Multi-parameter dynamic coupling hydrothermal activity determinacy calculation and quantitative evaluation method

Abstract

The invention discloses a multi-parameter dynamic coupling hydrothermal activity deterministic calculation and quantitative evaluation method which is used for quantitatively evaluating hydrothermal activity in marine shale. The method comprises the following steps: firstly, collecting a typical marine shale sample, and carrying out systematic experimental test analysis, including TOC determination and element content (ICP-MS, XRF) and mineral composition (XRD, FEM-EDS) analysis, to obtain geochemical and mineralogical data of the sample; thirdly, constructing a hydrothermal influence index (HII), and based on the influence of organic matter development on nonlinear enrichment of metal elements, introducing TOC data to perform dynamic threshold adjustment on HII to form an HIIc model; and the existence of hydrothermal activity is further verified by combining the rare earth element Eu abnormity. The method comprises the following steps: constructing a hydrothermal mineral combination index (HMI) through mineral phase equilibrium constraint, and establishing a hydrothermal contribution rate (HCR) model in combination with multivariate parameters; and finally, according to the HCR value and mineral development characteristics, performing quantitative evaluation on the activity intensity of the hydrothermal solution.

Description

Technical Field

[0001] This invention belongs to the fields of geology, oil and gas exploration and mineral deposit geology, and specifically relates to a method for deterministic calculation and quantitative evaluation of multi-parameter dynamic coupling hydrothermal activity. Background Technology

[0002] Hydrothermal activity is an important fluid-rock interaction process in sedimentary basins, significantly impacting oil and gas reservoir quality, ore-forming material enrichment, and the thermal evolution of shale organic matter. Identification and evaluation of hydrothermal activity in marine shale are crucial topics in petroleum geology and mineral deposit studies. Accurate assessment of hydrothermal activity intensity is of great significance for oil and gas exploration, shale gas reservoir prediction, and the study of mineral deposit genesis.

[0003] Currently, there are three main technical approaches to assessing hydrothermal activity: geochemical index methods, mineralogical identification methods, and reservoir hydrothermal assessment techniques. Hydrothermal activity is a complex physicochemical process in geological history. Therefore, due to the complexity of hydrothermal-sedimentary interactions, these methods have the following limitations: Geochemical index methods mainly rely on elemental ratios (such as Ni-Co-Zn) and Eu anomalies (δEu) for judgment, but do not consider the nonlinear influence of organic matter development on the enrichment of metallic elements in organic-rich shale; mineralogical identification methods use hydrothermal mineral identification for qualitative judgment, but suffer from strong subjectivity and difficulty in quantification. Reservoir hydrothermal assessment techniques, such as the TII index proposed in patent CN202510186075.1, although employing deep learning methods, are only applicable to reservoir units and cannot evaluate non-reservoir shale sections, making them difficult to apply to paleomarine sedimentary environments.

[0004] Therefore, in terms of theoretical foundation, existing technologies suffer from two key deficiencies: First, there is a lack of systematic understanding of the hydrothermal-organic matter interaction mechanism, especially in high-TOC shale, where the specific adsorption of metal elements by organic matter often leads to distortion of geochemical indicators. Second, the application of mineral phase equilibrium theory is insufficient, failing to fully integrate the genetic types and co-occurrence assemblage characteristics of hydrothermal minerals for comprehensive identification. In practical hydrothermal activity assessment, three main problems arise: First, the problem of singular evaluation indicators is prominent; existing methods mostly use isolated geochemical or mineralogical indicators, lacking a multi-parameter synergistic evaluation system, resulting in insufficient interpretability of complex hydrothermal-sedimentary systems. Second, dynamic adaptability is insufficient; existing threshold setting methods cannot adapt to the varying needs of rocks with different TOC contents. Third, regional applicability is limited; existing technologies have not established an effective background value correction mechanism, leading to significant differences in evaluation results across different sedimentary basins. These theoretical and technical deficiencies directly affect the accuracy and reliability of the methods, severely restricting the practical application of hydrothermal activity assessment technology in oil and gas exploration. Summary of the Invention

[0005] To address the technical problems existing in the background art, this invention aims to provide a multi-parameter dynamically coupled deterministic calculation and quantitative evaluation method for hydrothermal activity. It innovatively constructs a complete multi-parameter dynamic evaluation system for hydrothermal activity, overcoming the limitations of existing technologies. The specific innovations are reflected in the following four aspects: First, this invention pioneers the HII-HMI-δEu-TOC multi-parameter dynamic coupling model. Based on geochemical and physicochemical theories, this invention establishes core calculation and correction methods for each parameter, including HII... c Methods such as dynamic threshold correction, hydrothermal mineral assemblage index (HMI), regional background value correction, and hydrothermal contribution rate (HCR) calculation achieve the organic integration of geochemical indicators and mineralogical characteristics. This model innovatively combines hydrothermal elemental differentiation theory, mineral phase equilibrium principle, and organic-inorganic interaction mechanism, overcoming the limitations of single-indicator evaluation. Secondly, this invention develops a dynamic threshold adjustment technique, specifically designing a segmented correction algorithm for organic-poor shale (TOC < 2 wt%) and organic-rich shale (TOC > 2 wt%). By establishing the HII... c The basic compensation model and the high TOC correction model of ΔHII effectively reduce the specific adsorption interference of organic matter on metal elements, improving the accuracy and applicability of the model. Then, this invention establishes a quantitative mineral phase equilibrium constraint mechanism. Based on the genesis and development characteristics of hydrothermal minerals, the hydrothermal mineral content is determined according to whole-rock mineral composition and FEM-EDS. Multi-mineral coupling is used for correction, a mineral assemblage index (HMI) is constructed, a hydrothermal mineral phase equilibrium constraint system is established, and the main development characteristics of hydrothermal minerals are clarified. This achieves accurate quantitative evaluation of hydrothermal minerals, enabling cross-validation between hydrothermal intensity evaluation results and mineral genesis mechanisms, and improving the quantitative evaluation standard for geological interpretation of hydrothermal activity intensity. Finally, this invention develops a standardized background value correction process. This invention proposes using mathematical statistical methods to establish regional geochemical background values, dynamically correcting background value differences in different sedimentary areas, significantly improving the regional applicability of the evaluation method.

[0006] To solve the technical problem, the technical solution of the present invention is as follows:

[0007] A multi-parameter dynamically coupled deterministic calculation and quantitative evaluation method for hydrothermal activity, wherein the method quantitatively evaluates hydrothermal activity in marine shale through multi-parameter dynamic coupling, the method comprising:

[0008] First, typical marine shale samples were collected and subjected to systematic physicochemical analysis, including TOC determination, elemental content analysis, and mineral composition analysis, to obtain geochemical and mineralogical data of the samples. Next, a hydrothermal influence index (HII) was constructed based on the effect of organic matter development on the nonlinear enrichment of metallic elements, and TOC data was incorporated to dynamically adjust the HII threshold, thus forming the HII.c The model was developed; the existence of hydrothermal activity was further verified by combining the Eu anomaly of rare earth elements; the hydrothermal mineral assemblage index (HMI) was constructed by constraining mineral phase equilibrium, and the hydrothermal contribution rate (HCR) model was established by combining multivariate parameters; finally, the intensity of hydrothermal activity was quantitatively evaluated based on the HCR value and mineral development characteristics.

[0009] Furthermore, typical marine shale samples were collected and subjected to systematic experimental testing and analysis, including TOC determination, elemental content and mineral composition analysis, to obtain geochemical and mineralogical data of the samples, specifically including:

[0010] Collect marine shale samples; input geological data related to hydrothermal activity in the study area, distribution of organic shale, and stratigraphic information; identify hydrothermal indicator layers and correlation layers; systematically collect core and outcrop samples to ensure representativeness, integrity, and no contamination; uniformly number and preserve the samples; and output a high-quality set of marine shale samples for subsequent experimental analysis.

[0011] Based on the collected marine shale samples, TOC determination, geochemical composition analysis and mineral composition analysis were performed.

[0012] Organic carbon content analysis (TOC determination): The sample is crushed to 80-200 mesh, and inorganic carbon is removed by soaking in 5% HCl. After drying, the sample is weighed and the total organic carbon (TOC) content is determined by high-temperature combustion or elemental analysis. The TOC data is in wt%.

[0013] Geochemical composition analysis: Major elements were determined using XRF; Trace elements and rare earth element concentrations were determined using ICP-MS.

[0014] Mineral composition analysis: Quantitative analysis of whole-rock minerals was performed using XRD; typical hydrothermal minerals were identified and characterized using FEM-EDS.

[0015] Organic matter index of the output sample:

[0016] TOC content (wt%);

[0017] Geochemical composition data: content of major elements, trace elements, and rare earth elements;

[0018] Mineralogical data: hydrothermal mineral types, abundance, and developmental characteristics;

[0019] The above data serve as a unified basic input for subsequent parametric modeling, used to construct HII and HII. c δEu, HMI, HCR hydrothermal evaluation model.

[0020] Furthermore, the hydrothermal influence index (HII) is constructed based on the effect of organic matter development on the nonlinear enrichment of metal elements, and TOC data is introduced to dynamically adjust the threshold of the HII to form the HII. c The model specifically includes:

[0021] The hydrothermal influence index (HII) was constructed based on the geochemical composition data of the output samples, using the elemental ratios of Ni, Zn, and Co to build a first-order hydrothermal influence index. (Unit: dimensionless); The steady-state solution formula is derived using the diffusion equation and element flux model, taking into account the effects of hydrothermal input and sedimentation; The preliminary hydrothermal influence index HII is output, reflecting the contribution of hydrothermal activity to element enrichment.

[0022] Establish a dynamic threshold adjustment model HII c Based on the calculated HII and TOC data, a TOC correction model is constructed, including HII. c Based on the basic compensation model and the high TOC correction model of ΔHII, the HII threshold is adjusted to obtain HII. c Output the hydrothermal influence index HII after TOC dynamic correction. c It is suitable for shale samples with different organic matter backgrounds.

[0023] Furthermore, a dynamic threshold adjustment model for HII was established. c Specifically, it includes:

[0024] The basic threshold setting is that if hydrothermal activity occurs during the shale deposition period, it can bring a large amount of nutrients, which can modify the organic matter and cause the enrichment of elements Ni and Co. The shale deposited in the hydrothermal deposition area has a significantly higher TOC content than the shale that was not modified by hydrothermal activity during the same period.

[0025] The method for establishing a dynamic baseline threshold TOC compensation model for HII is as follows:

[0026] HII cb =1.5 + 0.2log 10 (TOC), (TOC≥0.5)

[0027] Among them, 1.5 is the baseline threshold for hydrothermal activity in the modern ocean background, with a TOC of approximately 0.5 wt%; 0.2 is the experimentally calibrated threshold that increases by 0.2 when the TOC increases by one order of magnitude, determined by regression analysis using hydrothermal vent data; the logarithmic relationship reflects the nonlinear effect of organic matter content on the enrichment of metal elements;

[0028] A hydrothermal alteration TOC correction model is established. Due to the complex relationship between hydrothermal activity and shale TOC enrichment, Ni and Zn elements in organic-rich shale (TOC > 2 wt%) are more easily retained in the organic matter pores, resulting in metal enrichment. Therefore, it is necessary to correct the HII threshold for high-TOC shale to increase the HII threshold and avoid misjudgment. The correction method is as follows:

[0029] ΔHII = 0.15 × (TOC - 2), (TOC > 2)

[0030] The coefficient 0.15 was determined through isothermal adsorption experiments;

[0031] Establish a hierarchical HII c The dynamic threshold adjustment model is as follows:

[0032]

[0033] The TOC unit is wt%.

[0034] Furthermore, the Eu anomaly associated with rare earth elements further verifies the existence of hydrothermal activity, specifically including:

[0035] Based on the rare earth element content data output above, using or Calculate the Eu anomaly and analyze the Eu in the hydrothermal system by combining the Eu valence behavior. 2+ The enrichment mechanism outputs δEu anomalies, serving as an independent geochemical criterion for hydrothermal activity.

[0036] Furthermore, the construction of the hydrothermal mineral assemblage index (HMI) through mineral phase equilibrium constraints specifically includes:

[0037] Based on the hydrothermal mineral content data output above, mineral weights are assigned (according to mineral indicativeness and stability), and nonlinear correction is performed using the tanh(x) function to remove outliers. The HMI is calculated by combining regional sedimentary background values ​​and mineral assemblages, and the hydrothermal mineral assemblage index HMI is output to quantitatively reflect the relationship between mineral development and hydrothermal activity intensity.

[0038] Furthermore, the hydrothermal mineral phase equilibrium constraints and HMI index construction specifically include:

[0039] Calculate the total content of typical hydrothermal minerals in shale samples:

[0040] Input mineral content data obtained through whole-rock mineral composition and FEM-EDS analysis, and identify and calculate the total content of typical hydrothermal minerals based on mineralogy principles and characteristics of hydrothermal mineralization systems;

[0041] C hydro,i =wi ×100wt%;

[0042] Among them, C hydro,i The total content of type i hydrothermal minerals in the shale sample is expressed in wt%; i The content of the i-th hydrothermal mineral is determined by whole-rock mineral composition and FEM-EDS testing; i represents the type and quantity of hydrothermal minerals (e.g., pyrite, sphalerite, barite), and the total content of hydrothermal minerals is output (unit: wt%), providing a basis for subsequent HMI index calculation;

[0043] Determine the weight values ​​for various hydrothermal minerals:

[0044] Input the results of mineral genesis, content, and stability analysis. Based on the relationship between the strength of mineral indication of hydrothermal activity and the factors of mineral genesis, stability, and content, assign weights to different minerals. Weight assignment: pyrite (0.5), barite (0.3), sphalerite (0.2). Output the weight values ​​of each hydrothermal mineral for HMI calculation.

[0045] A hydrothermal mineral weight correction factor is established, and the above weight factors are corrected using the following method:

[0046] First, the relationship between hydrothermal mineral content and hydrothermal intensity exhibits two key characteristics: nonlinear response (mineral content gradually tends to saturate in high-value areas) and geochemical difference response. Therefore, in the quantitative evaluation process, it is necessary to avoid the excessive influence of outliers on the model and to eliminate regional geochemical background differences. Specific methods include:

[0047] A hyperbolic tangent function tanh(x) is introduced, which is continuous, nonlinear and has a finite range [0,1), where x is the correction outlier;

[0048] Statistical analysis of hydrothermal mineral content (C) in all shale samples from the study area i Calculate the median M and the median absolute deviation MAD, and then... i After removing outliers >3MAD, the background value C of hydrothermal mineral content in the study area was obtained. bac The method is as follows:

[0049] C bac =M(C i )±MAD(C i )

[0050] w is obtained by multi-mineral coupling correction. i For samples containing multiple hydrothermal minerals, the correction method is as follows:

[0051]

[0052] This method can be further analyzed for errors, and the analysis method is as follows:

[0053]

[0054] Experience shows that when n>20, based on the central limit theorem, the relative error is <5%, therefore the number of samples involved n>20 is required. When n>20, the model has high applicability.

[0055] Finally, the mineral assemblage index HMI was constructed to establish hydrothermal mineral phase equilibrium constraints. The method is as follows:

[0056]

[0057] Where m represents the specific types and quantities of hydrothermal minerals (such as pyrite, sphalerite, barite, etc.).

[0058] Furthermore, the establishment of a hydrothermal contribution rate (HCR) model by combining multivariate parameters includes:

[0059] Based on the above output HII c Based on δEu, HMI, and TOC data, a quaternary parameter set is constructed. Principal component analysis (PCA) is used to calculate the parameter weights A, B, C, and D, then the hydrothermal contribution rate is calculated, and finally, uncertainty is assessed, outputting the HCR value ±u. HCR That is, the contribution of hydrothermal activity to each sample and the error range.

[0060] Furthermore, a hydrothermal contribution rate (HCR) model is established, specifically including:

[0061] A parameter set model is established, and based on the above multivariate parameter indicators, a comprehensive deterministic calculation method for hydrothermal activity is established. The hydrothermal contribution rate (HCR) evaluation equation is fitted, and the equation is as follows:

[0062]

[0063] Principal component analysis (PCA) was used to calculate the weights of shale sample parameters and determine the weights A, B, C, and D of HII, δEu, HMI, and TOC parameters.

[0064] First, construct the parameter matrix X:

[0065]

[0066] Standardization process: Among them, u j δ is the background value of the j-th parameter. j Let be the standard deviation of the j-th parameter;

[0067] Calculate using the covariance matrix:

[0068]

[0069] Perform eigenvalue decomposition, ∑v=vλ, to obtain eigenvalues ​​λ and eigenvectors v1;

[0070] Calculate the variance contribution rate.

[0071] The weights of each parameter are determined, and the weights are calculated as follows:

[0072] The weight values ​​are normalized, and the calculation method is as follows:

[0073] The hydrothermal contribution rate (HCR) of each shale sample was calculated using a multivariate parameter model.

[0074] The method for uncertainty assessment is as follows:

[0075]

[0076] The final HCR output, and its final expression result are as follows:

[0077]

[0078] Furthermore, the quantitative evaluation of hydrothermal activity intensity based on HCR values ​​and mineral development characteristics specifically includes:

[0079] Based on the output HCR values ​​and the identified mineral development characteristics, an HCR grading standard is established. Combined with mineral assemblages, the intensity levels of hydrothermal activity are classified (none – weak – moderate – strong – extremely strong). Evaluation tables or diagrams are output to serve oil and gas exploration and mineral deposit genetic research. A hydrothermal activity intensity level classification and geological interpretation report is generated, and finally, a quantitative evaluation of hydrothermal activity driven by multiple parameters is completed.

[0080] Compared with the prior art, the advantages of the present invention are as follows:

[0081] To address the limitations of existing technologies that only assess hydrothermal activity in reservoirs, this invention breaks through the reservoir unit limitation and establishes a method for assessing hydrothermal activity applicable to non-reservoir segments. It also develops a hydrothermal activity assessment system for paleooceanic environments and provides a solution for quantifying its intensity. Furthermore, hydrothermal activity is a highly complex physicochemical process. Given the current reliance on single parameters for assessing reservoir hydrothermal activity intensity, this invention integrates multiple geochemical parameters (including key geochemical indicators such as Ni / Zn / Co, TOC, and δEu) and the types, genesis, and typological characteristics of hydrothermal minerals (including pyrite, barite, and sphalerite). Based on the physicochemical mechanisms of hydrothermal activity, it further establishes a hydrothermal activity assessment model, strengthening the theoretical basis in geochemistry, mineralogy, and physicochemicals, thereby improving the theoretical consistency rate of hydrothermal activity assessment in paleooceanic environments.

[0082] TOC (Total Organic Carbon) is a key evaluation indicator affecting reservoir quality. The development of organic matter leads to nonlinear enrichment of hydrothermal metals. However, existing techniques do not consider organic matter-related parameters in their parameter selection, making them unsuitable for evaluating hydrothermal activity under different TOC content backgrounds. This invention constructs a dynamic TOC correction model, introducing TOC to perform segmented correction for organic-poor shale (TOC < 2 wt%) and organic-rich shale (TOC > 2 wt%), and establishes a Hi-Index (HII) model. c A dynamic threshold adjustment model enables accurate correction of the Hydrothermal Influence Index (HII) for shale with TOC ≥ 0.5 wt%, eliminating the interference of TOC on hydrothermal activity assessment. This significantly improves the accuracy of hydrothermal activity assessment based on measured experimental data from Cambrian shale in the Sichuan Basin.

[0083] Hydrothermal activity inevitably influences mineral types and associated assemblages, thus hydrothermal minerals can serve as an effective indicator for assessing hydrothermal activity. However, existing technologies lack mineralogical constraints. This invention constructs a hydrothermal mineral assemblage index (HMI) system. By integrating mineralogical evidence such as hydrothermal mineral types, morphology, and content, a hydrothermal mineral facies equilibrium constraint model is established, enabling cross-validation between hydrothermal intensity assessment results and mineral genesis mechanisms, thereby improving the quantitative evaluation standard for the geological interpretation of hydrothermal activity intensity. Furthermore, given the complex tectonic evolution history of different sedimentary basins and the strong heterogeneity of shale development, existing technologies may not be applicable to different sedimentary regions. To reduce evaluation errors caused by differences in geochemical background, this invention proposes a solution using mathematical statistical methods to establish regional geochemical background values. This dynamically corrects for differences in background values ​​across different sedimentary facies zones, significantly improving the regional applicability of the evaluation method.

[0084] Meanwhile, the dynamic weight allocation of the multi-parameter coupled dynamic evaluation method of the present invention adopts mathematical geological methods to ensure the objectivity of the weights, and the error of each parameter is independently quantified. The evaluation results are accurate and objective, significantly improving the consistency rate with geological phenomena, and have repeatability and scientificity. Attached Figure Description

[0085] Figure 1 The main technical roadmap of this invention;

[0086] Figure 2 FEM-EDS characteristics of samples Q-178 and Q-156; Figure 2 Sample A, Q-178, is associated with barite veins and euhedral to subhedral pyrite. Figure 2 Sample B, Q-178, exhibits abundant euhedral to subhedral pyrite. Figure 2 a- Figure 2 Energy spectrum of barite in A; Figure 2 b- Figure 2 Energy spectrum of pyrite in B; Figure 2 In sample C-Q-156, strawberry-like pyrite is abundant, and locally it appears as banded pyrite. Figure 2 Image showing the development of strawberry pyrite in sample Q-156 (D-sample). Detailed Implementation

[0087] The specific implementation of the present invention is described below with reference to embodiments:

[0088] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0089] Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0090] Example 1:

[0091] Existing technologies focus on hydrothermal intensity indices within reservoirs, neglecting methods for evaluating hydrothermal intensity in non-reservoir sections and lacking assessment of hydrothermal activity in paleooceanic environments. This invention employs a multivariate dynamic parameter coupling method to conduct quantitative evaluation and deterministic calculations of hydrothermal activity in shale sections, applicable to paleooceanic hydrothermal assessment. Furthermore, existing technologies lack geochemical and mineralogical theoretical support, relying solely on reservoir-related parameters and failing to explain the physicochemical mechanisms of hydrothermal activity. This invention organically integrates geochemical theory, mineral development characteristics, and dynamic calibration models through a multivariate parameter dynamic coupling method, improving the theoretical consistency rate of paleooceanic hydrothermal activity assessment.

[0092] Existing technologies use a single threshold to determine reservoir hydrothermal intensity, failing to consider actual hydrothermal activity under varying TOC content. This invention addresses this by establishing a dynamic TOC compensation model and a high TOC correction model, eliminating the specific adsorption effect of organic matter on metal elements and reducing errors in hydrothermal activity evaluation.

[0093] Existing technologies lack mineralogical constraints and do not consider the influence of hydrothermal activity on rock mineral composition, structure, and mineral assemblages. This invention, based on the genesis and development characteristics of hydrothermal minerals, determines hydrothermal mineral content according to whole-rock mineral composition and FEM-EDS, employs multi-mineral coupling for correction, constructs a mineral assemblage index (HMI), establishes a hydrothermal mineral phase equilibrium constraint system, and clarifies the main developmental characteristics of hydrothermal minerals.

[0094] The lack of background value error correction analysis in existing technologies leads to significant analytical errors in different sedimentary regions. This invention utilizes mathematical statistical analysis methods to establish background value correction and quantify uncertainty, obtaining background values ​​for hydrothermal mineral content in the study area. This eliminates regional differences in geochemical background, improving model stability and regional applicability.

[0095] In summary, as Figure 1 As shown, this invention proposes a deterministic calculation method and a quantitative evaluation method for hydrothermal activity based on multi-parameter dynamic coupling, specifically including the following steps:

[0096] Step 1: Collect marine shale samples.

[0097] When conducting quantitative evaluation and deterministic calculations of hydrothermal activity, it is necessary to first select typical marine shale samples. Sample collection must strictly adhere to all test specifications. The specific steps are as follows:

[0098] 1. First, select the organic matter-enriched shale layers for research, conduct detailed interpretation of the stratigraphy in the study area, further select the hydrothermal activity indicator layers in the study area, and establish correlation layers, which are mainly the upper and lower layers in contact with the target layer.

[0099] 2. Conduct systematic sampling of outcrops and drilling cores. During outcrop sampling, the surface weathering layer must first be removed, and samples should be taken along the bedding planes. Core and outcrop samples should weigh ≥500g and ensure the presence of intact blocks ≥1cm×1cm×1cm to meet laboratory analysis requirements. Sampling is prohibited in fault fracture zones or areas with calcite veins to avoid picking up fragmented samples and preventing contamination. The sampling accuracy should be 0.2-0.5m. At the target stratum, sampling can be more frequent, with one duplicate sample inserted for every 10 samples to verify sampling uniformity. Collected samples should be placed in dry, sealed double-layered polyethylene sample bags to prevent cross-contamination. For sulfide-enriched samples, ceramic tools should be used for sampling to avoid metal contamination. All samples should be carefully labeled and marked, with special samples packaged and labeled separately.

[0100] Step 2: Experimental analysis.

[0101] 1. Experimental Data Testing. An agate mortar and pestle was used for grinding to avoid metal contamination. The sample was crushed to 80 mesh, sieved, and the middle particle size fraction was collected to avoid enrichment of coarse minerals. 100 mg of the sample was weighed and soaked in 5% HCl for 24 hours to remove inorganic carbon, and the organic carbon content was determined. The same method was used to crush the sample particles to 200 mesh before testing. During the testing process, 50 mg of dried and constant-weight sample powder was first weighed into a dissolution bottle, and the sample was chemically pretreated using a mixed strong acid under high temperature and high pressure. Major elements were analyzed using X-ray fluorescence spectrometry, and trace and rare earth element determinations were performed using ICP-MS based on the solution method.

[0102] 2. Mineral Composition and Characterization Analysis. First, the shale sample was ground into powder. 5g of the powder was weighed for testing using a K40522 X-ray diffractometer to determine the whole-rock mineral composition. The intact sample was then cut into 1cm × 1cm × 1cm pieces, argon-ion polished, and further analyzed using FEM-EDS for mineral composition identification. Combining these two methods, the contents of typical hydrothermal minerals, including pyrite, sphalerite, and barite, were obtained.

[0103] Step 3: Establish a primary hydrothermal influence index.

[0104] 1. A First-Level Hydrothermal Influence Index (HII) was established based on experimental data. According to the "Enrichment Laws of Metal Elements in Submarine Hydrothermal Systems," the solubility of Ni and Zn in high-temperature hydrothermal fluids (>250℃) is 1-2 orders of magnitude higher than that of Co, leading to a significant increase in the Ni / Co and Zn / Co ratios in hydrothermal sediments. Based on Ni and Zn elements closely related to submarine hydrothermal activity in shale, and Co elements mainly originating from aqueous sedimentary environments, a basic evaluation of the HII was established. The method is as follows:

[0105]

[0106] Coefficient 10 -3 The numerical values ​​are normalized to a range of 0.1-10 for engineering applications.

[0107] 2. The method for calculating the ion diffusion coefficient in high-temperature fluids using the Stokes-Einstein equation is as follows:

[0108]

[0109] Wherein, the Di ion diffusion coefficient; k B Boltzmann constant; T is temperature; η is fluid viscosity in Pa·s; r i denoted as ion hydration radius.

[0110] 3. Based on the element mass conservation equation, establish an element flux model for hydrothermal-oceanic sedimentary systems. The method is as follows:

[0111]

[0112] Where Ci is the concentration of element i (Ni, Co, Zn), in ppm; Di is the diffusion coefficient of element i (in D... Ni =1.2×10 -6 cm 2 / s,D Zn =0.8×10 -6 cm 2 / s); Q hydro,i Hydrothermal input flux, in mol / m³ 2 / yr;λ sed,i The depositional burial attenuation index is λsed, where λsed, In the formula, v is the deposition rate, in cm / kyr; L is the burial depth, in m; λ is the ratio of the concentration of elements in the surface layer to the concentration of elements in the deeper layers. sed,Ni =0.75×λ sed,Co The calibration result for this test is λ. sed,Co =0.02Ma -1 ,λ sed,Ni =0.015Ma -1 .

[0113] 4. The steady-state solution used for deriving the HII exponent is used for error verification, and the method is as follows:

[0114]

[0115] Step 4: Establish a dynamic threshold adjustment model for HII. c .

[0116] 1. Basic threshold setting. During shale deposition, if hydrothermal activity occurs, it can bring a large amount of nutrients, modifying organic matter and enriching elements Ni and Co. Shale deposited in hydrothermal areas has a significantly higher TOC content compared to shale that was not modified by hydrothermal activity during the same period.

[0117] 2. Establish a dynamic basic threshold TOC compensation model for HII, the method of which is as follows:

[0118] HII cb =1.5 + 0.2log 10 (TOC), (TOC≥0.5)

[0119] Among them, 1.5 is the baseline threshold for modern marine background hydrothermal activity, with a TOC of approximately 0.5 wt%; 0.2 is the threshold for experimental calibration that increases by 0.2 when the TOC increases by one order of magnitude, determined by regression analysis using hydrothermal zone data; the logarithmic relationship reflects the nonlinear effect of organic matter content on the enrichment of metal elements.

[0120] 3. Establish a hydrothermal alteration TOC correction model. Due to the complex relationship between hydrothermal activity and shale TOC enrichment, Ni and Zn elements in organic-rich shale (TOC > 2 wt%) are more easily retained in the organic matter pores, resulting in metal enrichment. Therefore, it is necessary to correct the HII threshold for high-TOC shale to increase the HII threshold and avoid misjudgment. The correction method is as follows:

[0121] ΔHII = 0.15 × (TOC - 2), (TOC > 2)

[0122] The coefficient 0.15 was determined through isothermal adsorption experiments.

[0123] 4. Establish a hierarchical HII dynamic threshold adjustment model, specifically as follows:

[0124]

[0125] The TOC unit is wt%.

[0126] Step 5: Establish a geochemical model of the Eu anomaly.

[0127] 1. In geochemistry, Eu is a variable valence element; under reducing conditions, some Eu... 3+ Restore to Eu 2+ Due to differences in valence and ionic radius, Eu 2+ Compared with other REEs 3+ Separation occurs, i.e., an Eu anomaly occurs in the Eu system. 2+ The enrichment mechanism is as follows:

[0128]

[0129] Eu anomalies are represented by δEu, and their calculation method is as follows:

[0130] or

[0131] Among them, [Eu] N [Sm] N [Gd] N All values ​​are chondrite standard values. Eu exhibits different partition coefficients in minerals, meaning different minerals will influence the distribution of Eu. 2+ and Eu 3+ Selective enrichment occurs, with the partition coefficient in plagioclase often being greater than that in other minerals. δEu>1 indicates a positive hydrothermal anomaly; δEu<1 indicates a negative anomaly.

[0132] Step 6: Establish hydrothermal mineral phase equilibrium constraints.

[0133] 1. Calculate the total content of typical hydrothermal minerals in shale samples. Based on mineralogical principles and the characteristics of hydrothermal mineralization systems, pyrite, as a typical sulfide mineral, can reflect the intensity of hydrothermal activity through its hydrothermal origin type (e.g., cubic or pentagonal dodecahedral crystal form), but it needs to be distinguished from sedimentary diagenetic origin (e.g., strawberry aggregates). Barite formation requires the interaction of barium-rich hydrothermal fluids with sulfates (usually from seawater), making it an important indicator of sulfate fluid involvement in hydrothermal systems; however, it can also form through non-hydrothermal sedimentary processes. Sphalerite mainly forms in low-to-medium temperature hydrothermal environments (150-300℃), and its iron content (color depth) can further indicate the specific mineralization temperature, making it an effective indicator for tracking low-to-medium temperature hydrothermal activity. Gypsum typically represents the low-temperature alteration products (<100℃) of late-stage hydrothermal systems, formed by the reaction of anhydrite hydration or sulfide oxidation with calcareous host rocks. It can reflect the evolution of hydrothermal fluids, but due to its high solubility, the impact of later alteration needs to be carefully considered during interpretation. To accurately interpret the hydrothermal indicative significance of these minerals, a comprehensive judgment must be made combining mineral assemblage, structural characteristics (such as hydrothermal vein structures), and geochemical indicators (such as sulfur isotope composition). Therefore, based on whole-rock mineral composition analysis and FEM-EDS analysis, combined with a comprehensive judgment of mineral assemblage, structural characteristics (such as hydrothermal vein structures), and geochemical indicators, the mineral assemblage was determined to be pyrite + barite + sphalerite. The hydrothermal mineral content was then obtained using the following method:

[0134] C hydro,i =w i ×100wt%;

[0135] Among them, C hydro,i The total content of type i hydrothermal minerals in the shale sample is expressed in wt%;i The content of the i-th hydrothermal mineral (based on whole-rock mineral composition and FEM-EDS analysis).

[0136] 2. Determine the weight values ​​of various hydrothermal minerals. Based on the relationship between the strength of each hydrothermal mineral's indication of hydrothermal activity, and according to the three principles of mineral genesis (the irreplaceable role of minerals as indicators of hydrothermal activity), mineral content (the quantitative relationship between mineral content and hydrothermal intensity), and mineral stability (the stability of minerals in later geological processes), combined with the above geochemical indicators, the accuracy of the judgment is determined to be: pyrite > barite > sphalerite. Therefore, using a weighted comprehensive evaluation method, the weights of the three minerals are assigned to 0.5, 0.3, and 0.2 respectively (the assignment basis is shown in Table 1).

[0137] Table 1 - Principles for Determining the Weights of Hydrothermal Minerals

[0138]

[0139] 3. Establish hydrothermal mineral weight correction factors. Since the relationship between mineral content and hydrothermal intensity is not a simple linear one, and shale mineral content varies significantly under different sedimentary backgrounds, and it is necessary to minimize the interference of later weathering and metamorphism on mineral content, the aforementioned weight factors need to be corrected. The correction method is as follows:

[0140] First, the relationship between hydrothermal mineral content and hydrothermal intensity exhibits two key characteristics: nonlinear response (mineral content gradually approaches saturation in high-value areas) and geochemical difference response. Therefore, in the quantitative evaluation process, it is necessary to avoid the excessive influence of outliers on the model and to eliminate regional geochemical background differences. Specific methods include:

[0141] A hyperbolic tangent function tanh(x) is introduced, which is continuous, nonlinear and has a finite range [0,1), where x is the correction outlier;

[0142] Statistical analysis of hydrothermal mineral content (C) in all shale samples from the study area i Calculate the median M and the median absolute deviation MAD, and then... i After removing outliers >3MAD, the background value C of hydrothermal mineral content in the study area was obtained. bac The method is

[0143] C bac =M(C i )±MAD(C i )

[0144] w is obtained by multi-mineral coupling correction. i For samples containing multiple hydrothermal minerals, the correction method is as follows:

[0145]

[0146] This method can be further analyzed for errors, and the analysis method is as follows:

[0147]

[0148] Experience shows that when n>20, based on the central limit theorem, the relative error is <5%, therefore requiring the number of samples involved, n>20. When n>20, the model has high applicability.

[0149] 4. Finally, the mineral assemblage index HMI is constructed to establish hydrothermal mineral phase equilibrium constraints. The method is as follows:

[0150]

[0151] Where m represents the specific types and quantities of hydrothermal minerals (including pyrite, sphalerite, barite, etc.).

[0152] Step 7: Establish a deterministic calculation method for hydrothermal activity with multi-parameter dynamic coupling.

[0153] 1. Establish a parameter set model. Based on the above multivariate parameter indicators, establish a comprehensive deterministic calculation method for hydrothermal activity, and fit the hydrothermal contribution rate (HCR) evaluation equation, which is as follows:

[0154]

[0155] 2. Principal component analysis (PCA) was used to calculate the weights of the shale sample parameters and determine the weights A, B, C, and D of the parameters HII, δEu, HMI, and TOC.

[0156] First, construct the parameter matrix X:

[0157]

[0158] Standardization process: Among them, u j δ is the background value of the j-th parameter. j Let be the standard deviation of the j-th parameter.

[0159] Calculate using the covariance matrix:

[0160]

[0161] Perform eigenvalue decomposition, ∑v=vλ, to obtain eigenvalues ​​λ and eigenvectors v1;

[0162] Calculate the variance contribution rate.

[0163] The weights of each parameter are determined, and the weights are calculated as follows:

[0164] The weight values ​​are normalized, and the calculation method is as follows:

[0165] 3. The hydrothermal contribution rate (HCR) of each shale sample was calculated using a multivariate parameter model.

[0166] 4. Conduct uncertainty assessment, the method of which is as follows:

[0167]

[0168] 5. Output the final HCR, the final expression result is as follows:

[0169]

[0170] Step 8: Establish a quantitative evaluation of hydrothermal activity with multi-parameter dynamic coupling.

[0171] 1. Based on the above scheme, and combined with HCR and mineral development characteristics, establish quantitative evaluation criteria for geological interpretation (Table 2):

[0172] Table 2 Quantitative Evaluation Criteria for Geological Interpretation of Hydrothermal Activity Intensity

[0173]

[0174]

[0175] 2. Complete the quantitative evaluation.

[0176] Example 2:

[0177] This embodiment 2 is specifically applied to embodiment 1, and a specific example analysis is performed:

[0178] Step 1: Obtain relevant parameters through experimental testing.

[0179] Taking the Cambrian Chizhusi Formation marine shale in the Upper Yangtze region as an example, the target stratigraphic segment Chizhusi-1 was selected in the study area. 200 typical samples of the hydrothermal segment and 60 non-hydrothermal samples of the upper Chizhusi-1-2 sub-segment were obtained. The above-mentioned experimental tests were carried out to obtain relevant data such as geochemistry, TOC, and minerals.

[0180] Step 2: Geochemical parameter calculation.

[0181] Taking typical samples Q-178 and Q-156 as examples, with Q-178 having a TOC of 4.2 wt% and Q-156 having a TOC of 1.2 wt%, parameter calculations were performed, and the basic information of the samples is shown in Table 3.

[0182] Table 3. Basic Information of Example Samples Q-178 and Q-156

[0183]

[0184] 1. Perform HII calculation on the sample:

[0185] Sample Q-178 (TOC = 4.2wt% > 2wt%):

[0186]

[0187] Sample Q-156 (TOC = 1.2wt% < 2wt%):

[0188]

[0189] 2. Perform HII c Dynamic threshold correction:

[0190] HII C-178 =1.5 + 0.2log 10 4.2 + 0.15 × (4.2 - 2) = 1.955

[0191] HII C-156 =1.5 + 0.2log 10 1.2 = 1.516

[0192] 3. Calculation of δEu:

[0193]

[0194] Step 3: Perform hydrothermal mineral phase equilibrium constraints.

[0195] 1. The median (M) and median deviation (MAD) of hydrothermal mineral (including pyrite, barite, and sphalerite) contents of 260 samples were statistically analyzed, and the background values ​​of mineral contents were calculated. The statistical results are shown in Table 4.

[0196] Table 4. Determination of hydrothermal mineral background values ​​for example samples.

[0197]

[0198] 2. Perform sample HMI calculation. First, perform hyperbolic tangent transformation and correction, then perform HMI calculation:

[0199] MHI = 0.5 × tanh(w py ′)+0.3×tanh(w Ba ′)+0.2×tanh(w sp ′)

[0200] Sample Q-178 (TOC = 4.2wt% > 2wt%):

[0201] HMI 178 =0.5×0.996+0.3×0.869+0.2×0.999=0.956

[0202] Sample Q-156 (TOC = 1.2wt% < 2wt%):

[0203] HMI 156 =0.5 × 0.985 = 0.493

[0204] Step 4: Perform deterministic calculations of hydrothermal activity with multi-parameter dynamic coupling.

[0205] 1. Use principal component analysis (PCA) to calculate the weights of the sample parameters and determine the weights A, B, C, and D of the parameters HII, δEu, HMI, and TOC.

[0206] First, construct the parameter matrix X:

[0207]

[0208] Standardize the samples:

[0209] Perform covariance matrix calculation:

[0210]

[0211] Eigenvalue decomposition:

[0212] The eigenvalues ​​λ = [2.17, 1.03, 0.65, 0.15];

[0213] The first principal component eigenvector v1 = [0.61, 0.53, 0.37, 0.08] T ;

[0214] Perform weight calculation:

[0215] The weight values ​​are normalized, and the calculation method is as follows:

[0216] The weights A, B, C, and D of the normalized HII, δEu, HMI, and TOC parameters:

[0217] Where A = 0.5; B = 0.3; C = 0.15; D = 0.05.

[0218] 2. Calculation of hydrothermal contribution rate (HCR).

[0219] Sample Q-178 (TOC = 4.2wt% > 2wt%):

[0220] HCR 178 =0.5×1.955+0.3×1.04+0.15×0.956+0.05×2.1=1.23 Perform uncertain calculations for each parameter:

[0221]

[0222] HCR 178 =1.23±0.04

[0223] Similarly, sample Q-156 (TOC = 1.2wt% < 2wt%):

[0224] MCR 156 =0.5×1.516+0.3×0.88+0.15×0.493+0.05×0.6=0.32

[0225] HCR 156 =0.32±0.04

[0226] Step 5: Determine the mineral development characteristics of the sample and verify the evaluation results.

[0227] Mineral development characteristics of samples Q-178 and Q-156 were analyzed using FEM-EDS to verify whether they conformed to the hydrothermal mineral development pattern. The results are as follows: Figure 2 As shown. Sample Q-178 contains a pyrite + barite assemblage (…). Figure 2 A, 2B, 2a, 2b), pyrite is mostly euhedral and subhedral (A, 2B, 2a, 2b). Figure 2 A, 2B), mostly hydrothermal mineral characteristics; strawberry-like pyrite is mainly developed in sample Q-156 ( Figure 2 The content of euhedral pyrite is relatively low (C,2D), and most of it is sedimentary pyrite.

[0228] Step 6: Complete the evaluation of hydrothermal activity intensity.

[0229] Based on the quantitative evaluation criteria for the geological interpretation of hydrothermal activity intensity and combined with mineral development characteristics, sample Q-178 reflects extremely strong hydrothermal activity, with barite veins and pyrite development; Q-156 reflects weak hydrothermal activity, mainly with strawberry pyrite development. Of the 260 samples, 48 ​​samples from the Qiong-1 sub-member reflect extremely strong hydrothermal activity (18.5%), 72 samples reflect strong hydrothermal activity (27.7%), 55 samples reflect moderate hydrothermal activity (21.2%), and 85 samples reflect no to weak hydrothermal activity (32.7%). The extremely strong hydrothermal activity layer is located at the bottom of the Qiong-1 sub-member, and the no to weak hydrothermal layer is located at the top of the Qiong-1 sub-member and the overlying Qiong-12 sub-member, consistent with the mineral development pattern. Therefore, hydrothermal activity gradually weakens from the Qiong-1 sub-member to the Qiong-12 sub-member.

[0230] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

[0231] Many other changes and modifications can be made without departing from the concept and scope of this invention. It should be understood that this invention is not limited to the specific embodiments, and the scope of this invention is defined by the appended claims.

Claims

1. A method for deterministic calculation and quantitative evaluation of multi-parameter dynamically coupled hydrothermal activity, characterized in that, The method quantitatively evaluates hydrothermal activity in marine shale through multi-parameter dynamic coupling. The method includes: First, typical marine shale samples were collected and subjected to systematic physicochemical analysis, including TOC determination, elemental content analysis, and mineral composition analysis, to obtain geochemical and mineralogical data of the samples. Next, a hydrothermal influence index (HII) was constructed based on the effect of organic matter development on the nonlinear enrichment of metallic elements, and TOC data was incorporated to dynamically adjust the HII threshold, thus forming the HII. c The model was developed; the existence of hydrothermal activity was further verified by combining the Eu anomaly of rare earth elements; the hydrothermal mineral assemblage index (HMI) was constructed by constraining mineral phase equilibrium, and the hydrothermal contribution rate (HCR) model was established by combining multivariate parameters; finally, the intensity of hydrothermal activity was quantitatively evaluated based on the HCR value and mineral development characteristics.

2. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamic coupling hydrothermal activity according to claim 1, characterized in that, Typical marine shale samples were collected and subjected to systematic experimental testing and analysis, including TOC determination, elemental content and mineral composition analysis, to obtain geochemical and mineralogical data of the samples, specifically including: Collect marine shale samples; input geological data related to hydrothermal activity in the study area, distribution of organic shale, and stratigraphic information; identify hydrothermal indicator layers and correlation layers; systematically collect core and outcrop samples to ensure representativeness, integrity, and no contamination; uniformly number and preserve the samples; and output a high-quality set of marine shale samples for subsequent experimental analysis. Based on the collected marine shale samples, TOC determination, geochemical composition analysis and mineral composition analysis were performed. Organic carbon content analysis (TOC determination): The sample is crushed to 80-200 mesh, and inorganic carbon is removed by soaking in 5% HCl. After drying, the sample is weighed and the total organic carbon (TOC) content is determined by high-temperature combustion or elemental analysis. The TOC data is in wt%. Geochemical composition analysis: Major elements were determined using XRF; Trace elements and rare earth element concentrations were determined using ICP-MS. Mineral composition analysis: Quantitative analysis of whole-rock minerals was performed using XRD; typical hydrothermal minerals were identified and characterized using FEM-EDS. Organic matter index of the output sample: TOC content (wt%); Geochemical composition data: content of major elements, trace elements, and rare earth elements; Mineralogical data: hydrothermal mineral types, abundance, and developmental characteristics; The above data serve as a unified basic input for subsequent parametric modeling, used to construct HII and HII. c δEu, HMI, HCR hydrothermal evaluation model.

3. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamic coupling hydrothermal activity according to claim 1, characterized in that, The constructed hydrothermal influence index (HII) is based on the effect of organic matter development on the nonlinear enrichment of metal elements, and incorporates TOC data to dynamically adjust the threshold of the HII, thus forming the HII. c The model specifically includes: The hydrothermal influence index (HII) was constructed based on the geochemical composition data of the output samples, using the elemental ratios of Ni, Zn, and Co to build a first-order hydrothermal influence index. (Unit: dimensionless); The steady-state solution formula is derived using the diffusion equation and element flux model, taking into account the effects of hydrothermal input and sedimentation; The preliminary hydrothermal influence index HII is output, reflecting the contribution of hydrothermal activity to element enrichment. Establish a dynamic threshold adjustment model HII c Based on the calculated HII and TOC data, a segmented correction model for TOC is constructed, including HII. c Based on the basic compensation model and the high TOC correction model of ΔHII, the HII threshold is adjusted to obtain HII. c Output the hydrothermal influence index HII after TOC dynamic correction. c It is suitable for shale samples with different organic matter backgrounds.

4. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamic coupling hydrothermal activity according to claim 3, characterized in that, Establish a dynamic threshold adjustment model for HII. c Specifically, it includes: The basic threshold setting is that if hydrothermal activity occurs during the shale deposition period, it can bring a large amount of nutrients, which can modify the organic matter and cause the enrichment of elements Ni and Co. The shale deposited in the hydrothermal deposition area has a significantly higher TOC content than the shale that was not modified by hydrothermal activity during the same period. The method for establishing a dynamic baseline threshold TOC compensation model for HII is as follows: HII cb =1.5+0.2log 10 (TOC),(TOC≥0.5) Among them, 1.5 is the baseline threshold for hydrothermal activity in the modern ocean background, with a TOC of approximately 0.5 wt%; 0.2 is the experimentally calibrated threshold that increases by 0.2 when the TOC increases by one order of magnitude, determined by regression analysis using hydrothermal vent data; the logarithmic relationship reflects the nonlinear effect of organic matter content on the enrichment of metal elements; A hydrothermal alteration TOC correction model is established. Due to the complex relationship between hydrothermal activity and shale TOC enrichment, Ni and Zn elements in organic-rich shale (TOC > 2 wt%) are more easily retained in the organic matter pores, resulting in metal enrichment. Therefore, it is necessary to correct the HII threshold for high-TOC shale to increase the HII threshold and avoid misjudgment. The correction method is as follows: ΔHII = 0.15 × (TOC - 2), (TOC > 2) The coefficient 0.15 was determined through isothermal adsorption experiments; Establish a hierarchical HII c The dynamic threshold adjustment model is as follows: The TOC unit is wt%.

5. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamic coupling hydrothermal activity according to claim 1, characterized in that, The Eu anomaly associated with rare earth elements further confirms the existence of hydrothermal activity, specifically including: Based on the rare earth element content data output above, using or Calculate the Eu anomaly and analyze the Eu in the hydrothermal system by combining the Eu valence behavior. 2+ The enrichment mechanism outputs δEu anomalies, serving as an independent geochemical criterion for hydrothermal activity.

6. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamically coupled hydrothermal activity according to claim 1, characterized in that, The construction of the hydrothermal mineral assemblage index (HMI) through mineral phase equilibrium constraints specifically includes: Based on the hydrothermal mineral content data output above, mineral weights are assigned (according to mineral indicativeness and stability), and nonlinear correction is performed using the tanh(x) function to remove outliers. The HMI is calculated by combining regional sedimentary background values ​​and mineral assemblages, and the hydrothermal mineral assemblage index HMI is output to quantitatively reflect the relationship between mineral development and hydrothermal activity intensity.

7. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamic coupling hydrothermal activity according to claim 6, characterized in that, Hydrothermal mineral phase equilibrium constraints and HMI index construction, specifically including: Calculate the total content of typical hydrothermal minerals in shale samples: Input mineral content data obtained through whole-rock mineral composition and FEM-EDS analysis, and identify and calculate the total content of typical hydrothermal minerals based on mineralogy principles and characteristics of hydrothermal mineralization systems; C hydro,i =w i ×100wt%; Among them, C hydro,i The total content of type i hydrothermal minerals in the shale sample is expressed in wt%; i The content of the i-th hydrothermal mineral is determined by whole-rock mineral composition and FEM-EDS testing; i represents the number of hydrothermal mineral types (e.g., pyrite, sphalerite, barite); the total hydrothermal mineral content is output (in wt%), providing a basis for subsequent HMI index calculation. Determine the weight values ​​for various hydrothermal minerals: Input the results of mineral genesis, content, and stability analysis. Based on the relationship between the strength of mineral indication of hydrothermal activity and the factors of mineral genesis, stability, and content, assign weights to different minerals. Weight assignment: pyrite (0.5), barite (0.3), sphalerite (0.2). Output the weight values ​​of each hydrothermal mineral for HMI calculation. A hydrothermal mineral weight correction factor is established, and the above weight factors are corrected using the following method: First, the relationship between hydrothermal mineral content and hydrothermal intensity exhibits two key characteristics: nonlinear response (mineral content gradually tends to saturate in high-value areas) and geochemical difference response. Therefore, in the quantitative evaluation process, it is necessary to avoid the excessive influence of outliers on the model and to eliminate regional geochemical background differences. Specific methods include: A hyperbolic tangent function tanh(x) is introduced, which is continuous, nonlinear and has a finite range [0,1), where x is the correction outlier; Statistical analysis of hydrothermal mineral content (C) in all shale samples from the study area i Calculate the median M and the median absolute deviation MAD, and then... i After removing outliers >3MAD, the background value C of hydrothermal mineral content in the study area was obtained. bac The method is as follows: C bac =M(C i )±MAD(C i ) w is obtained by multi-mineral coupling correction. i For samples containing multiple hydrothermal minerals, the correction method is as follows: This method can be further analyzed for errors, and the analysis method is as follows: Experience shows that when n>20, based on the central limit theorem, the relative error is <5%, therefore the number of samples involved n>20 is required. When n>20, the model has high applicability. Finally, the mineral assemblage index HMI was constructed to establish hydrothermal mineral phase equilibrium constraints. The method is as follows: Where m represents the specific types and quantities of hydrothermal minerals (including pyrite, sphalerite, and barite).

8. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamically coupled hydrothermal activity according to claim 1, characterized in that, The establishment of a hydrothermal contribution rate (HCR) model by combining multiple parameters includes: Based on the above output HII c Based on δEu, HMI, and TOC data, a quaternary parameter set is constructed. Principal component analysis (PCA) is used to calculate the parameter weights A, B, C, and D, then the hydrothermal contribution rate is calculated, and finally, uncertainty is assessed, outputting the HCR value ±u. HCR That is, the contribution of hydrothermal activity to each sample and the error range.

9. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamic coupling hydrothermal activity according to claim 8, characterized in that, Establishing a hydrothermal contribution rate (HCR) model, specifically including: A parameter set model is established, and based on the above multivariate parameter indicators, a comprehensive deterministic calculation method for hydrothermal activity is established. The hydrothermal contribution rate (HCR) evaluation equation is fitted, and the equation is as follows: Principal component analysis (PCA) was used to calculate the weights of shale sample parameters and determine the weights A, B, C, and D of HII, δEu, HMI, and TOC parameters. First, construct the parameter matrix X: Standardization process: Among them, u j δ is the background value of the j-th parameter. j Let be the standard deviation of the j-th parameter; Calculate using the covariance matrix: Perform eigenvalue decomposition, ∑v=vλ, to obtain eigenvalues ​​λ and eigenvectors v1; Calculate the variance contribution rate. The weights of each parameter are determined, and the weights are calculated as follows: The weight values ​​are normalized, and the calculation method is as follows: The hydrothermal contribution rate (HCR) of each shale sample was calculated using a multivariate parameter model. The method for uncertainty assessment is as follows: The final HCR output, and its final expression result are as follows:

10. The method for deterministic calculation and quantitative evaluation of multi-parameter dynamically coupled hydrothermal activity according to claim 1, characterized in that, The quantitative evaluation of hydrothermal activity intensity based on HCR values ​​and mineral development characteristics specifically includes: Based on the output HCR values ​​and the identified mineral development characteristics, an HCR grading standard is established. Combined with mineral assemblages, the intensity levels of hydrothermal activity are classified (none – weak – moderate – strong – extremely strong). Evaluation tables or diagrams are output to serve oil and gas exploration and mineral deposit genetic research. A hydrothermal activity intensity level classification and geological interpretation report is generated, and finally, a quantitative evaluation of hydrothermal activity driven by multiple parameters is completed.

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