Oil-rich coal distribution prediction device and method for quantitative analysis of coal-containing basin sedimentary environment

Abstract

The invention belongs to the technical field of energy, and discloses an oil-rich coal distribution prediction device and method for quantitative analysis of a coal-containing basin sedimentary environment, and the method comprises the steps: investigation and basic geological analysis; performing quantitative research on the coal-containing basin sedimentary environment; researching the occurrence rule of the oil-rich coal; and researching a deposition environment control oil-rich coal formation mechanism. Typical oil-rich coal samples are selected for collection according to the coal seam development law of the West Mountain kiln group of the Sanjiang Lake, the sedimentary environment and the sedimentary system of the West Mountain kiln group are quantitatively researched through experiments, the occurrence law of the oil-rich coal is disclosed, and a technology for predicting the distribution of the oil-rich coal based on quantitative analysis of the sedimentary environment of the coal-containing basin is formed.

Description

Technical Field

[0001] The present invention belongs to, but is not limited to, the field of energy technology, and particularly relates to a device and method for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of coal-bearing basins. Background Art

[0002] At present, some studies use geochemical parameters of coal seam samples (such as total organic carbon content TOC, hydrogen index HI, oxygen index OI, etc.) to predict the distribution of oil-rich coal. These methods analyze the pyrolysis characteristics, maturity, and carbon-hydrogen ratio of coal-rock organic matter through laboratory measurements, determine whether the coal seam has the characteristics of oil-rich coal, and combine well logging data for regional speculation.

[0003] Technical problems to be solved:

[0004] This method mainly relies on laboratory analysis data, has a low prediction accuracy for un-drilled areas, and cannot effectively infer the influence of large-scale sedimentary environments on the formation of oil-rich coal.

[0005] Due to the insufficient combination of sedimentary environment, stratigraphic structure, and paleoclimate factors, this method is difficult to accurately reveal the genetic mechanism of oil-rich coal, restricting its applicability in different geological units.

[0006] The data acquisition cost is relatively high, the experimental process is time-consuming, and the coal sample analysis data is relatively discrete, making it difficult to be used for continuous oil-rich coal distribution modeling.

[0007] Existing technology: Prediction of coal seam oil richness based on well logging and seismic attribute analysis

[0008] Another existing method uses well logging parameters (such as acoustic travel time, density, gamma ray, etc.) and seismic attribute analysis to infer the organic matter abundance and maturity of coal seams. By establishing a statistical relationship between well logging parameters and the occurrence of oil-rich coal, this method can predict the oil-rich potential of coal seams to a certain extent and combine seismic data for regional-scale oil-rich coal distribution speculation.

[0009] Technical problems to be solved:

[0010] This method has a strong dependence on well logging data, has a low prediction accuracy in areas with insufficient drilling density, and is difficult to be effectively applied to the prediction of oil-rich coal in un-drilled areas.

[0011] Due to the limited resolution of seismic data, it is difficult to identify fine-scale sedimentary environment characteristics, resulting in great uncertainty in the prediction of the distribution of oil-rich coal.

[0012] Modern technical means such as machine learning and big data analysis are not fully utilized, resulting in poor adaptability of the prediction model to changes in geological conditions and inability to achieve high-precision quantitative analysis of sedimentary environments.

[0013] Through the above analysis, the problems and defects of the prior art are as follows:

[0014] (1) The prior art methods mainly rely on laboratory analysis data, with low prediction accuracy for un-drilled areas, and are unable to effectively infer the impact of large-scale sedimentary environments on the formation of oil-rich coal. Due to the lack of full integration of sedimentary environment, stratigraphic structure, and paleoclimate factors, it is difficult for this method to accurately reveal the genetic mechanism of oil-rich coal, restricting its applicability in different geological units. The data acquisition cost is relatively high, the experimental process is time-consuming, and the coal sample analysis data is relatively discrete, making it difficult to be used for continuous oil-rich coal distribution modeling.

[0015] (2) The prior art methods are highly dependent on logging data, with low prediction accuracy in areas with insufficient drilling density, and are difficult to be effectively applied to the prediction of oil-rich coal in un-drilled areas. Due to the limited resolution of seismic data, it is difficult to identify fine-scale sedimentary environment characteristics, resulting in large uncertainties in the distribution prediction of oil-rich coal. Modern technical means such as machine learning and big data analysis are not fully utilized, resulting in poor adaptability of the prediction model to changes in geological conditions and unable to achieve high-precision quantitative analysis of sedimentary environments. Summary of the Invention

[0016] In view of the problems existing in the prior art, the present invention provides an oil-rich coal distribution prediction device and method for quantitative analysis of sedimentary environments in coal-bearing basins.

[0017] The present invention is implemented as follows. An oil-rich coal distribution prediction method for quantitative analysis of sedimentary environments in coal-bearing basins, the method comprising:

[0018] S1, data collection and digital modeling: Through collection of regional geological data and field investigations, obtain the rock layer thickness, coal seam distribution, sedimentary facies types, and paleo-environmental characteristics of the coal-bearing basin, perform standardization and digital processing on the data, and import it into a geographic information system or geological modeling software to form a unified geological data set;

[0019] S2, construction of a three-dimensional sedimentary environment model: Based on geological parameters such as sediment particle size, sedimentation rate, and organic matter content, use frequency analysis and spectral analysis to identify the periodic changes in sedimentation rate, interpolate the parameters in three-dimensional space through interpolation techniques, and combine numerical modeling methods to simulate the evolution process of the sedimentary environment to obtain a three-dimensional sedimentary environment model;

[0020] S3, multivariate statistics and machine learning classification prediction: Combine the organic geochemical indicators of coal seam samples with the parameters of the three-dimensional sedimentary environment model, screen the main control factors related to the occurrence of oil-rich coal through multivariate statistical analysis, and use machine learning classification algorithms to classify or predict the distribution of coal seams;

[0021] S4, Quantitative coupling of sedimentary environment and distribution of oil-rich coal: Based on the three-dimensional model of the sedimentary environment and the results of machine learning classification, a multiple regression model or a numerical simulation model is constructed. Through time series trend analysis and identification of geological event layers, the influence of sedimentary environment factors on the generation and distribution of oil-rich coal is quantitatively evaluated, and the occurrence areas and enrichment degrees of oil-rich coal are marked in three-dimensional space.

[0022] Furthermore, the specific content of S1 includes: collecting and collating geological background materials such as cores, drilling logs, etc. in this area and data such as coal quality tests, and systematically and hermetically collecting typical oil-rich coal samples for coal petrology and coal quality characteristic analysis; dividing the stratigraphic sequence through aspects such as lithology, lithofacies change, stratigraphic development law and its superimposed pattern, observing and studying the burial depth, distribution range and shape of coal seams, and counting the cumulative thickness of the development of oil-rich coal in this area.

[0023] Furthermore, the specific content of S2 includes: carrying out tests on microscopic coal petrographic components and determination of vitrinite reflectance; calculating the tar yield and vitrinite-inertinite ratio using the test results of microscopic coal petrographic components, and making phase diagrams of the relationship between the content of active components such as vitrinite + exinite and tar yield; quantitatively calculating parameters such as gelification index GI, plant preservation index TPI, groundwater flow index GWI, and vegetation index VI using each coal petrographic microscopic component measured by low-temperature carbonization experiments, and combining X-ray fluorescence spectrometry to determine the mineral composition in coal and judge the combination form.

[0024] Furthermore, the specific content of S3 includes: determining the spatial distribution position and characteristics of the development of oil-rich coal according to the cumulative thickness map of coal seams and the comprehensive columnar section in this area; characterizing the paleosalinity, paleoclimate, and redox environment during the coal-forming period by using element ratios such as Sr / Ba, Sr / Cu, V / (V+Ni), etc.; in addition, using an X-ray diffractometer to analyze minerals in coal such as clays, carbonates, sulfides, and silica, etc., and estimating its coal resource amount.

[0025] Furthermore, the specific content of S4 includes: restoring the groundwater level, plant preservation and degradation degree, water-covering depth, type and preservation degree of coal-forming plants during the coal-forming period through the GI, TPI, GWI, and VI data calculated from the contents of each microscopic coal petrographic component; analyzing the control effect of the synergistic relationship between the sedimentary environment and other geological factors on oil-rich coal, and revealing the genesis of the development of oil-rich coal and its formation mechanism under the sedimentary environment.

[0026] Furthermore, the gelification index GI is the ratio of gelified components to non-gelified components, showing the characteristics of being higher in humid environments and lower in dry environments; the plant preservation index TPI is the ratio of structured microscopic components to unstructured microscopic components, which can measure the degree of humification and the change of the pH value of swamp water bodies, and can also finely divide swamp types through the relationship between TPI and GI;

[0027] The groundwater flow index GWI is calculated based on the strength of gelation experienced by macerals and mineral content, and characterizes the control degree of groundwater on peat bogs and the water-bearing depth. Generally, a high water level environment indicates a higher degree of maceral degradation and a deeper water-covering degree, while a low water level environment is the opposite;

[0028] The vegetation index VI is the ratio of the components retaining cell structure to the matrix, granular components and detrital materials, reflecting the type and preservation degree of coal-forming plants. Based on the plant type, the swamp types can be further divided through the cross-relationship between GWI and VI;

[0029] Furthermore, in S3, the ratios of elements such as Sr / Ba, Sr / Cu, V / (V+Ni) are studied to characterize the paleosalinity, paleoclimate, and redox environment during the coal-forming period, specifically including:

[0030] Element Sr is an important element reflecting the paleoenvironment during the coal-forming period, with a higher content in arid and hot environments and a lower content in humid environments. Therefore, Sr / Ba is used to characterize the paleosalinity, and Sr / Cu is used to indicate the paleoclimate; since elements such as V, Ni, Cr, Co are relatively chemically active, multiple element ratios such as V / (V+Ni), V / Cr, Ni / Co are used to judge the paleoclimate, paleosalinity, and redox characteristics during the coal-forming period; the development of oil-rich coal is comprehensively considered based on quantitative indicators in multiple dimensions such as the characteristics of microscopic coal petrographic components, coal facies conditions, and element geochemistry characteristics.

[0031] Another object of the present invention is to provide a prediction device for the distribution of oil-rich coal in the quantitative analysis of the sedimentary environment of coal-bearing basins, including:

[0032] The investigation and basic geological analysis module collects and collates geological background materials such as cores, drilling and logging in this area and data such as coal quality testing, and systematically and hermetically collects typical oil-rich coal samples for the analysis of coal petrographic and coal quality characteristics; the stratigraphic sequence is divided through aspects such as lithology, lithofacies change, stratigraphic development law and its superimposed pattern, observes and studies the burial depth, distribution range and morphology of coal seams, and statistically calculates the cumulative thickness of the development of oil-rich coal in this area;

[0033] The quantitative research module is connected to the investigation and basic geological analysis module, and conducts tests on microscopic coal petrographic components and determination of vitrinite reflectance; calculates the tar yield, vitrinite-inertinite ratio, and makes a phase diagram of the relationship between the content of active components such as vitrinite + exinite and the tar yield using the test results of microscopic coal petrographic components; quantitatively calculates parameters such as the gelification index GI, plant preservation index TPI, groundwater flow index GWI, vegetation index VI, etc. using each microscopic coal petrographic component measured by low-temperature carbonization experiments, and combines X-ray fluorescence spectrometry to determine the mineral composition in coal and judge the combination form;

[0034] The module for studying the occurrence law of oil-rich coal is connected to the quantitative research module. According to the cumulative coal seam thickness map and comprehensive columnar section of this area, it determines the spatial distribution position and characteristics of the development of oil-rich coal. By studying the element ratios such as Sr / Ba, Sr / Cu, V / (V+Ni), etc., it characterizes the paleosalinity, paleoclimate, and redox environment during the coal-forming period. In addition, it uses an X-ray diffractometer to analyze the minerals in coal, such as clay, carbonate, sulfide, and silica, etc., and estimates the coal resource volume.

[0035] The module for studying the formation mechanism of oil-rich coal is connected to the module for studying the occurrence law of oil-rich coal. Through the GI, TPI, GWI, and VI data calculated from the contents of each maceral component, it restores the groundwater level, the degree of plant preservation and degradation, the water-covering depth, and the type and preservation degree of coal-forming plants during the coal-forming period. It analyzes the control effect of the synergetic relationship between the sedimentary environment and other geological factors on oil-rich coal, and reveals the origin of the development of oil-rich coal and its formation mechanism under the sedimentary environment.

[0036] Another object of the present invention is to provide a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for predicting the distribution of oil-rich coal by quantitative analysis of the sedimentary environment of the coal-bearing basin.

[0037] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for predicting the distribution of oil-rich coal by quantitative analysis of the sedimentary environment of the coal-bearing basin.

[0038] Combined with the above technical solutions and the solved technical problems, the advantages and positive effects of the technical solution to be protected by the present invention are as follows:

[0039] Aiming at the development law of the coal seam of the Xishanyao Formation in Santanghu, the present invention selects typical oil-rich coal samples for collection, conducts experiments and quantitatively studies the sedimentary environment and sedimentary system of the Xishanyao Formation. By systematically analyzing the relationship between the sedimentary environment and oil-rich coal, it reveals the occurrence law of oil-rich coal, and finally forms the technology of "prediction of oil-rich coal distribution based on quantitative analysis of the sedimentary environment of the coal-bearing basin". This method provides a new technical means for the accurate prediction of oil-rich coal and resource development.

[0040] In previous studies, there was a lack of systematic quantitative analysis of the "relationship between the sedimentary environment of the coal-bearing basin and the formation of oil-rich coal". The present invention breaks through the limitations of traditional methods, starts from the quantification of the sedimentary environment, and studies the spatial development and distribution law of oil-rich coal. By revealing the control mechanism of the sedimentary environment of the Santanghu coal-bearing basin on the formation of oil-rich coal, it provides a scientific basis and guidance for the further exploration and development of coal and oil and gas resources.

[0041] The traditional prediction of the distribution of rich oil-bearing coal mainly relies on geological experience and limited exploration data, resulting in large prediction errors due to the complexity of the sedimentary environment. Through the establishment of a detailed quantitative model of the sedimentary environment and in-depth research on the occurrence law of rich oil-bearing coal, this invention comprehensively analyzes the key factors affecting the formation of rich oil-bearing coal. The new method significantly improves the prediction accuracy, reduces the exploration risk and resource development cost, and provides a strong guarantee for the exploration efficiency.

[0042] Most of the traditional sedimentary environment analysis methods are qualitative descriptions, lacking the support of systematic and quantitative research. By constructing a quantitative index system for the sedimentary environment, this invention realizes the accurate quantitative analysis of key parameters such as sedimentary facies, sedimentation rate, and paleo-water depth. The quantitative research not only provides a scientific basis for the prediction of the distribution of rich oil-bearing coal, but also breaks through the limitations of traditional reliance on subjective judgment, promoting the development of sedimentary environment research towards a more scientific and systematic direction.

[0043] In the existing technology, the research on the occurrence law of rich oil-bearing coal mostly stays at the macroscopic level, lacking in-depth analysis of the relationship between the sedimentary environment and the distribution of rich oil-bearing coal. By establishing a quantitative model of the occurrence law of rich oil-bearing coal, this invention deeply reveals the distribution characteristics of rich oil-bearing coal under different sedimentary environments and clarifies the key sedimentary conditions controlling the formation of rich oil-bearing coal. This research formulates a more targeted development strategy for the resource exploration of different types of coal-bearing basins. The existing prediction models of rich oil-bearing coal often lack scientific verification and have poor reliability. By combining the sedimentary environment model and the formation mechanism model of rich oil-bearing coal, this invention develops a set of verifiable prediction methods for the distribution of rich oil-bearing coal. Using multi-source geological data and advanced data processing technologies for model construction and numerical simulation, it ensures the scientificity and reliability of the prediction model. This model is applicable to different types of coal-bearing basins, provides efficient and reliable technical support for actual exploration, and has wide application value in energy resource development. Description of the Drawings

[0044] Figure 1 is the flow chart of the prediction method for the distribution of rich oil-bearing coal with quantitative analysis of the sedimentary environment in a coal-bearing basin provided by an embodiment of the present invention;

[0045] Figure 2 is the structural diagram of the device for predicting the distribution of rich oil-bearing coal with quantitative analysis of the sedimentary environment in a coal-bearing basin provided by an embodiment of the present invention.

[0046] Figure 2 In it: 1. Research and basic geological analysis module; 2. Quantitative research module; 3. Research module on the occurrence law of rich oil-bearing coal; 4. Research module on the formation mechanism of rich oil-bearing coal. Detailed Embodiments

[0047] To make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] As Figure 1 shown, an oil-rich coal distribution prediction method for quantitative analysis of sedimentary environments in coal-bearing basins provided by an embodiment of the present invention includes:

[0049] S1, data collection and digital modeling: Through regional geological data collection and field research, obtain the rock layer thickness, coal seam distribution, sedimentary facies types and paleoenvironmental characteristics of the coal-bearing basin, perform standardization and digital processing on the data, and import it into a geographic information system or geological modeling software to form a unified geological data set;

[0050] S2, construction of a three-dimensional sedimentary environment model: Based on geological parameters such as sediment particle size, sedimentation rate, and organic matter content, use frequency analysis and spectral analysis to identify the periodic changes in the sedimentation rate, interpolate the parameters in three-dimensional space through interpolation technology, and combine numerical modeling methods to simulate the evolution process of the sedimentary environment to obtain a three-dimensional sedimentary environment model;

[0051] S3, multivariate statistics and machine learning classification prediction: Combine the organic geochemical indicators of coal seam samples with the parameters of the three-dimensional sedimentary environment model, screen the main control factors related to the occurrence of oil-rich coal through multivariate statistical analysis, and use machine learning classification algorithms to classify or predict the distribution of coal seams;

[0052] S4, quantitative coupling of sedimentary environment and oil-rich coal distribution: Based on the three-dimensional sedimentary environment model and the machine learning classification results, construct a multiple regression model or numerical simulation model, and through time series trend analysis and identification of geological event layers, quantitatively evaluate the influence of sedimentary environment factors on the generation and distribution of oil-rich coal, and mark the occurrence areas and enrichment degrees of oil-rich coal in three-dimensional space.

[0053] In step S1, it is necessary to widely collect regional geological data of the coal-bearing basin and conduct field research to obtain key geological information such as rock layer thickness, coal seam distribution, sedimentary facies types and paleoenvironmental characteristics. To ensure the availability and consistency of the data, the data from different sources should be standardized and digitally processed: for example, converting the rock layer thickness measurement point data into geographic information under a unified coordinate system, and vectorizing the description of the coal seam distribution and sedimentary facies boundary. These data are then imported into a geographic information system (GIS) or geological modeling software to form a "unified geological data set". This process lays the foundation for subsequent three-dimensional sedimentary environment modeling and oil-rich coal distribution prediction.

[0054] In step S2, by performing frequency analysis and spectral analysis on geological parameters such as sediment particle size, sedimentation rate, and organic matter content, the periodic changes in the sedimentation rate and the distribution characteristics of sediment grain size among different stratigraphic units can be identified. Subsequently, interpolation techniques (such as Kriging method, inverse distance weighting, etc.) are used to interpolate these parameters in three-dimensional space to obtain the continuous distribution of various indicators in the sedimentary environment. Numerical modeling methods (such as finite element or finite difference) can then combine this spatial distribution information with geomechanics or stratigraphic sedimentation dynamics to further simulate the evolution process of the sedimentary environment, thereby reproducing the coupling relationship between coal seam occurrence and sedimentary conditions in three-dimensional space.

[0055] In step S3, to more accurately characterize the formation mechanism of oil-rich coal, it is necessary to combine the organic geochemical characteristics of coal seam samples, such as total organic carbon content (TOC), hydrogen index (HI), pyrolysis product distribution, etc. Through multivariate statistical analysis (such as principal component analysis PCA or factor analysis FA), the control factors most relevant to the occurrence of oil-rich coal can be screened out. Subsequently, machine learning classification algorithms (such as support vector machine SVM, random forest RF, or neural network NN) can be used to classify the coal seams and predict their distribution. Specifically, by inputting the spatial model parameters of the aforementioned sedimentary environment and the geochemical indicators of coal seam samples into the machine learning model, a set of classification rules or probability distributions for identifying the distribution of oil-rich coal can be obtained after training.

[0056] In step S4, based on the three-dimensional model of the sedimentary environment and the machine learning classification results obtained previously, a multiple regression model or numerical simulation model of the sedimentary environment on the distribution of oil-rich coal can be constructed. This model combines time series trend analysis with the identification of geological event layers to quantitatively evaluate the impact of changes in sedimentary environment factors (such as water depth, provenance supply, sedimentary energy) on the generation and distribution of oil-rich coal. For example, by adding time series variables of sedimentation rate and paleoenvironmental changes to the regression model, the contribution of specific geological events (such as marine transgression, climate change, etc.) to the formation period of oil-rich coal can be discovered. Finally, this quantitative analysis can mark the possible areas and enrichment degrees of oil-rich coal occurrence in three-dimensional space, providing a more scientific decision-making basis for oil and gas exploration and the comprehensive utilization of coal resources.

[0057] In step S2, to obtain the three-dimensional model of the sedimentary environment, it is necessary to combine parameters such as sediment particle size (d), sedimentation rate (r), and organic matter content (c) for interpolation and numerical simulation of the spatial distribution. Geostatistical methods such as the Kriging method or inverse distance weighting (IDW) can be selected to interpolate each layer to obtain the corresponding (d, r, c) values on the spatial grid. Taking the Kriging method as an example, its mathematical model is usually expressed as:

[0058]

[0059] Among them, is the predicted value of the target point xo, λ i are the interpolation weight coefficients to be determined, satisfying ∑λ i = 1. After interpolation, the result is input into the numerical simulation equation of the sedimentary environment (such as sedimentation kinetics equation, diffusion equation or Darcy flow equation) to simulate the evolution process of the sedimentary environment in three-dimensional space. This process can usually be expressed as a set of partial differential equations:

[0060]

[0061] Among them, φ can represent sediment concentration or organic matter concentration, v is the sedimentation rate vector, and S is the source-sink term (such as new material deposition or loss), so as to dynamically analyze the enrichment or dilution process of organic matter in the sedimentary environment.

[0062] In step S3, it is necessary to comprehensively conduct multivariate statistical analysis and machine learning modeling on the organic geochemical characteristics of coal seam samples (such as total organic carbon TOC, hydrogen index H1, vitrinite reflectance, etc.) and the aforementioned three-dimensional model data of the sedimentary environment, so as to extract the control factors for the occurrence of oil-rich coal and predict the distribution. Mathematically, principal component analysis (PCA) or factor analysis (FA) can be performed on each variable first to extract a few principal components / factors to represent the main changes in the sedimentary environment and the characteristics of coal seam organic matter:

[0063] X = P·T + E

[0064] Among them, X is the original data matrix, P is the loading matrix, T is the score matrix, and is the residual matrix. Then, based on these principal components, machine learning classification algorithms such as support vector machine (SVM) or random forest (RF) can be used to establish an oil-rich coal distribution prediction model:

[0065] y = RF(d, r, c, TOC, HI,...)

[0066] Among them, y represents the occurrence category or probability value of oil-rich coal. Through the verification of the training set and the test set, an oil-rich coal distribution prediction model with high accuracy can be finally obtained.

[0067] In step S4, by combining the results of the three-dimensional model of the sedimentary environment with the results of machine learning classification prediction, a multiple regression model or a numerical simulation model is constructed to quantitatively analyze the influence of sedimentary conditions on the generation and distribution of oil-rich coal. The multiple regression model can be written as:

[0068]

[0069] Among them, can represent the thickness, oil content rate or distribution probability of oil-rich coal, β iIt is the regression coefficient. At the same time, combining time series trend analysis (such as ARIMA or wavelet transform) with the identification of geological event layers (such as sea, tectonic movement, climate change, etc.) can capture the impact of different sedimentation periods on coal seam evolution. By iteratively updating the regression model or numerical simulation, the role of sedimentary environment factors (grain size, sedimentation rate, organic matter supply) in the formation of oil-rich coal is further quantified, so as to give the potential distribution range of oil-rich coal in three-dimensional space, providing data support and decision-making reference for subsequent oil and gas exploration and comprehensive utilization of coal resources.

[0070] In the S1 stage, the geological data of coal-bearing basins collected usually contain information from different sources and at different scales, such as rock layer thickness, coal seam distribution, sedimentary facies types, etc. To ensure data consistency, standardization processing is required, including unit conversion, coordinate system unification, noise filtering, and missing value filling. At the same time, spatial analysis is carried out through geographic information system (GIS) tools to ensure that the data can be seamlessly docked with subsequent three-dimensional modeling.

[0071] In the S2 stage, geological parameters such as sediment particle size distribution, sedimentation rate, and organic matter content need to be signal-processed to reveal the changing rules of the sedimentary environment. The specific methods include:

[0072] Frequency analysis: Using fast Fourier transform (FFT) or wavelet transform to identify sedimentary cycle characteristics;

[0073] Spectral analysis: Calculating the power spectral density (PSD) to evaluate the periodic changes between formation thickness and sedimentation rate;

[0074] Interpolation technique: Using Kriging interpolation or inverse distance weighting (IDW) method to generate a three-dimensional sedimentary environment distribution map;

[0075] Numerical modeling: Constructing a sedimentary environment prediction model based on multiple regression or finite element method to quantify the relationship between sedimentation rate and environmental factors.

[0076] In the S3 stage, the organic geochemical data of coal seam samples (such as hydrogen index, oxygen index, total organic carbon content TOC, etc.) can be used for machine learning modeling after data cleaning. The key steps include:

[0077] Feature engineering: Using principal component analysis (PCA) for dimensionality reduction and screening the variables most relevant to the occurrence of oil-rich coal;

[0078] Classification prediction: Using random forest, support vector machine (SVM), or XGBoost model to predict the distribution probability of oil-rich coal under different sedimentary environments;

[0079] Factor extraction: Explain the model decision-making process through the SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) model, and extract the key geological factors controlling the distribution of rich oil coal.

[0080] In the S4 stage, in order to quantify the impact of sedimentary conditions on the formation of rich oil coal, it is necessary to combine time series analysis and geological event layer identification:

[0081] Time series trend analysis: Use ARIMA (Autoregressive Integrated Moving Average) or LSTM (Long Short-Term Memory Network) to predict the evolution trend of the sedimentary environment;

[0082] Event layer detection: Based on change point detection algorithms (such as CUSUM, Pruned Exact Linear Time), identify key geological events such as volcanic eruptions, marine transgressions, and climate mutations, and analyze their impact on the formation of rich oil coal;

[0083] Quantitative regression analysis: Establish a multiple regression model between sedimentary environment parameters and the occurrence of rich oil coal, calculate the distribution area of rich oil coal under different sedimentary conditions, and verify it using numerical simulation.

[0084] The above methods combine geological data processing, signal analysis, machine learning, and time series analysis to achieve quantitative prediction of the sedimentary environment and the distribution of rich oil coal, providing data support for coalfield exploration.

[0085] The method for predicting the distribution of rich oil coal by quantitative analysis of the sedimentary environment in a coal-bearing basin provided by the embodiments of the present invention aims to accurately predict the distribution of rich oil coal through multi-step signal and data processing. This method reveals the control mechanism of the sedimentary environment on the formation of rich oil coal through the collection, analysis, and model construction of geological data, thereby providing a scientific basis for energy exploration.

[0086] First, in the research and basic geological analysis stage (S1), researchers conduct a detailed geological survey of the target coal-bearing basin and collect relevant geological data, including rock layer structure, coal seam thickness, paleoclimate conditions, etc. These data serve as the basic information for subsequent analysis. After preliminary processing, the geological data is archived and classified to ensure the integrity and availability of the data.

[0087] Next, in the quantitative research stage (S2) of the coal-bearing basin sedimentary environment, researchers conduct further quantitative analysis on the collected geological data. By establishing a quantitative index system for the sedimentary environment, parameters such as sedimentary facies, sedimentation rate, and paleo-water depth in the basin are accurately calculated and analyzed. Using a variety of data processing tools, such as geological modeling software and statistical analysis software, various indicators of the sedimentary environment are quantitatively evaluated, thus obtaining a quantitative sedimentary environment model.

[0088] Then, entering the research stage of the occurrence law of rich oil coal (S3), researchers use the sedimentary environment model obtained from the previous quantitative analysis, combined with the known distribution data of rich oil coal, to study the distribution law of rich oil coal under different sedimentary environments. Through comparative analysis, key sedimentary conditions controlling the formation of rich oil coal are identified, such as organic matter abundance, reducing environment, sediment source, etc. During the data processing, spatial analysis and regression analysis methods are used to construct a quantitative relationship model for the occurrence law of rich oil coal.

[0089] Finally, in the research stage of the formation mechanism of rich oil coal controlled by the sedimentary environment (S4), through in-depth analysis of the previous models, how the sedimentary environment affects the generation and distribution of rich oil coal is studied. The specific influence mechanisms of factors such as temperature, pressure, and geological structure in the sedimentary environment on the generation of rich oil coal are mainly analyzed, and numerical simulation and verification are carried out. Finally, through multiple iterations and model optimization, a set of quantitative models capable of effectively predicting the distribution of rich oil coal are established, providing a reliable prediction basis for actual exploration.

[0090] This method effectively improves the accuracy of predicting the distribution of rich oil coal through a scientific signal and data processing process, providing important technical support for energy exploration and development.

[0091] The specific content of S1 includes: collecting and sorting out geological background materials such as cores, drilling logs, etc. in this area and data such as coal quality tests, and systematically and hermetically collecting typical rich oil coal samples for coal petrology and coal quality characteristic analysis; dividing the stratigraphic sequence through aspects such as lithology, lithofacies change, stratigraphic development law and its superposition pattern, observing and studying the burial depth, distribution range and shape of coal seams, and statistically calculating the cumulative thickness of rich oil coal development in this area.

[0092] The specific content of S2 includes: carrying out tests on microscopic coal petrographic components and determination of vitrinite reflectance; calculating the tar yield and vitrinite-inertinite ratio using the test results of microscopic coal petrographic components, and making a phase diagram of the relationship between the content of active components, such as vitrinite + exinite and tar yield; quantitatively calculating parameters such as gelification index GI, plant preservation index TPI, groundwater flow index GWI, and vegetation index VI using each coal petrographic microscopic component measured by low-temperature carbonization experiments, and combining X-ray fluorescence spectrometry to determine the mineral composition in coal and judge the combination form.

[0093] The specific steps of S3 include: determining the spatial distribution position and characteristics of rich oil coal development according to the cumulative thickness map and comprehensive columnar section of the coal seam in this area; characterizing the paleosalinity, paleoclimate, and redox environment during the coal-forming period by studying the ratios of elements such as Sr / Ba, Sr / Cu, and V / (V+Ni); in addition, using an X-ray diffractometer to analyze the minerals in coal, such as clay, carbonate, sulfide, and silica, and estimating the coal resource volume.

[0094] The specific steps of S4 include: restoring the groundwater level, plant preservation and degradation degree, water-covering depth, and type and preservation degree of coal-forming plants during the coal-forming period through the GI, TPI, GWI, and VI data calculated from the contents of each maceral component; analyzing the control effect of the synergetic relationship between the sedimentary environment and other geological factors on rich oil coal, and revealing the origin of rich oil coal development and its formation mechanism under the sedimentary environment.

[0095] The gelification index GI is the ratio of the gelified component to the non-gelified component, showing higher values in humid environments and lower values in dry environments; the plant preservation index TPI is the ratio of the structured maceral to the unstructured maceral, which can measure the degree of humification and the change of the pH value of the swamp water body, and can also be used to finely divide the swamp type through the relationship between TPI and GI;

[0096] The groundwater flow index GWI is calculated based on the strength of the gelation effect experienced by macerals and the mineral content, characterizing the control degree of groundwater on the peat swamp and the water-bearing depth. A high water level environment generally indicates a higher degree of maceral degradation and a deeper water-covering degree, while a low water level environment is the opposite;

[0097] The vegetation index VI is the ratio of the component with preserved cell structure to the matrix, granular component, and clastic material, reflecting the type and preservation degree of coal-forming plants, and further dividing the swamp type based on the plant type through the cross relationship between GWI and VI;

[0098] In S3, characterizing the paleosalinity, paleoclimate, and redox environment during the coal-forming period by studying the ratios of elements such as Sr / Ba, Sr / Cu, and V / (V+Ni) specifically includes:

[0099] Element Sr is an important element reflecting the paleoenvironment during the coal-forming period, with higher contents in arid and hot environments and lower contents in humid environments. Therefore, Sr / Ba is used to characterize the paleosalinity, and Sr / Cu is used to indicate the paleoclimate; since elements such as V, Ni, Cr, and Co are relatively chemically active, multiple element ratios such as V / (V+Ni), V / Cr, and Ni / Co are used to judge the paleoclimate, paleosalinity, and redox characteristics during the coal-forming period; comprehensively considering the development of rich oil coal based on quantitative indicators from multiple dimensions such as maceral component characteristics, coal facies conditions, and element geochemical characteristics.

[0100] As Figure 2 shown, an embodiment of the present invention provides an oil-rich coal distribution prediction device for quantitative analysis of sedimentary environments in coal-bearing basins based on a prediction method. The device specifically includes:

[0101] A research and basic geological analysis module 1 collects and collates geological background materials such as cores, drilling logs, etc. in this area and data such as coal quality tests, and systematically and hermetically collects typical oil-rich coal samples for coal petrographic and coal quality characteristics analysis; conducts stratigraphic sequence division through aspects such as lithology, lithofacies changes, stratigraphic development laws, and their superimposed patterns, observes and studies the burial depth, distribution range, and morphology of coal seams, and statistically calculates the cumulative thickness of oil-rich coal development in this area;

[0102] A quantitative research module 2 is connected to the research and basic geological analysis module 1 to carry out tests on microscopic coal petrographic components and determination of vitrinite reflectance; calculates the tar yield, vitrinite-inertinite ratio, and active component content using the test results of microscopic coal petrographic components, such as the phase diagram of the relationship between vitrinite + exinite and tar yield; quantitatively calculates parameters such as the gelification index GI, plant preservation index TPI, groundwater flow index GWI, and vegetation index VI using each coal petrographic microscopic component measured by low-temperature carbonization experiments, and combines X-ray fluorescence spectrometry to determine the mineral composition in coal and judge the combination form;

[0103] An oil-rich coal occurrence law research module 3 is connected to the quantitative research module 2. According to the cumulative thickness map of coal seams and the comprehensive columnar section in this area, determines the spatial distribution position and characteristics of oil-rich coal development; characterizes the paleosalinity, paleoclimate, and redox environment during coal formation by studying element ratios such as Sr / Ba, Sr / Cu, V / (V+Ni), etc.; in addition, analyzes minerals in coal such as clay, carbonate, sulfide, and silica using an X-ray diffractometer, and estimates its coal resources.

[0104] An oil-rich coal formation mechanism research module 4 is connected to the oil-rich coal occurrence law research module 3. Through the GI, TPI, GWI, and VI data calculated from the content of each microscopic coal petrographic component, restores the groundwater level, plant preservation and degradation degree, water-covering depth, and type and preservation degree of coal-forming plants during coal formation; analyzes the control effect of the synergistic relationship between sedimentary environments and other geological factors on oil-rich coal, and reveals the origin of oil-rich coal development and its formation mechanism under sedimentary environments.

[0105] An embodiment of the present invention provides a computer device. The computer device includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the oil-rich coal distribution prediction method for quantitative analysis of sedimentary environments in coal-bearing basins.

[0106] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the steps of the method for predicting the distribution of oil-rich coal in the quantitative analysis of the sedimentary environment of a coal-bearing basin.

[0107] In the analysis of the sedimentary environment of a coal-bearing basin, the coupling effect among the tectonic pattern, sea-level rise and fall, and the prevailing paleo-wind direction has a key impact on the development of coal-bearing strata and the process of oil and gas enrichment. On the basis of retaining the effectiveness of the existing research methods for marine platforms, the present invention accurately depicts the evolution law of the sedimentary facies distribution and the provenance transport path in the basin by introducing a parametric model that is more adaptable to the tectonic characteristics of the basin. Especially in the multi-stage process of sea-level rise and fall, the present invention combines the paleo-wind direction quantification and reconstruction technology with the analysis of the diagenesis process, and systematically reveals the coupling mechanism between the transformation of sedimentary facies zones inside the basin and the oil-rich characteristics of coal-bearing strata from multiple dimensions such as sedimentary differentiation, lithological combination, and porosity change.

[0108] To better study the mechanism of sedimentary differentiation and reservoir densification in a coal-bearing basin, the present invention makes targeted expansions and improvements on the basis of the previous analysis of marine carbonate platforms. By adapting to the characteristics of terrigenous clastic supply and the hydrocarbon generation conditions in the transitional environment between land and sea, and using multi-dimensional geological, geochemical, and petrophysical data, the comprehensive influence of basin tectonics - sea level - paleo-wind direction is quantitatively described. With the collaborative research of magnetic susceptibility anisotropy and multiple geochemical indicators, the present invention can more comprehensively reveal the key factors and evolution process of the formation of oil-rich coal inside the coal-bearing basin, providing accurate theoretical support and technical demonstration for the exploration and development of coal-bearing oil and gas resources.

[0109] Example 1: Prediction of the distribution of oil-rich coal in a specific block of the Ordos Basin

[0110] 1) Data collection and preprocessing

[0111] Select a block in the Ordos Basin, collect existing drilling data, core analysis data, and seismic profile data, and focus on the thickness of coal-bearing strata, the distribution characteristics of coal seams, and the types of sedimentary facies. Use GIS tools to align the data spatially and normalize different logging data (such as acoustic travel time, density, gamma ray, etc.) to ensure data consistency.

[0112] 2) Sedimentary environment signal analysis and modeling

[0113] Use Fourier transform (FFT) to analyze the sedimentary cycle characteristics in the coal seam profile, and use spectral analysis methods to calculate the change law of sedimentation rate. Construct a three-dimensional sedimentary environment model of this block by Kriging interpolation method, and combine the sediment particle size and organic matter content data to identify high sedimentation rate and high organic matter enrichment areas, providing a basis for the prediction of oil-rich coal.

[0114] 3) Analysis of control factors for the occurrence of oil-rich coal

[0115] Collect multiple coal seam samples for organic geochemical tests to determine their hydrogen index (HI), total organic carbon (TOC) content, and pyrolysis parameter (Tmax). Through principal component analysis (PCA) and random forest models, extract key geological factors affecting the occurrence of oil-rich coal, such as sedimentation rate, shale content, and reduction environment index, and establish a prediction model.

[0116] 4) Prediction and verification of the distribution of oil-rich coal

[0117] Combined with time series analysis methods, use the changing trend of sedimentation rate to predict the generation probability of oil-rich coal in different sedimentary periods, and establish a multiple regression model to map sedimentary environment parameters to the oil-rich coal distribution map. Verify the prediction results through existing drill coal samples, and find that the coincidence rate between the high oil-rich coal areas predicted by the model and the analysis results of actual coal samples reaches more than 85%, providing an effective prediction tool for coalfield development.

[0118] Example 2: Prediction and genetic analysis of oil-rich coal in the eastern Yunnan fold belt

[0119] 1) Geological data collection and processing

[0120] Select a coalfield in the eastern Yunnan fold belt, and collect data on regional tectonic background, sedimentary facies types, paleoclimate environment, and coal seam thickness. Analyze the basin tectonic deformation characteristics through remote sensing images, and combine with field outcrop measurement data to establish a high-precision sedimentary geology database.

[0121] 2) Three-dimensional modeling of sedimentary environment

[0122] Adopt seismic attribute analysis technology, combine well logging data (acoustic, resistivity, density logging) and core analysis results, and use interpolation algorithms to construct a three-dimensional distribution model of sedimentary facies. Calculate the change of paleo-hydrodynamic force through numerical simulation methods, analyze the enrichment properties of organic matter under different sedimentary environments, and use sequence stratigraphy methods to divide sedimentary cycles.

[0123] 3) Analysis of the formation mechanism of oil-rich coal by machine learning

[0124] By collecting organic geochemical data of coal seam samples, use the K-means clustering algorithm to classify different types of coal seams, and combine with support vector machine (SVM) to establish an oil-rich coal distribution prediction model. Through feature importance analysis, it is found that sedimentation rate, redox conditions, and organic matter input are the main factors controlling the formation of oil-rich coal in this block.

[0125] 4) Verification and optimization of prediction results

[0126] Combined with time series analysis, identify the volcanic ash sedimentary layers in coal seams and analyze their influence on the formation of oil-rich coal. Establish a multiple regression equation for the sedimentary environment and the distribution of oil-rich coal, and compare the prediction results with the drilling sampling analysis data. It is found that the accuracy of the prediction model reaches more than 90%, providing accurate data support for the exploration and development of the coalfield in the eastern Yunnan fold belt.

[0127] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention by those skilled in the art within the technical scope disclosed by the present invention shall be covered by the protection scope of the present invention.

Claims

1. A method for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of a coal-bearing basin, characterized in that: The method includes: S1, Data collection and digital modeling: Through regional geological data collection and field investigation, the rock layer thickness, coal seam distribution, sedimentary phase type and paleoenvironmental characteristics of the coal-bearing basin are obtained, the data are standardized and digitized, and imported into the geographic information system or geological modeling software to form a unified geological data set; S2, construction of a three-dimensional model of the sedimentary environment: based on geological parameters such as sediment particle size, sedimentation rate and organic matter content, frequency analysis and spectrum analysis are used to identify the periodic changes in sedimentation rate. The parameters are interpolated in three-dimensional space through interpolation technology and combined with numerical modeling methods to simulate the evolution process of the sedimentary environment, thus obtaining a three-dimensional model of the sedimentary environment; S3, multivariate statistics and machine learning classification prediction: combining the organic geochemical indicators of coal seam samples with the parameters of the three-dimensional model of the sedimentary environment, screening the main control factors related to the occurrence of oil-rich coal through multivariate statistical analysis, and using machine learning classification algorithms to classify or predict the distribution of coal seams; S4, quantitative coupling of sedimentary environment and distribution of oil-rich coal: Based on the three-dimensional model of the sedimentary environment and the machine learning classification results, a multivariate regression model or a numerical simulation model is constructed. Through time series trend analysis and geological event layer identification, the impact of sedimentary environmental factors on the generation and distribution of oil-rich coal is quantitatively evaluated, and the occurrence area and enrichment degree of oil-rich coal are marked in three-dimensional space.

2. The method for predicting the distribution of oil-rich coal by quantitative analysis of the sedimentary environment of a coal-bearing basin according to claim 1, characterized in that: The S1 specifically includes: collecting and collating the core, drilling and logging geological background data and coal quality test data of the area, and systematically and sealedly collecting typical oil-rich coal samples for coal rock and coal quality characteristics analysis; dividing the stratigraphic sequence according to lithology, lithofacies changes, stratigraphic development laws and their superposition styles, observing and studying the burial depth, distribution range and morphology of coal seams, and counting the cumulative thickness of oil-rich coal development in the area.

3. The method for predicting the distribution of oil-rich coal by quantitative analysis of the sedimentary environment of a coal-bearing basin according to claim 1, characterized in that: The S2 specifically includes: conducting microscopic coal rock component testing and vitrinite reflectance determination; using the microscopic coal rock component test results to calculate the tar yield and vitrinite-inertia ratio, and to make an active component content, such as a phase diagram of the relationship between vitrinite + exinite and tar yield; using the microscopic components of each coal rock measured by the low-temperature retorting experiment to quantitatively calculate the gelation index GI, plant preservation index TPI, groundwater flow index GWI, and vegetation index VI parameters, and combining X-ray fluorescence spectrometry to determine the mineral composition in the coal and determine the combination form; In step S2, in order to obtain a three-dimensional model of the sedimentary environment, it is necessary to interpolate and numerically simulate the spatial distribution by combining the sediment particle size (d), sedimentation rate (r) and organic matter content (c) parameters; the Kriging geostatistical method is used to interpolate each layer to obtain the corresponding (d, r, c) values ​​on the spatial grid; for example, the Kriging method, its mathematical model is usually expressed as: in, is the predicted value of the target point xo, λ i is the interpolation weight coefficient to be determined, satisfying ∑λ i =1; after the interpolation is completed, the result is input into the numerical simulation equation of the sedimentary environment to simulate the evolution process of the sedimentary environment in three-dimensional space; the process is expressed as a set of partial differential equations: Among them, φ can represent the sediment concentration or organic matter concentration, v is the sedimentation rate vector, and S is the source and sink term.

4. The method for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of a coal-bearing basin according to claim 1, characterized in that: The S3 specifically includes: determining the spatial distribution location and characteristics of the development of oil-rich coal based on the cumulative thickness map and comprehensive columnar map of the coal seams in the area; characterizing the paleo-salinity, paleo-climate, and redox environment during the coal-forming period by studying the Sr / Ba, Sr / Cu, and V / (V+Ni) element ratios; in addition, analyzing the minerals in the coal using an X-ray diffractometer and estimating the amount of coal resources; In step S3, the organic geochemical characteristics of the coal seam samples and the aforementioned three-dimensional model data of the sedimentary environment are integrated to perform multivariate statistical analysis and machine learning modeling to extract the control factors of the occurrence of oil-rich coal and predict the distribution; mathematically, principal component analysis or factor analysis is first performed on each variable to extract a few principal components / factors to represent the main changes in the sedimentary environment and coal seam organic matter characteristics: X=P·T+E Among them, X is the original data matrix, P is the loading matrix, T is the score matrix, and is the residual matrix; then based on these principal components, a machine learning classification algorithm such as support vector machine (SVM) or random forest (RF) can be used to establish an oil-rich coal distribution prediction model: y=RF(d, r, c, TOC, HI,...) Where y represents the occurrence category or probability value of oil-rich coal.

5. The method for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of a coal-bearing basin according to claim 1, characterized in that: The S4 specifically includes: restoring the groundwater level, plant preservation and degradation degree, water cover depth, type and preservation degree of coal-forming plants during the coal-forming period through the GI, TPI, GWI and VI data calculated from the content of each microscopic coal rock component; analyzing the control effect of the synergistic relationship between the sedimentary environment and other geological factors on the oil-rich coal, revealing the cause of the development of oil-rich coal and its formation mechanism in the sedimentary environment; In step S4, by combining the results of the three-dimensional model of the sedimentary environment with the machine learning classification prediction results, a multivariate regression model or a numerical simulation model is constructed to quantitatively analyze the impact of sedimentary conditions on the generation and distribution of oil-rich coal; the multivariate regression model can be written as: in, It can represent the thickness, oil content or distribution probability of oil-rich coal, β i is the regression coefficient.

6. The method for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of a coal-bearing basin according to claim 1, characterized in that: The gelation index GI is the ratio of gelation components to non-gelation components, which is higher in a humid environment and lower in a dry environment; the plant preservation index TPI is the ratio of structured microscopic components to non-structured microscopic components, which can measure the degree of humification and the change in the pH value of the swamp water body, and can also be used to finely divide the swamp type through the relationship between TPI and GI; The groundwater flow index GWI is calculated based on the strength of the gelation of the microscopic components and the mineral content, and characterizes the degree of control of groundwater on the peat bog and the depth of water supply. A high water level environment generally indicates a higher degree of degradation of the microscopic components and a deeper degree of water coverage, while a low water level environment is the opposite. The vegetation index VI is the ratio of components retaining cellular structure to matrix, granular components and debris, reflecting the type of coal-forming plants and the degree of preservation. Based on the plant type, the swamp type can be further divided through the cross-relationship between GWI and VI.

7. The method for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of a coal-bearing basin according to claim 1, characterized in that: In the S3, the paleo-salinity, paleo-climate and redox environment during the coal-forming period are characterized by studying the Sr / Ba, Sr / Cu and V / (V+Ni) element ratios, including: Sr is an important element that reflects the paleoenvironment during the coal-forming period. Its content is higher in arid and hot environments and lower in humid environments. Therefore, Sr / Ba is used to characterize paleosalinity, and Sr / Cu is used to indicate paleoclimate. Since the chemical properties of V, Ni, Cr, and Co are relatively active, V / (V+Ni), V / Cr, and Ni / Co are used to determine the paleoclimate, paleosalinity, and redox characteristics of the coal-forming period. The development of oil-rich coal is comprehensively considered based on quantitative indicators in multiple dimensions, including microscopic coal rock component characteristics, coal phase conditions, and elemental geochemical characteristics.

8. A device for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of a coal-bearing basin based on the prediction method according to claims 1-7, characterized in that: The device specifically comprises: The survey and basic geological analysis module collects and organizes the core, drilling and logging geological background data and coal quality test data of the area, and systematically and sealedly collects typical oil-rich coal samples for coal rock and coal quality characteristics analysis; divides the stratigraphic sequence according to the lithology, lithofacies changes, stratigraphic development laws and superposition styles, observes and studies the burial depth, distribution range and morphology of coal seams, and counts the cumulative thickness of oil-rich coal development in the area; The quantitative research module is connected with the survey and basic geological analysis module to carry out microscopic coal rock component testing and vitrinite reflectance determination; the tar yield and vitrinite-inertia ratio are calculated using the microscopic coal rock component test results, and the active component content, such as the phase diagram of the relationship between vitrinite + exinite and tar yield, is made; the gelation index GI, plant preservation index TPI, groundwater flow index GWI, and vegetation index VI parameters are quantitatively calculated using the microscopic components of each coal rock measured by the low-temperature retorting experiment, and the mineral composition in the coal is determined and the combination form is judged by combining the X-ray fluorescence spectrometry method; The oil-rich coal occurrence law research module is connected to the quantitative research module. According to the cumulative thickness map of coal seams and the comprehensive column chart in the area, the spatial distribution position and characteristics of the oil-rich coal development are determined; the paleo-salinity, paleo-climate and redox environment during the coal-forming period are characterized by studying the Sr / Ba, Sr / Cu and V / (V+Ni) element ratios; in addition, the minerals in the coal are analyzed by X-ray diffractometer, and the coal resources are estimated. The module on the formation mechanism of oil-rich coal is connected to the module on the occurrence law of oil-rich coal. Through the GI, TPI, GWI and VI data calculated from the contents of each microscopic coal rock component, the module can restore the groundwater level, the degree of plant preservation and degradation, the depth of water coverage, and the type and degree of preservation of coal-forming plants during the coal-forming period. The module also analyzes the control of the synergistic relationship between the sedimentary environment and other geological factors on the oil-rich coal, and reveals the causes of the development of oil-rich coal and its formation mechanism in the sedimentary environment.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of a coal-bearing basin as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the method for predicting the distribution of oil-rich coal by quantitatively analyzing the sedimentary environment of a coal-bearing basin as described in any one of claims 1 to 7.

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