Evaluation method, system, equipment and terminal for controlling factors of shale organic matter enrichment

Abstract

The present invention belongs to the technical field of oil and gas exploration, and discloses a method, system, equipment and terminal for evaluating the controlling factors of shale organic matter enrichment. On the basis of restoring the ancient sedimentary environment and ancient productivity by inorganic geochemical element indicators, a paleoenvironment normalization coefficient is proposed; an organic carbon content TOC correlation model is constructed, and the organic carbon content TOC is used to evaluate the abundance of organic matter in source rocks, and the controlling factors of shale organic matter enrichment are evaluated. The present invention uses a normalization coefficient to process a data set composed of elemental indicators with different controlling factors, and simplifies the cumbersome process of analyzing the correlation between a single indicator and TOC content one by one in the traditional method; a single factor indicator is used to fit the measured TOC, and a correlation model is established, and the control factor analysis process of organic matter enrichment under the comprehensive control of multiple factors is considered, which improves the accuracy of the traditional method in analyzing the correlation between a single factor indicator and TOC, and realizes the comprehensive evaluation of the main controlling factors of black shale organic matter enrichment.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas exploration, and in particular relates to a method, system, equipment and terminal for evaluating control factors of shale organic matter enrichment. Background Art

[0002] At present, unconventional oil and gas has gradually become the focus of global oil and gas exploration, and shale oil and gas has become one of the main directions of unconventional oil and gas exploration. China immediately launched shale oil and gas exploration. With the rapid development of my country's industrial economy, the demand for oil and gas resources is increasing, and the energy crisis is imminent. A solution must be found as soon as possible. Shale oil, as an important type of unconventional oil and gas resource, has huge exploration prospects and is expected to solve the huge gap in energy demand. Organic matter is the basis of oil and gas generation, and its content is an important factor in evaluating the hydrocarbon generation potential of source rocks. Its sedimentary enrichment is also closely related to the development of favorable layers for oil and gas exploration. Sedimentary environment research, as an indispensable part of shale oil exploration and development, plays a decisive role in the source and preservation of organic matter and controls the enrichment of organic matter. Therefore, conducting an evaluation study on the control factors of shale sedimentary paleoenvironment on organic matter enrichment is of great significance to promote the exploration and development of unconventional oil and gas.

[0003] Previous researchers have conducted a series of research and analysis on the factors affecting the enrichment of organic matter. Many scholars have limited their research focus to ancient productivity and ancient redox environment, and have given relatively little consideration to other influencing factors. Moreover, different influencing factors often influence each other, and the superposition and combination of various factors will also control the enrichment of organic matter. It is understood that in previous studies on the factors affecting the enrichment of organic matter, only a few scholars have explored the remaining factors (including ancient salinity, ancient climate, water depth, etc.) in addition to ancient productivity and ancient oxygen phases, but they often only focus on the relationship between organic matter and various influencing factors, ignoring the mutual influence between various factors, which needs further research to provide new geochemical evidence for shale oil exploration.

[0004] Organic matter is the material basis for the generation of oil and gas, and organic carbon content (TOC) is often used to evaluate the abundance of organic matter in source rocks. Initially, beginners often use the longitudinal comparison of TOC and paleoenvironmental element geochemical indicators on the profile to analyze the main controlling factors of organic matter enrichment in black shale. When a stratum with high TOC is found on the profile, the environmental indicators with similar TOC trends are considered to be the main controlling factors of organic matter enrichment. This method can also be comprehensively compared with paleo-salinity, redox conditions, paleo-climate, paleo-water depth and paleo-productivity to comprehensively analyze the main controlling factors of organic matter enrichment, and has been widely used in the early stages of research on the main controlling factors of organic matter enrichment.

[0005] Although the above technology can comprehensively analyze the main controlling factors of organic matter enrichment in black shale, such as the control of single factors such as paleo-salinity, redox conditions, paleo-climate, paleo-water depth and paleo-productivity, etc., this method requires analyzing each sedimentary environmental control factor one by one, which is a cumbersome process. In addition, since this method mainly identifies the degree of correlation by observing the distribution trend, its accuracy is low and needs to be improved.

[0006] At present, the most common method used by scholars is to use the correlation between the organic carbon content of black shale and the geochemical index parameters of paleo-salinity, redox conditions, paleo-climate, paleo-water depth and paleo-productivity elements, and to analyze the main controlling factors of organic matter enrichment in black shale by drawing correlation cross-plots. It is believed that the environmental factors corresponding to the indicators with a good correlation with TOC control the enrichment of organic matter. Conversely, the correlation between paleo-environmental indicators and TOC is poor, which indicates that the enrichment of organic matter is not controlled by the sedimentary environmental factors corresponding to the indicator. This method improves the reliability of previous analyses.

[0007] Although the above method can more accurately reflect the control of single factor indicators on organic matter, due to the diversity of paleoenvironmental indicators, when using the above method for cross-plot analysis, it can only reflect the control of a certain paleoenvironmental factor, and cannot directly conduct a comprehensive analysis of multiple factors. If you want to analyze the control of multiple factors on organic matter enrichment, you need to draw the cross-plot of TOC and the corresponding environmental indicators in turn, which is a cumbersome and complicated process.

[0008] In recent years, cluster analysis has also been used to evaluate the main controlling factors of organic matter enrichment in black shales. Clustering is a process of dividing data into different classes or clusters, so objects in the same cluster have great similarities, while objects in different clusters have great differences. Cluster analysis was performed on organic carbon content (TOC) and paleo-salinity, redox conditions, paleoclimate, paleo-water depth and paleo-productivity index parameters. Among them, the paleo-environmental factors corresponding to the indicators classified into the same cluster as TOC content have a great relationship with the enrichment of organic matter and may be the main controlling factors of organic matter enrichment. On the contrary, the paleo-environmental factors corresponding to the indicators classified into different clusters with TOC content have a weaker effect on organic matter enrichment. This method can analyze the control of various environmental factors on organic matter enrichment in a more comprehensive and comprehensive manner, without the need to analyze each sedimentary environmental control factor one by one.

[0009] Although the above method simplifies the process of analyzing TOC content and control factor indicators one by one, the corresponding indicators of each environmental factor are not unique, and the analysis system will be relatively complex. It is worth noting that cluster analysis is mainly used in exploratory research. The results of its analysis can provide multiple possible solutions. The selection of the final solution requires the subjective judgment of the researcher and subsequent analysis. Whether there are really different categories in the process of cluster analysis, and whether cluster analysis can obtain solutions divided into several categories, the accuracy of the final analysis results is also worth considering.

[0010] Therefore, in order to solve the problem that multiple indicators in the evaluation of shale organic matter enrichment control factors in the above-mentioned traditional methods jointly represent the same environmental control factor and analyze the comprehensive impact of various environmental control factors on organic matter enrichment, it is urgent to design a new evaluation method for shale organic matter enrichment control factors.

[0011] Through the above analysis, the problems and defects of the prior art are as follows:

[0012] (1) The existing longitudinal profile comparison technology requires analyzing each sedimentary environment control factor one by one, and cannot directly conduct a comprehensive analysis of multiple factors. The process is relatively cumbersome. In addition, the accuracy of identifying the degree of correlation by observing the distribution trend is low and needs to be improved.

[0013] (2) The existing cross-plot analysis technology can only reflect the controlling effect of a single paleoenvironmental factor and cannot directly conduct a comprehensive analysis of multiple factors. It is necessary to draw cross-plots of TOC content and corresponding environmental indicators in sequence, which is a tedious and complicated process.

[0014] (3) Existing cluster analysis methods are mainly used in exploratory research. The results of the analysis can provide multiple possible solutions. Whether there are really different categories in the cluster analysis process, cluster analysis can obtain several categories of solutions. The accuracy of the analysis results is worth considering. Summary of the invention

[0015] In view of the problems existing in the prior art, the present invention provides a method, system, equipment and terminal for evaluating control factors of shale organic matter enrichment.

[0016] The present invention is implemented in this way: on the basis of restoring the ancient environment and ancient productivity by inorganic geochemical element indicators, the traditional ancient sedimentary environment indicator parameters are optimized, a normalization coefficient is proposed, and the organic carbon content (TOC), an organic matter abundance evaluation parameter, is combined. The pre-treated normalization coefficient is used to construct a TOC correlation model with comprehensive control of multiple factors to evaluate the control factors of shale organic matter enrichment.

[0017] Furthermore, the evaluation method of the control factors of shale organic matter enrichment includes the following steps:

[0018] Step 1: When a single indicator indicates paleoenvironmental characteristics, the correlation between the single indicator and TOC is analyzed to characterize the control of environmental factors on organic matter;

[0019] Step 2: When multiple indicators jointly characterize paleoenvironmental characteristics, a paleoenvironmental normalization coefficient is proposed, and a normalization method is used to process a data set composed of elemental indicators with different controlling factors;

[0020] Step 3: Renormalize the single indicators or normalized coefficients of different paleo-sedimentary environments, and use SPSS (Statistical Product and Service Solutions) software to analyze the importance of predictive variables for the normalized control factor indicators;

[0021] Step 4: Select the control factor indicators with importance greater than 0.1, and establish a TOC correlation model of multi-factor comprehensive control with measured TOC as the target;

[0022] Step five: determine the main controlling factors of organic matter enrichment based on the influencing factor coefficients of the TOC correlation model, and then achieve a comprehensive evaluation of the main controlling factors of organic matter enrichment in black shale.

[0023] Furthermore, the traditional paleoenvironmental indicator parameters in step one include paleoenvironmental information including paleoproductivity, paleo-redox conditions, paleo-salinity, paleo-climate, terrigenous debris input, and paleo-water depth.

[0024] Furthermore, biological phosphorus P bio and biological uranium U bio Two indicators are selected to characterize the characteristics of paleoproductivity; V / Cr and V / V+Ni are selected to characterize the paleo-redox characteristics; Sr / Ba and m are selected to characterize the paleo-salinity characteristics, where m=100×(MgO / Al2O3); the climate index C is selected to characterize the paleoclimate characteristics; the Ti / Al indicator is selected to characterize the terrigenous debris input characteristics; and Rb / K is selected to characterize the paleo-water depth characteristics.

[0025] Furthermore, in step 2, when multiple indicators are used to jointly characterize paleoenvironmental characteristics, a normalization coefficient is proposed. nor , is to convert P bio and U bio The two indicators are normalized and the average value is calculated; the redox normalization coefficient Redox nor , is to obtain the average value after normalizing the V / Cr and V / (V+Ni) indicators respectively; the paleosalinity normalization coefficient Salinitynor , is to normalize the Sr / Ba and m indicators respectively and then find the average value.

[0026] Paleoproductivity normalization coefficient Productivity nor The calculation formula is:

[0027] Productivity nor =[(P bio -P biomin ) / (P biomax -P biomin )+(U bio -U biomin ) / (U biomax -U biomin )] / 2;

[0028] Where P bio is the sample biological phosphorus, P biomin is the minimum value of sample biological phosphorus, P biomax is the maximum value of sample biological phosphorus, U bio For the sample biological uranium, U biomin is the minimum value of biological uranium in the sample, U biomax is the maximum value of biological uranium in the sample.

[0029] Furthermore, the construction of the TOC correlation model of multi-factor comprehensive control in step 4 includes:

[0030] Use each single environmental factor index to fit the measured TOC and establish a correlation model; by drawing the correlation diagram between each single factor, it is determined that when the correlation coefficient between each single factor is less than 0.7, Figure 4A to Figure 4C , when establishing the model, each type of single factor can be analyzed as an independent influencing factor; the data is normalized and preprocessed before fitting, and the preprocessing process is shown in the following formula:

[0031] X nor =(XX min ) / (X max -X min );

[0032] In the formula, X is the original data to be calculated, X nor is the normalized data, X min is the minimum value of the data, X max is the maximum value of the data.

[0033] SPSS software was used to analyze the importance of predictive variables for each normalized control factor index. nor Represents the normalized paleoproductivity index; Normalized Redoxnor Represents the normalized paleo-redox index; Normalized Salinity nor represents the normalized paleo-salinity index; Normalized C-value represents the normalized paleoclimate index; Normalized Ti / Al represents the normalized terrigenous debris input index; Normalized Rb / K represents the normalized paleo-water depth index.

[0034] Select the control factor indicators with importance greater than 0.1, such as Figure 5A to Figure 5C As shown in Figure 2, a TOC correlation model with multi-factor comprehensive control was established. According to the correlation coefficient of each influencing factor indicator, the TOC correlation model of unit A is shown in Figure 2. Figure 6 , among which the most important indicator is Normalized Salinity nor , followed by Normalized Redox nor , Normalized Ti / Al and Normalized Rb / K; TOC correlation model for unit B is shown in Figure 7 , among which the most important indicator is NormalizedProductivity nor , followed by Normalized Ti / Al; C unit TOC correlation model is shown in Figure 8 , the most important indicator is Normalized Rb / K, followed by Normalized Salinity nor ,Normalized C-value and Normalized Redox nor .

[0035] Among them, the A unit correlation model is as follows:

[0036] TOC Model =-0.283+3.522×A1+1.424×A2+2.417×A3-1.762×A4;

[0037] Where A1 is Normalized Salinity nor , represents the salinity of lake water, the larger the value, the higher the salinity of lake water; A2 is Normalized Redox nor , characterizes the degree of reduction, and the larger the value, the stronger the reducing conditions; A3 is NormalizedTi / Al, which characterizes the input of terrigenous debris, and the larger the value, the more terrigenous debris input; A4 is Normalized Rb / K, which characterizes the ancient water depth, and the larger the value, the greater the water depth.

[0038] The correlation model of unit B is as follows:

[0039] TOC Model =1.282+1.516×B1-0.717×B2;

[0040] Where B1 is Normalized Productivity nor , represents the initial productivity of the lake, and the larger the value, the higher the initial productivity; B2 is Normalized Ti / Al, which represents the input of terrigenous debris, and the larger the value, the more terrigenous debris input.

[0041] The C unit correlation model is as follows:

[0042] TOC Model =2.687-1.345×C1+0.704×C2-0.597×C3-0.207×C4;

[0043] Where, C1 is Normalized Rb / K, which represents the paleo-water depth. The larger the value, the greater the water depth. C2 is Normalized Salinity nor , represents the salinity of the lake water, the larger the value, the higher the salinity of the lake water; C3 is NormalizedRedox nor , represents the degree of reduction, and the larger the value, the stronger the reducing conditions; C4 is the Normalized C-value, which represents the characteristics of the paleoclimate, and the larger the value, the warmer and more humid the climate.

[0044] Another object of the present invention is to provide a shale organic matter enrichment control factor evaluation system using the shale organic matter enrichment control factor evaluation method, the shale organic matter enrichment control factor evaluation system comprising:

[0045] The parameter optimization module is used to optimize the traditional paleoenvironmental index parameters. When multiple indicators jointly characterize the paleoenvironmental characteristics, a paleoenvironmental normalization coefficient is proposed, and a normalization method is used to process the data set composed of different elemental indicators of the control factors; the data preprocessing module is used to normalize different elemental indicators, including a single paleoenvironmental index parameter or the paleoenvironmental normalization coefficient proposed by the present invention, to provide a good basic data foundation for model establishment;

[0046] Importance analysis module, used for data analysis of renormalized single paleoenvironmental index parameters or paleoenvironmental normalization coefficients, using SPSS software to perform importance analysis of predictive variables on normalized control factor indicators;

[0047] The TOC correlation model building module is used to select the control factor indicators with importance greater than 0.1, and establish a TOC correlation model with multi-factor comprehensive control based on the measured organic carbon content;

[0048] The organic matter enrichment factor evaluation module is used to determine the main controlling factors of organic matter enrichment according to the influencing factor coefficients of the TOC correlation model, and then to achieve a comprehensive evaluation of the main controlling factors of organic matter enrichment in black shale.

[0049] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein 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 evaluating the control factors of shale organic matter enrichment.

[0050] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for evaluating the control factors of shale organic matter enrichment.

[0051] Another object of the present invention is to provide an information data processing terminal, which is used to implement the shale organic matter enrichment control factor evaluation system.

[0052] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0053] First, in view of the technical problems existing in the above-mentioned prior art and the difficulty of solving the problems, the technical solutions to be protected by the present invention and the results and data during the research and development process are closely combined to analyze in detail and deeply how the technical solutions of the present invention solve the technical problems, and some creative technical effects brought about after solving the problems. The specific description is as follows:

[0054] The present invention provides a new method for evaluating the controlling factors of shale organic matter enrichment. On the basis of restoring the ancient sedimentary environment and ancient productivity by inorganic geochemical element indicators, the organic carbon content (TOC) is used to evaluate the abundance of organic matter in source rocks, and the controlling factors of shale organic matter enrichment are evaluated. The present invention optimizes the traditional paleoenvironmental indicator parameters. When a single indicator indicates paleoenvironmental characteristics, it is only necessary to analyze the correlation between the single indicator and TOC to characterize the control effect of a certain environmental factor on organic matter; when multiple indicators jointly characterize a certain paleoenvironmental characteristic, the present invention proposes a normalization coefficient (Normalization Coefficient), which uses a normalization method to process a data set composed of different element indicators of a certain control factor. Through the normalization process, it is only necessary to analyze the correlation between the normalization coefficient and TOC to reflect the control effect of a certain environmental factor jointly characterized by multiple indicators on organic matter. In order to improve the fitting effect and facilitate comparison, the present invention needs to preprocess (normalize) the data, and then use the normalized (eliminate the dimensional relationship between the data) control factor indicators to perform the importance analysis of the predictive variables. In order to improve the accuracy of the model, the present invention generally selects the control factor indicators with importance greater than 0.1, establishes a TOC correlation model of multi-factor comprehensive control, and thus realizes the comprehensive evaluation of the main control factors of organic matter enrichment in black shale.

[0055] At the same time, the technical solution of the present invention can also bring the following beneficial effects:

[0056] (1) Convenience and universality

[0057] In the traditional evaluation method of shale organic matter enrichment control factors, when faced with multiple indicators jointly characterizing the characteristics of the same environmental factor, it is often necessary to analyze the correlation between different indicators of the same environmental factor characteristics and TOC content separately, and then integrate the correlation analysis results of multiple indicators for comprehensive analysis. The whole process is cumbersome and complicated, and the operation is inconvenient. This method proposes a normalization coefficient, which processes a data set composed of different element indicators of a certain control factor. Through normalization processing, it is only necessary to analyze the correlation between the normalization coefficient and TOC, which simplifies the cumbersome process of analyzing the correlation between a single indicator and TOC content one by one in the conventional analysis process, and greatly facilitates the evaluation of shale organic matter enrichment control factors.

[0058] (2) High accuracy and comprehensiveness

[0059] The enrichment of organic matter is a very complex process, which may be controlled by multiple factors such as initial productivity, redox conditions, paleo-salinity, paleoclimate, water depth, etc. It may also be controlled by a combination of different factors, which will result in insignificant results when single factor analysis is performed. Figure 3A to Figure 3FAs shown in the figure, the main controlling factors of organic matter enrichment are complex control, and the control effect of a single factor cannot be well reflected. This method uses each type of single factor index to fit the measured TOC and establish a correlation model. The modeling effect shows that the simulation result R 2 All of them are higher than 0.8, showing a very good fitting effect, which improves the accuracy of traditional methods in analyzing the correlation between a single factor index and TOC, and considers the control factor analysis process of organic matter enrichment under the comprehensive control of multiple factors, which is more comprehensive. It also shows that this technology has a good prospect for use in the evaluation of control factors of shale organic matter enrichment and is suitable for promotion and application.

[0060] Second, considering the technical solution as a whole or from the perspective of the product, the technical effects and advantages of the technical solution to be protected by the present invention are described in detail as follows:

[0061] In view of the problem that multiple indicators jointly characterize the same environmental control factor when evaluating the control factors of shale organic matter enrichment in traditional methods, the evaluation method of the control factors of shale organic matter enrichment of the present invention proposes a normalization coefficient (Normalization Coefficient). This parameter uses a normalization method to process a data set composed of different elemental indicators of a certain control factor; through the normalization process, only the correlation between the normalization coefficient and TOC needs to be analyzed to reflect the control effect of a certain environmental factor jointly characterized by multiple indicators on organic matter. In addition, the present invention solves the problem that traditional methods cannot simultaneously and comprehensively analyze the control effects of multiple environmental factors on organic matter enrichment. The normalization coefficients of various paleoenvironments and environmental indicators are used to fit the measured TOC, and a correlation model is established, thereby realizing a comprehensive evaluation of the main control factors of organic matter enrichment in black shale.

[0062] The present invention uses a normalization coefficient to process a data set composed of different element indicators of a certain control factor, which simplifies the tedious process of analyzing the correlation between a single indicator and TOC content one by one in the traditional method. The evaluation of the organic matter enrichment control factor can be completed by only analyzing the correlation between the normalization coefficient and TOC.

[0063] In addition, the present invention also uses each type of single factor index to fit the measured TOC and establishes a correlation model, taking into account the control factor analysis process of organic matter enrichment under the comprehensive control of multiple factors, thereby improving the accuracy of traditional methods in analyzing the correlation between single factor indicators and TOC, and realizing a comprehensive evaluation of the main control factors of organic matter enrichment in black shale.

[0064] Third, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0065] The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0066] Previous researchers have conducted a series of research and analysis on the control factors of organic matter enrichment, but many scholars have only focused on the control of paleoproductivity on organic matter enrichment, or the control of redox environment on organic matter enrichment. Such a single factor has a control effect on organic matter enrichment, and relatively few considerations have been given to other influencing factors. In recent years, scholars have gradually realized that not only paleoproductivity and redox environment will affect the enrichment of organic matter, but other factors such as paleosalinity, paleoclimate, and paleowater depth will also have a certain control effect on organic matter enrichment. However, currently, due to the constraints of technical methods, people can only analyze the impact of each environmental factor on organic matter enrichment one by one, and the entire operation process is cumbersome. In addition, each environmental factor does not have a single alternative indicator. Often, each environmental factor can be represented by multiple alternative indicators, which makes the entire analysis process more complicated and the operation process more troublesome. People are in urgent need of an optimized operation to simplify this cumbersome operation step. To this end, the present invention proposes a normalization coefficient (Normalization Coefficient). This parameter uses a normalization method to process a data set composed of different element indicators of a certain control factor. Through the normalization process, it is only necessary to analyze the correlation between the normalization coefficient and TOC to reflect the control effect of a certain environmental factor jointly characterized by multiple indicators on organic matter.

[0067] In addition, scholars have also realized that the various influencing factors will also affect each other. It is not just the ancient productivity or the redox environment that affects the enrichment of organic matter alone. The superposition and combination of other factors such as ancient salinity, ancient climate, and ancient water depth will also control the enrichment of organic matter. However, it is still restricted by technical methods. It can only analyze the control effect of a single influencing factor on organic matter. It is impossible to conduct a comprehensive analysis and study of the effects of various influencing factors on organic matter enrichment, and it is impossible to achieve the analysis and study of the mutual influence of various factors. A new technical solution is urgently needed to solve the above problems. For this reason, the present invention combines the organic carbon content (TOC), an evaluation parameter of organic matter abundance, and uses the normalized coefficient after pretreatment to construct a TOC correlation model of multi-factor comprehensive control, thereby realizing a comprehensive evaluation of the main control factors of organic matter enrichment in black shale. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0069] Figure 1is a flow chart of a method for evaluating control factors of shale organic matter enrichment provided by an embodiment of the present invention;

[0070] Figure 2 It is a schematic diagram of a method for evaluating control factors of shale organic matter enrichment provided by an embodiment of the present invention;

[0071] Figure 3A is the paleoproductivity normalization coefficient Productivity provided in the embodiment of the present invention nor Cross-plot with TOC content of each unit;

[0072] Figure 3B is the redox normalization coefficient Redox provided in the embodiment of the present invention nor Cross-plot with TOC content of each unit;

[0073] Figure 3C Salinity is the paleo-salinity normalization coefficient provided by the embodiment of the present invention. nor Cross-plot with TOC content of each unit;

[0074] Figure 3D is a cross-plot between the climate index C and the TOC content of each unit provided by an embodiment of the present invention;

[0075] Figure 3E is a cross-plot between the Ti / Al index and the TOC content of each unit provided in an embodiment of the present invention;

[0076] Figure 3F It is a cross-plot between the Rb / K index and the TOC content of each unit provided in the embodiment of the present invention;

[0077] Figure 4A It is a schematic diagram of correlation coefficients of various influencing factor indicators of unit A provided in an embodiment of the present invention;

[0078] Figure 4B Schematic diagram of correlation coefficients of influencing factor indicators of unit B provided by an embodiment of the present invention;

[0079] Figure 4C It is a schematic diagram of correlation coefficients of various influencing factor indicators of the C unit provided in an embodiment of the present invention;

[0080] Figure 5A is an importance analysis diagram of the predictor variables of unit A output by the SPSS software provided in an embodiment of the present invention, with a cutoff value = 0.1;

[0081] Figure 5B is an importance analysis diagram of the predictor variables of unit B output by the SPSS software provided in an embodiment of the present invention, with a cutoff value = 0.1;

[0082] Figure 5Cis an importance analysis diagram of the predictor variables of unit C output by the SPSS software provided in an embodiment of the present invention, with a cutoff value = 0.1;

[0083] Figure 6 It is a schematic diagram of the fitting results of the A unit TOC correlation model provided by an embodiment of the present invention;

[0084] Figure 7 It is a schematic diagram of the fitting results of the TOC correlation model of unit B provided in an embodiment of the present invention;

[0085] Figure 8 It is a schematic diagram of the fitting results of the C unit TOC correlation model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the 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.

[0087] In view of the problems existing in the prior art, the present invention provides a method, system, device and terminal for evaluating control factors of shale organic matter enrichment. The present invention is described in detail below in conjunction with the accompanying drawings.

[0088] Definitions of abbreviations and key terms: TOC: Total Organic Carbon; SPSS: Statistical Product and Service Solutions; bio : Biological phosphorus; U bio :Biological uranium; Productivity nor : Normalization coefficient of ancient productivity; Redox nor : Redox normalization coefficient; Salinity nor : Normalized Productivity nor : Normalized Redox nor : Normalized Salinity nor : Normalized paleo-salinity index; Normalized C-value: Normalized paleoclimate index; Normalized Ti / Al: Normalized terrigenous debris input index; Normalized Rb / K: Normalized paleo-water depth index.

[0089] like Figure 1As shown, the method for evaluating the control factors of shale organic matter enrichment provided by the embodiment of the present invention includes the following steps:

[0090] S101, when a single indicator indicates paleoenvironmental characteristics, the correlation between the single indicator and TOC is analyzed to characterize the control of environmental factors on organic matter;

[0091] S102, when faced with multiple indicators jointly characterizing paleoenvironmental characteristics, a paleoenvironmental normalization coefficient is proposed, and a normalization method is used to process data sets composed of elemental indicators with different control factors;

[0092] S103, re-normalizing the normalized coefficients representing different environmental control factors, and using SPSS software to perform importance analysis of predictive variables on the normalized control factor indicators;

[0093] S104, select the normalized coefficient index of each control factor with importance greater than 0.1, and establish a TOC correlation model of multi-factor comprehensive control in combination with organic carbon content;

[0094] S105, determining the main controlling factors of organic matter enrichment according to the influencing factor coefficients of the TOC correlation model, and then achieving a comprehensive evaluation of the main controlling factors of organic matter enrichment in black shale.

[0095] As a preferred embodiment, Figure 2 As shown, the method for evaluating the controlling factors of shale organic matter enrichment provided by the embodiment of the present invention specifically includes the following steps:

[0096] 1. Normalization coefficient

[0097] The geochemical distribution of geochemical elements between sediments and water media is not only related to the physical and chemical properties of the elements themselves, but is also mainly affected by the water sedimentation environment. Therefore, by analyzing the geochemical characteristics of elements in sediments or sedimentary rocks, paleoenvironmental information such as paleoproductivity, paleo-redox conditions, paleo-salinity, and water restriction conditions during the geological history period can be inverted. Among the many element geochemical indicators, the present invention selects biogenic phosphorus (P bio ) and biological uranium (U bio ) are selected to characterize the characteristics of paleoproductivity; the element ratio V / Cr and the element ratio V / V+Ni are selected to characterize the paleo-redox characteristics; the element ratio Sr / Ba and the m value (m=100×(MgO / Al2O3)) are selected to characterize the paleo-salinity characteristics; the climate index C is selected to characterize the paleoclimate characteristics; the element ratio Ti / Al is selected to characterize the input characteristics of terrigenous debris; the element ratio Rb / K is selected to characterize the paleo-water depth characteristics.

[0098] When there is more than one indicator that characterizes a certain aspect of paleoenvironmental characteristics, such as paleoproductivity characteristics, paleo-oxidation-reduction characteristics, and paleo-salinity characteristics that appear in the present invention, the present invention proposes a normalization coefficient for the first time. This parameter uses a normalization method to process a data set composed of different elemental indicators of a certain control factor. Through the normalization process, it is only necessary to analyze the correlation between the normalization coefficient and the TOC content to obtain the control effect of each factor on the organic matter enrichment. Compared with the traditional correlation analysis between a single element indicator and the TOC content, this parameter also takes into account the influence of multiple elemental indicators in a more comprehensive and comprehensive manner. The paleoproductivity normalization coefficient Productivity that appears in the present invention nor , is to convert P bio and U bio The two indicators are normalized and the average value is calculated; the redox normalization coefficient Redox nor , is to obtain the average value after normalizing the element ratio V / Cr and element ratio V / (V+Ni) respectively; Paleosalinity normalization coefficient Salinity nor , is to find the average value after normalizing the element ratio Sr / Ba and m value index respectively. nor For example, the calculation formula is:

[0099] Productivity nor =[(P bio -P biomin ) / (P biomax -P biomin )+(U bio -U biomin ) / (U biomax -U biomin )] / 2

[0100] (Formula 1)

[0101] Where P bio is the sample biological phosphorus, P biomin is the minimum value of sample biological phosphorus, P biomax is the maximum value of sample biological phosphorus, U bio For the sample biological uranium, U biomin is the minimum value of biological uranium in the sample, U biomax is the maximum value of biological uranium in the sample.

[0102] 2.TOC association model

[0103] The enrichment of organic matter should be accompanied by the conditions of flourishing aquatic organisms (good biological productivity), anoxic bottom water conditions (favorable preservation conditions) and the input of a small amount of detrital material (low dilution effect). In recent years, a large number of scholars have believed that the differential enrichment of organic matter is controlled by many factors, which may be controlled by multiple factors such as initial productivity, redox conditions, paleo-salinity, paleoclimate, water body depth, etc., or it may be controlled by a combination of different factors. The present invention analyzes the controlling factors of organic matter enrichment in three consecutive different sedimentary units. When the conventional intersection plot method is used for analysis, the control effect of each single factor of the three units on organic matter is not obvious (see Figure 3A to Figure 3F ), which may be the result of comprehensive control of multiple factors. To this end, the present invention uses the above-mentioned single environmental factor indicators to fit the measured TOC and establish a correlation model. Figure 4A to Figure 4C As shown in the figure, by drawing the correlation diagram between each single factor, it can be seen that the correlation coefficients between each single factor of the three units are all less than 0.7. Therefore, when establishing the model, each type of single factor is analyzed as an independent influencing factor. Since the values ​​of the indicators vary greatly, in order to improve the fitting effect and facilitate comparison, the data needs to be preprocessed before fitting. The specific process of preprocessing is shown in formula (2):

[0104] X nor =(XX min ) / (X max -X min ) (Formula 2)

[0105] In the formula, X is the original data to be calculated, X nor is the normalized data, X min is the minimum value of the data, X max is the maximum value of the data.

[0106] The importance of predictive variables is analyzed using the normalized (eliminating the dimensional relationship between data) control factor indicators mentioned above. SPSS (Statistical Product and Service Solutions) software is used here to perform the above analysis. nor Represents the normalized paleoproductivity index; NormalizedRedox nor Represents the normalized paleo-redox index; Normalized Salinity nor represents the normalized paleo-salinity index; Normalized C-value represents the normalized paleoclimate index; Normalized Ti / Al represents the normalized terrigenous debris input index; Normalized Rb / K represents the normalized paleo-water depth index.

[0107] In order to improve the accuracy of the model, the present invention selects the control factor indicators with unit importance greater than 0.1 to establish a TOC correlation model of multi-factor comprehensive control. Figure 5A to Figure 5C It can be seen that the most important indicator in unit A is Normalized Salinity nor , followed by Normalized Redox nor , Normalized Ti / Al and NormalizedRb / K; the most important indicator in unit B is Normalized Productivity nor , followed by Normalized Ti / Al; the most important indicator in C unit is Normalized Rb / K, followed by Normalized Salinity nor ,Normalized C-value and Normalized Redox nor .

[0108] The A unit correlation model is as follows:

[0109] TOC Model =-0.283+3.522×A1+1.424×A2+2.417×A3-1.762×A4 (Formula 3)

[0110] Where A1 is Normalized Salinity nor , represents the salinity of the lake water. The larger the value, the higher the salinity of the lake water. A2 is Normalized Redox nor , represents the degree of reduction, the larger the value, the stronger the reduction condition; A3 is NormalizedTi / Al, represents the input of terrigenous debris, the larger the value, the more terrigenous debris input; A4 is Normalized Rb / K, represents the paleo-water depth, the larger the value, the greater the water depth. Figure 6 It can be seen that the TOC of the A unit correlation model (TOC Model ) and the measured TOC(TOC Measured )R 2 =0.8411, the fitting effect is good. The coefficient values ​​of each influencing factor in the model show that the enrichment of organic matter in unit A is mainly affected by paleo-salinity. The increase of paleo-salinity promotes the enrichment of organic matter; the moderate input of terrigenous debris increases the content of organic matter; the increase of paleo-water depth makes the residence time of organic matter in the water body longer, the degree of oxygen degradation is higher, and the abundance of organic matter decreases.

[0111] The correlation model of unit B is as follows:

[0112] TOCModel =1.282+1.516×B1-0.717×B2 (Formula 4)

[0113] Where B1 is Normalized Productivity nor , represents the initial productivity of the lake, the larger the value, the higher the initial productivity; B2 is Normalized Ti / Al, representing the terrigenous debris input, the larger the value, the more terrigenous debris input. Figure 7 It can be seen that the B unit TOC Model With TOC Measured Between the two 2 =0.9483, with a good fitting effect. The coefficient values ​​of the influencing factors in the model show that the enrichment of organic matter in Unit B is mainly affected by the initial productivity conditions. The higher the initial productivity, the more conducive it is to the enrichment of organic matter; the input of terrigenous debris will have a weak inhibitory effect on the enrichment of organic matter.

[0114] The C unit correlation model is as follows:

[0115] TOC Model =2.687-1.345×C1+0.704×C2-0.597×C3-0.207×C4 (Formula 5)

[0116] Where, C1 is Normalized Rb / K, which represents the paleo-water depth. The larger the value, the greater the water depth. C2 is Normalized Salinity nor , represents the salinity of the lake water. The larger the value, the higher the salinity of the lake water. C3 is NormalizedRedox nor , represents the degree of reduction, the larger the value, the stronger the reduction condition; C4 is the Normalized C-value, which represents the paleoclimate characteristics, the larger the value, the warmer and more humid the climate. Figure 8 It can be seen that C unit TOC Model With TOC Measured Between the two 2 =0.9391, the fitting effect is good. Through the correlation model, it can be seen that the low organic matter content of this unit is mainly affected by the ancient water depth conditions. The deep water environment and humid climate have an inhibitory effect on the enrichment of organic matter, and the appropriate lake water salinity will promote biological reproduction.

[0117] The shale organic matter enrichment control factor evaluation system provided by the embodiment of the present invention includes:

[0118] The parameter optimization module is used to optimize the traditional paleoenvironmental index parameters. When multiple indexes jointly characterize the paleoenvironmental characteristics, a paleoenvironmental normalization coefficient is proposed, and a normalization method is used to process the data set composed of different elemental indexes of the control factors; the data preprocessing module is used to normalize different elemental indexes, including a single paleoenvironmental index parameter or the paleoenvironmental normalization coefficient proposed by the present invention, to provide a good basic data foundation for model establishment;

[0119] Importance analysis module, used for re-normalization preprocessing of single paleoenvironmental index parameters or paleoenvironmental normalization coefficients, and using SPSS software to perform importance analysis of predictive variables on normalized control factor indicators;

[0120] The TOC correlation model building module is used to select the control factor indicators with importance greater than 0.1, and establish a TOC correlation model with multi-factor comprehensive control based on the measured organic carbon content;

[0121] The organic matter enrichment factor evaluation module is used to determine the main controlling factors of organic matter enrichment according to the influencing factor coefficients of the TOC correlation model, and then to achieve a comprehensive evaluation of the main controlling factors of organic matter enrichment in black shale.

[0122] In order to prove the creativity and technical value of the technical solution of the present invention, this section provides application examples of the claimed technical solution on specific products or related technologies.

[0123] Taking the three sedimentary units of the first member of the Qingshankou Formation in the Songliao Basin as an example, the traditional paleoenvironmental indicator parameters in the evaluation of the controlling factors of organic matter enrichment in the first member of the Qingshankou Formation were optimized, and the TOC correlation model with multi-factor comprehensive control was constructed using the pre-processed normalized coefficients. The controlling factors of shale organic matter enrichment in the three units of the first member of the Qingshankou Formation were evaluated respectively.

[0124] The traditional paleoenvironmental indicators commonly used in this area mainly include biogenic phosphorus (P bio ) and biological uranium (U bio ) two indicators to characterize paleoproductivity; V / Cr and V / V+Ni two indicators to characterize paleooxidation-reduction characteristics; Sr / Ba and m (m = 100 × (MgO / Al2O3)) two indicators to characterize paleosalinity characteristics; climate index C to characterize paleoclimate characteristics; Ti / Al indicator to characterize terrigenous debris input characteristics; Rb / K to characterize paleowater depth characteristics. The traditional paleoenvironmental indicator parameters in the evaluation of organic matter enrichment control factors of the first section of the Qingshankou Formation were optimized to obtain Normalized Productivity nor Represents the normalized paleoproductivity index; Normalized Redox nor Represents the normalized paleo-redox index; Normalized Salinitynor represents the normalized paleo-salinity index; Normalized C-value represents the normalized paleoclimate index; Normalized Ti / Al represents the normalized terrigenous debris input index; Normalized Rb / K represents the normalized paleo-water depth index.

[0125] Select the control factor indicators of each unit whose importance is greater than 0.1, and establish the TOC correlation model of multi-factor comprehensive control. Finally, the most important indicator in unit A is Normalized Salinity nor , followed by Normalized Redox nor ,Normalized Ti / Al and Normalized Rb / K( Figure 6 ), the enrichment of organic matter is mainly affected by paleo-salinity. Increased paleo-salinity promotes the enrichment of organic matter; moderate input of terrigenous debris increases the content of organic matter; the increase in paleo-water depth makes the residence time of organic matter in the water longer, the degree of oxygen degradation is higher, and the abundance of organic matter decreases. The most important indicator in unit B is Normalized Productivity nor , followed by NormalizedTi / Al( Figure 7 ), the enrichment of organic matter in unit B is mainly affected by the initial productivity conditions. The higher the initial productivity, the more conducive it is to the enrichment of organic matter; the input of terrigenous debris will have a weak inhibitory effect on the enrichment of organic matter. The most important indicator in unit C is Normalized Rb / K, followed by Normalized Salinity nor ,Normalized C-value and Normalized Redox nor ( Figure 8 ), the lower organic matter content of this unit is mainly affected by the ancient water depth conditions. The deep water environment and humid climate have an inhibitory effect on the enrichment of organic matter; suitable lake water salinity will promote biological reproduction.

[0126] Existing technologies can often only analyze the influence of each environmental factor on organic matter enrichment one by one, and the entire operation process is cumbersome (Figure 3). In addition, the alternative index of each environmental factor is not single, and each environmental factor is represented by a variety of alternative indicators, which makes the analysis process more complicated and cumbersome. Not only that, the existing technology can only analyze the control effect of a single influencing factor on organic matter (Figure 3), and it is impossible to realize the comprehensive analysis and research of each influencing factor on organic matter enrichment, and it is even more impossible to realize the analysis and research of the mutual influence between each factor. However, the normalization coefficient proposed in the present invention uses a normalization method to process a data set composed of different elemental indicators of a certain control factor. Through the normalized processing, it is only necessary to analyze the correlation between the normalization coefficient and TOC to reflect the control effect of a certain environmental factor on organic matter jointly characterized by multiple indicators, which simplifies the cumbersome process of analyzing the correlation between a single indicator and TOC content one by one in the conventional analysis process, and greatly facilitates the evaluation of the control factors of shale organic matter enrichment. In addition, the present invention combines the organic carbon content, an evaluation parameter of organic matter abundance, to construct a TOC correlation model with comprehensive multi-factor control, thereby achieving a comprehensive evaluation of the main control factors of organic matter enrichment in black shale. Figure 6 Only through the TOC correlation model, we can know that the most important indicator in unit A is Normalized Salinity nor , followed by Normalized Redox nor , Normalized Ti / Al and Normalized Rb / K, the enrichment of organic matter is mainly controlled by paleo-salinity, redox environment, moderate input of terrigenous debris and paleo-water depth. The control factor analysis process of organic matter enrichment under multi-factor comprehensive control of the present invention is more comprehensive and easy to operate, and is suitable for promotion and application.

[0127] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0128] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for evaluating control factors of shale organic matter enrichment, characterized in that: The evaluation method of controlling factors of shale organic matter enrichment includes the following steps: Step 1: When a single indicator indicates paleoenvironmental characteristics, the correlation between the single indicator and TOC is analyzed to characterize the control of environmental factors on organic matter; Step 2: When multiple indicators jointly characterize paleoenvironmental characteristics, a paleoenvironmental normalization coefficient is proposed, and a normalization method is used to process a data set composed of elemental indicators with different control factors; Step 3: Renormalize the single index or normalization coefficient of different paleo-sedimentary environments, and use SPSS software to analyze the importance of predictive variables for each normalized control factor index; Step 4: Select the control factor indicators with importance greater than 0.1, and establish a TOC correlation model of multi-factor comprehensive control with measured TOC as the target; Step 5: Determine the main controlling factors of organic matter enrichment based on the influencing factor coefficients of the TOC correlation model, and then achieve a comprehensive evaluation of the main controlling factors of organic matter enrichment in black shale; Select biological phosphorus bio and biological uranium U bio Two indicators are selected to characterize the characteristics of paleoproductivity; the element ratio V / Cr and the element ratio V / (V+Ni) are selected to characterize the paleo-redox characteristics; the element ratio Sr / Ba and m are selected to characterize the paleo-salinity characteristics, where m = 100×(MgO / Al2O3); the climate index C is selected to characterize the paleoclimate characteristics; the element ratio Ti / Al is selected to characterize the input characteristics of terrigenous debris; the element ratio Rb / K is selected to characterize the paleo-water depth characteristics; In step 2, when multiple indicators jointly characterize the paleoenvironmental characteristics, a normalization coefficient is proposed; among them, the paleoproductivity normalization coefficient Productivity nor , is to convert P bio and U bio The two indicators are normalized and the average value is calculated; the redox normalization coefficient Redox nor , is to obtain the average value after normalizing the V / Cr and V / (V+Ni) indicators respectively; the paleosalinity normalization coefficient Salinity nor , is to obtain the average value after normalizing the Sr / Ba and m indicators respectively; The normalized coefficient of ancient productivity is Productivity nor The calculation formula is: Productivity nor =[(P bio -P biomin ) / (P biomax -P biomin )+(U bio -U biomin ) / (U biomax -U biomin )] / 2; Where P bio is the sample biological phosphorus, P biomin is the minimum value of sample biological phosphorus, P biomax is the maximum value of sample biological phosphorus, U bio For the sample biological uranium, U biomin is the minimum value of biological uranium in the sample, U biomax is the maximum value of biological uranium in the sample; The construction of the TOC correlation model of multi-factor comprehensive control in step 4 includes: Use each single environmental factor index to fit the measured TOC and establish a correlation model; by drawing the correlation diagram between each single factor, it is determined that when the correlation coefficients between the single factors of unit A, unit B and unit C are all less than 0.7, each type of single factor can be analyzed as an independent influencing factor when establishing the model; preprocess the data before fitting, and the preprocessing process is shown in the following formula: X nor =(X-X min ) / (X max -X min ); In the formula, X is the original data to be calculated, X nor is the normalized data, X min is the minimum value of the data, X max is the maximum value of the data; SPSS software was used to analyze the importance of predictive variables for each normalized control factor index; NormalizedProductivity nor Represents the normalized paleoproductivity index; Normalized Redox nor Represents the normalized paleo-redox index; Normalized Salinity nor represents the normalized paleo-salinity index; Normalized C-value represents the normalized paleoclimate index; Normalized Ti / Al represents the normalized terrigenous debris input index; Normalized Rb / K represents the normalized paleo-water depth index; Select the control factor indicators of each unit whose importance is greater than 0.1, and establish the TOC correlation model of multi-factor comprehensive control; according to the correlation coefficient of each influencing factor indicator, the most important indicator in unit A is Normalized Salinity nor , followed by Normalized Redox nor , Normalized Ti / Al and Normalized Rb / K; the most important indicator in unit B is Normalized Productivity nor , followed by Normalized Ti / Al; the most important indicator in C unit is Normalized Rb / K, followed by Normalized Salinity nor ,Normalized C-value and Normalized Redox nor ; Among them, the A unit correlation model is as follows: TOC Model =-0.283+3.522×A1+1.424×A2+2.417×A3-1.762×A4; Where A1 is the Normalized Salinity of unit A nor , represents the salinity of the lake water, the larger the value, the higher the salinity of the lake water; A2 is the Normalized Redox nor , represents the degree of reduction, the larger the value, the stronger the reduction condition; A3 is the Normalized Ti / Al of unit A, which represents the input of terrigenous debris, the larger the value, the more terrigenous debris input; A4 is the Normalized Rb / K of unit A, which represents the paleo-water depth, the larger the value, the greater the water depth; The correlation model of unit B is as follows: TOC Model =1.282+1.516×B1-0.717×B2; Where B1 is the Normalized Productivity of unit B nor , represents the initial productivity of the lake, and the larger the value, the higher the initial productivity; B2 is the NormalizedTi / Al of unit B, which represents the terrigenous debris input, and the larger the value, the more terrigenous debris input; The C unit correlation model is as follows: TOC Model =2.687-1.345×C1+0.704×C2-0.597×C3-0.207×C4; Where, C1 is the Normalized Rb / K of the C unit, which represents the paleo-water depth. The larger the value, the greater the water depth. C2 is the Normalized Salinity of the C unit. nor , represents the salinity of the lake water, the larger the value, the higher the salinity of the lake water; C3 is the Normalized Redox nor , represents the degree of reduction, and the larger the value, the stronger the reducing conditions; C4 is the Normalized C-value of the C unit, which represents the paleoclimate characteristics, and the larger the value, the warmer and more humid the climate.

2. The method for evaluating controlling factors of shale organic matter enrichment according to claim 1, characterized in that: The traditional paleo-sedimentary environmental indicator parameters in step one include paleo-productivity, paleo-redox conditions, paleo-salinity, paleo-climate, terrigenous debris input, and paleo-water depth.

3. A shale organic matter enrichment control factor evaluation system using the shale organic matter enrichment control factor evaluation method according to any one of claims 1 to 2, characterized in that: The evaluation system of controlling factors of shale organic matter enrichment includes: The parameter optimization module is used to optimize the parameters of traditional paleoenvironmental indicators. When multiple indicators jointly characterize paleoenvironmental characteristics, a paleoenvironmental normalization coefficient is proposed, and a normalization method is used to process a data set composed of different element indicators of the controlling factor; when a single indicator indicates paleoenvironmental characteristics, the correlation between the analysis indicator and TOC is used to characterize the control of environmental factors on organic matter; Data preprocessing module, used to normalize different element indicators and provide a good basic data foundation for model establishment based on paleoenvironmental normalization coefficients; Importance analysis module, used for re-normalization preprocessing of single paleoenvironmental index parameters or paleoenvironmental normalization coefficients, and using SPSS software to perform importance analysis of predictive variables on normalized control factor indicators; The TOC correlation model building module is used to select the control factor indicators with importance greater than 0.1, and establish a TOC correlation model with multi-factor comprehensive control based on the measured organic carbon content; The organic matter enrichment factor evaluation module is used to determine the main controlling factors of organic matter enrichment according to the influencing factor coefficients of the TOC correlation model, and then to achieve a comprehensive evaluation of the main controlling factors of organic matter enrichment in black shale.

4. A computer device, characterized in that: The computer device 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 evaluating the controlling factors of shale organic matter enrichment as claimed in any one of claims 1 to 2.

5. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for evaluating control factors of shale organic matter enrichment as claimed in any one of claims 1 to 2.

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