combined with dynamic monitoring results, gas anomaly areas can be identified and the main paths of oil and gas micro-leakage can be inferred.

[0075] Step 2.2: Remote sensing information anomaly extraction

[0076] (1) Hyperspectral remote sensing analysis:

[0077] An automatic matching algorithm of the secondary mineral spectral feature library is introduced to automatically identify the distribution of iron oxides (such as hematite and limonite) and clay minerals (such as montmorillonite and kaolinite).

[0078] Extract the spectral reflectance characteristics of secondary altered minerals and generate mineral distribution maps.

[0079] (2) Thermal infrared remote sensing analysis:

[0080] Thermal infrared remote sensing data (such as ASTER or Landsat 8TIRS) are used to analyze the distribution of surface thermal anomalies.

[0081] Combined with the coupled analysis of surface temperature and gas anomalies, the thermal effect caused by oil and gas hydrocarbon micro-seepage is inferred.

[0082] Step 2.3: Multi-element anomaly analysis and geochemical anomaly extraction

[0083] (1) Soil sample collection and analysis:

[0084] Soil samples were collected in the gas anomaly area and analyzed for trace elements (U, Mo, V, Se, Re) and rare earth element contents.

[0085] The element ratio analysis method (such as U / Th and U / V ratios) is introduced to improve the accuracy of identifying uranium mineralization anomalies.

[0086] (2) Multi-element anomaly analysis:

[0087] Principal component analysis (PCA) and factor analysis were used to extract multi-element combination characteristics related to uranium mineralization.

[0088] Generate a geochemical anomaly distribution map, overlay it with the gas and remote sensing anomalies in step 2.1 and step 2.2, and delineate the geochemical anomaly areas related to oil and gas microseepage.

[0089] Step 2.4: Analyze the anomaly of geophysical data and infer the distribution range of the alteration zone

[0090] (1) Induced polarization (IP) measurement:

[0091] Conduct induced polarization measurements in geochemical anomaly areas to identify high polarizability anomalies beneath the overburden.

[0092] The high polarizability may be related to the enriched metal sulfides or clay minerals in the alteration zone. Based on the three-dimensional inversion technology, a three-dimensional electrical model of the alteration zone is constructed.

[0093] (2) Magnetic measurement:

[0094] High-precision magnetic methods are used to detect magnetic anomalies under the overburden and infer the distribution range of the alteration zone.

[0095] Magnetic anomalies may be related to the precipitation of iron oxide minerals (such as hematite and limonite). The accuracy of alteration zone identification can be improved by combining magnetic anomalies with the joint inversion of hyperspectral data.

[0096] Step 3: Establishment of a correlation model between alteration information and uranium mineralization

[0097] Step 3.1: Multi-source data fusion, construction of 3D alteration zone distribution model, extraction of key features, and optimization of weight analysis

[0098] Step 3.1.1: Refined processing and fusion of multi-source data

[0099] Based on the comprehensive analysis of gas anomalies, geochemical anomalies, remote sensing anomalies and geophysical anomalies, multi-scale spatial analysis technology is further introduced to carry out refined processing of different data types to ensure the spatial resolution and temporal consistency of the data.

[0100] A multi-source data fusion algorithm based on Bayesian theory is used to dynamically adjust the weights of each data type to obtain fused multi-source data; adaptive weights are assigned according to the performance of different data in different regions to improve the regional adaptability and overall accuracy of the model.

[0101] Step 3.1.2: Construction of three-dimensional alteration zone distribution model

[0102] Based on the fused multi-source data and combined with geological modeling software (such as GOCAD or Petrel), a three-dimensional distribution model of the alteration zone is constructed.

[0103] Geostatistical methods (such as Kriging interpolation) are introduced to interpolate and predict the spatial distribution characteristics of the alteration zone to ensure the continuity and accuracy of the model.

[0104] Step 3.1.3: Key feature extraction and weight analysis optimization

[0105] When extracting key features related to uranium mineralization in alteration zones (such as oxidation-reduction interface and trace element enrichment zone), principal component analysis (PCA) and feature selection algorithms (such as LASSO regression) are combined to screen out the characteristic variables that have the greatest impact on uranium mineralization.

[0106] Based on the weight analysis method of multi-source data, the analytic hierarchy process (AHP) is introduced to optimize the weight distribution scheme to make the model more scientific and interpretable.

[0107] Step 3.2: Analyze and quantify the influence of oil and gas micro-seepage on uranium mineralization, analyze the mineralization mechanism, and analyze the influence of the local reducing environment caused by oil and gas micro-seepage on uranium precipitation;

[0108] The dynamic simulation technology of oxidation-reduction interface migration is introduced to quantify the impact of oil and gas microleakage on uranium mineralization.

[0109] Step 3.2.1. Effect of local reducing environment on uranium precipitation

[0110] Combining laboratory simulation and field measurement data, the influence of the local reducing environment (such as H2S and CH4 concentrations) caused by oil and gas microseepage on uranium precipitation is further quantified.

[0111] Reaction path simulation technology was introduced to simulate the migration and precipitation process of uranium at the oxidation-reduction interface and to clarify the chemical mechanism between oil and gas microleakage and uranium mineralization.

[0112] Step 3.2.2: Dynamic simulation of redox interface migration

[0113] Numerical simulation software (such as COMSOL Multiphysics) is used to dynamically simulate the oxidation-reduction interface migration caused by oil and gas microleakage and quantify its impact on uranium mineralization.

[0114] Optimize simulation parameters (such as permeability, fluid pressure gradient, etc.) to ensure the authenticity and regional applicability of the model.

[0115] Step 3.2.3 Comprehensive analysis of mineralization mechanism

[0116] By integrating the dynamic simulation results and the distribution characteristics of the alteration zones, combined with the mineralization examples of typical uranium mineralization areas, the mineralization mechanism model of oil and gas microseepage and uranium mineralization was verified, providing a theoretical basis for subsequent modeling.

[0117] Step 3.3: Analyze the correlation between alteration information and uranium mineralization, build a machine learning model, and identify and model spatiotemporal features

[0118] Step 3.3.1. Correlation analysis and data preprocessing

[0119] Quantitatively analyze the correlation between alteration information (such as gas concentration, trace element content, and geophysical anomaly intensity) and uranium mineralization, and optimize the data preprocessing process (such as normalization and denoising).

[0120] Statistical test methods (such as Pearson correlation coefficient and chi-square test) are introduced to screen characteristic variables that are highly correlated with uranium mineralization.

[0121] Step 3.3.2: Machine Learning Model Construction and Optimization

[0122] Random Forest (RF) and XGBoost algorithms are used to model and classify multi-source data to build a high-precision prediction model for alteration information and uranium mineralization.

[0123] Improve the predictive performance and stability of your model through hyperparameter optimization methods such as grid search or Bayesian optimization.

[0124] Step 3.3.3: Deep learning modeling and spatial feature recognition

[0125] The convolutional neural network (CNN) is introduced to automatically identify and model the spatial distribution characteristics of alteration information, further improving the model's ability to recognize complex geological features.

[0126] Combined with the long short-term memory network (LSTM), the time series changes of alteration information are modeled to explore the potential impact of the dynamic process of oil and gas microseepage on uranium mineralization.

[0127] Step 4: Delineation of favorable uranium mineralization areas

[0128] Step 4.1: Based on the alteration information and uranium mineralization correlation model, superimpose and analyze multi-source data to delineate favorable areas for uranium mineralization

[0129] Step 4.1.1: Multi-source data overlay analysis

[0130] Based on the correlation model between alteration information and uranium mineralization, multi-source data (such as geochemical anomalies, geophysical anomalies, gas anomalies, etc.) are superimposed and analyzed to identify high-potential areas for uranium mineralization.

[0131] The weighted superposition method is introduced to assign weights to different prospecting indicators to ensure the scientificity and accuracy of the delineation results.

[0132] Step 4.1.2: Application of multi-objective optimization algorithm

[0133] When delineating favorable areas for uranium mineralization, a multi-objective optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) is used to weigh between prospecting indicators (such as geological conditions, economic feasibility, and exploration difficulty) to determine priority exploration areas.

[0134] Step 4.1.3: Dynamic adjustment and feedback optimization

[0135] According to the actual exploration results, the parameters and weights of the delineation model are dynamically adjusted to form a closed-loop process of "prediction-verification-optimization" to gradually improve the reliability of the delineation results.

[0136] Step 4.2: Generate a distribution map of favorable uranium mineralization areas based on the identified favorable uranium mineralization areas

[0137] Step 4.2.1: Generate distribution map using GIS technology

[0138] GIS technology is used to generate a distribution map of favorable uranium mineralization areas, mark the priority of the predicted areas, and combine three-dimensional visualization tools (such as ArcScene or Leapfrog) to display the spatial distribution of alteration zones and uranium mineralization potential areas.

[0139] Step 4.2.2: Result display

[0140] The delineation results are intuitively displayed through visual charts (such as heat maps, profile diagrams, etc.), providing a scientific basis for mineral exploration decisions.

[0141] Step 5: Drilling verification and model optimization

[0142] Step 5.1: Drilling verification

[0143] Drill holes are arranged in the identified favorable areas of uranium mineralization to verify the matching degree between alteration information and uranium mineralization.

[0144] The mineralization mechanism is further verified by combining trace element analysis of drilling samples with secondary mineral identification.

[0145] Step 5.2: Model optimization

[0146] According to the drilling verification results, the oil and gas hydrocarbon micro-leakage alteration information extraction method and correlation model parameters are adjusted.

[0147] Introduce adaptive optimization algorithm to dynamically adjust model weights and improve prospecting accuracy.

[0148] The present invention is described in detail above in conjunction with the embodiments, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of ordinary technicians in the field without departing from the purpose of the present invention. The contents not described in detail in the present invention can adopt the existing technology.

Claims

1. A prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and its association with uranium mineralization, characterized in that: The method comprises: Step 1: Study area selection and basic data collection; Step 2: Extracting oil and gas hydrocarbon micro-leakage alteration information; Step 3: Establishment of a correlation model between alteration information and uranium mineralization; Step 4: Delineation of favorable areas for uranium mineralization; Step 5: Drilling verification and model optimization.

2. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 1 is characterized in that: The study area selected in step 1 has the following characteristics: The geological background of oil and gas reservoirs or oil and gas seepage exists; the mineralization conditions of sandstone-type uranium mineralization are met; there may be alteration information under the thick overburden, but it is difficult to identify with traditional prospecting methods; the basic data collected include: geological data, oil and gas data, remote sensing data, geophysical data, geochemical data, and overburden thickness distribution data.

3. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 1 is characterized in that: The step 2 includes: gas anomaly extraction, remote sensing information anomaly extraction, geochemical anomaly extraction, and geophysical data anomaly analysis.

4. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 1 is characterized in that: The step 3 comprises: Step 3.1: Multi-source data fusion, construction of 3D alteration zone distribution model, extraction of key features, and optimization of weight analysis; Step 3.2: Analyze and quantify the impact of oil and gas microleakage on uranium mineralization and analyze the mineralization mechanism; Step 3.3: Analyze the correlation between alteration information and uranium mineralization, build a machine learning model, and identify and model spatiotemporal characteristics.

5. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 4 is characterized in that: The step 3.1 comprises: Step 3.1.1, Refined processing and fusion of multi-source data: Use multi-scale spatial analysis technology to refine different data types to ensure the spatial resolution and temporal consistency of the data; use a multi-source data fusion algorithm based on Bayesian theory to dynamically adjust the weight of each data type to obtain fused multi-source data; Step 3.1.2, constructing a three-dimensional alteration zone distribution model: Based on the fused multi-source data and combined with geological modeling software, construct a three-dimensional distribution model of the alteration zone; interpolate and predict the spatial distribution characteristics of the alteration zone through geostatistical methods; Step 3.1.3, key feature extraction and weight analysis optimization: When extracting the key features related to uranium mineralization in the alteration zone, principal component analysis and feature selection algorithm are combined to screen out the characteristic variables that have the greatest impact on uranium mineralization; based on the weight analysis method of multi-source data, the hierarchical analysis method is introduced to optimize the weight distribution scheme.

6. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 4 is characterized in that: The step 3.2 comprises: Step 3.2.

1. Effect of local reducing environment on uranium precipitation: Combine laboratory simulation and field measurement data to quantify the effect of local reducing environment caused by oil and gas micro-leakage on uranium precipitation; Step 3.2.2, Dynamic simulation of oxidation-reduction interface migration: Use numerical simulation software to dynamically simulate the oxidation-reduction interface migration caused by oil and gas micro-leakage to quantify its impact on uranium mineralization; Step 3.2.3 Comprehensive analysis of mineralization mechanism: Based on the dynamic simulation results and the distribution characteristics of the alteration zones, combined with the mineralization examples of typical uranium mineralization areas, the mineralization mechanism model of oil and gas microseepage and uranium mineralization is verified.

7. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 4 is characterized in that: The step 3.3 comprises: Step 3.3.1, Correlation analysis and data preprocessing: quantitatively analyze the correlation between alteration information and uranium mineralization, optimize the data preprocessing process; use statistical test methods to screen characteristic variables that are highly correlated with uranium mineralization; Step 3.3.2, machine learning model construction and optimization: Use random forest and XGBoost algorithms to model and classify multi-source data, build a high-precision prediction model for alteration information and uranium mineralization; improve the prediction performance and stability of the model through hyperparameter optimization methods; Step 3.3.3, deep learning modeling and spatial feature recognition: Use convolutional neural networks to automatically identify and model the spatial distribution characteristics of alteration information; combine long short-term memory networks to model the time series changes of alteration information.

8. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 1 is characterized in that: The step 4 comprises: Step 4.1: Based on the alteration information and uranium mineralization correlation model, the multi-source data are superimposed and analyzed to delineate the favorable areas for uranium mineralization; Step 4.2: Generate a distribution map of favorable uranium mineralization areas based on the identified favorable uranium mineralization areas.

9. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 8, characterized in that: The step 4.1 comprises: Step 4.1.1, multi-source data overlay analysis: Based on the alteration information and uranium mineralization correlation model, multi-source data are overlaid and analyzed to identify high potential areas of uranium mineralization; weighted overlay method is used to assign weights to different prospecting indicators; Step 4.1.2, application of multi-objective optimization algorithm: when delineating favorable areas for uranium mineralization, a multi-objective optimization algorithm is used to weigh the prospecting indicators and determine the priority exploration areas; Step 4.1.3: Dynamic adjustment and feedback optimization: According to the actual exploration results, dynamically adjust the parameters and weights of the delineation model to form a closed-loop process of "prediction-verification-optimization".

10. The prospecting method based on the identification of oil and gas hydrocarbon micro-leakage alteration information and the association with uranium mineralization according to claim 1, characterized in that: The step 5 comprises: Step 5.1, Drilling verification: Drill holes are arranged in the identified favorable areas of uranium mineralization to verify the matching degree between alteration information and uranium mineralization; trace element analysis of drilling samples and identification of secondary minerals are combined to further verify the mineralization mechanism; Step 5.2, model optimization: According to the drilling verification results, adjust the oil and gas hydrocarbon micro-leakage alteration information extraction method and associated model parameters; use an adaptive optimization algorithm to dynamically adjust the model weights.