A coal facies identification method and system for a delta-tidal flat-lagoon facies coal seam

Abstract

This invention discloses a method and system for coal facies identification in deltaic-tidal flat-lagoon coal seams, relating to the field of quantitative coal facies identification technology. The method includes: collecting Late Paleozoic coal samples and extracting measurement parameters; classifying primary coal facies types based on the measurement parameters, statistically analyzing the consistency of the discrimination results, and determining the reliability of the primary coal facies type classification; for Late Paleozoic deltaic-tidal flat-lagoon coal seams, using the subtypes of vitrinite within the coal seam and microscopic components directly related to vegetation as the analysis objects, obtaining revised TPI and VI, classifying secondary coal facies types, and determining the reliability of the secondary coal facies type classification; obtaining the final reliability by productting the reliability of the primary coal facies type classification with the reliability of the secondary coal facies type classification. This invention employs a multi-angle, multi-parameter cross-validation approach to classify coal facies, effectively avoiding the subjective bias of a single method, making the identification results more comprehensive and closer to geological reality.

Description

Technical Field

[0001] This invention relates to the technical field of quantitative coal phase identification, and particularly to a method and system for identifying coal phases in delta-tidal flat-lagoon coal seams. Background Technology

[0002] With the increasing sophistication of deep coal and gas exploration, traditional coal geology (especially coal facies classification) is far from meeting the needs of efficient deep coal and gas exploration, particularly for deep coal and gas exploration and development in Late Paleozoic marine-continental transitional facies-marine coal seams.

[0003] The increasing sophistication of modern exploration demands more accurate and efficient identification of target areas conducive to coal-gas accumulation. Traditional coal sedimentology primarily predicts the development and distribution of coal seams. However, in Late Paleozoic delta-tidal flat-lagoon coal seams, coal facies types are more diverse, development environments are more sensitive, and distribution changes more rapidly. Coal facies types and their development are mainly influenced by multiple factors, including the sedimentary environment of coal-forming swamps, water supply conditions, and paleovegetation types. Traditional coal facies classification methods mainly rely on single organic microstructure components of coal, resulting in insufficient comprehensiveness of coal types and a need to improve the reliability and accuracy of the classification results, making it difficult to effectively guide precise coal-gas exploration. Therefore, there is an urgent need to develop more effective coal facies discrimination techniques based on multiple parameters, establish the correlation between coal facies and coal-gas accumulation, and more accurately and efficiently predict the distribution of favorable coal-gas accumulation target areas, providing support for coal-gas exploration in Late Paleozoic delta-tidal flat-lagoon coal seams.

[0004] While there are numerous traditional theories and methods for coal facies classification, most rely on different coal petrographic parameters combined with map plotting, resulting in limited classification types and questionable reliability and accuracy. Coal facies are complex and variable, and cannot be reconstructed using only a few coal petrographic parameters. Therefore, it is urgent to analyze multiple controlling factors in the development of coal-forming peat bogs and to comprehensively determine coal facies types using multiple parameters. This is crucial for more accurate and reliable identification of Late Paleozoic delta-tidal flat-lagoon coal seams, thereby meeting the needs of precise coal and gas exploration. Furthermore, current coal facies classification methods are too broad and lack specificity, failing to fully consider the differences in coal-forming peat bog development across different ages and sedimentary backgrounds, thus significantly limiting their practicality and applicability.

[0005] Existing coal phase identification schemes fall into two categories. One category is based on single coal petrographic microscopic component parameters, such as the original tissue structure preservation index (TPI) formula (TPI=(structural vitrinite + homogeneous vitrinite + hemifilamentous + filamentous) / (matrix vitrinite + coarse-grained + indolent)) and the original vegetation index (VI) formula (VI=(structural vitrinite + homogeneous vitrinite + hemifilamentous + fungal bodies + secretory bodies + resinous bodies) / (matrix vitrinite + detrital vitrinite + algal bodies + detrital chitinous bodies + colloidal bodies)). The plotting points are divided after calculating the parameters using the above original formulas. This type of method does not take into account the coal-forming water source conditions (such as differences in seawater / freshwater recharge), and cannot distinguish the essential differences between rain-fed, mineral-fed, and mixed swamps. For example, in the Late Paleozoic delta-tidal flat-lagoon coal seams in North China, the TPI of rain-fed swamps and mineral-fed swamps may overlap due to similar local burial conditions (e.g., both are 1.0-1.2), but the coal-forming environments of the two are significantly different, and a single microscopic component parameter cannot identify this core difference. Furthermore, the classification results are not quantified to ensure their reliability. When applied to Late Paleozoic delta-tidal flat-lagoon coal seams, the actual coal facies identification accuracy is less than 50% because the coupling relationship between water source and vegetation is ignored.

[0006] Another type is based on a single geochemical indicator, such as classifying coal facies solely by total sulfur content (St) or ash yield (Ad)—for example, classifying a nearshore mineralized swamp solely by St>1.5%, or a mineral-rich replenishing swamp solely by Ad>20%. While such methods can reflect some information about the coal-forming environment (e.g., St reflects the influence of seawater and Ad reflects the input of exogenous debris), they cannot fully reflect key hydrocarbon accumulation control factors such as vegetation type (woody / herbaceous / aquatic) and sedimentary dynamics (strong reduction / weak oxidation). For example, in the same mineralized swamp, the coal gas adsorption capacity of woody swamp (vitrinite V=70-85%) and aquatic swamp (V=50-65%) differs significantly (the former adsorbs 20-30% more than the latter). St or Ad alone cannot distinguish such differences, leading to a break in the logical connection between coal facies type and coal gas accumulation conditions, making it difficult to support the selection of target areas. For instance, in a certain block of the Qinshui Basin, the low-Ad rain-fed moss swamp (V=60-75%) was mistakenly selected as the target area because only Ad was used to classify the coal facies. After actual drilling, the coal gas content was only 0.8-1.2 m³ / t, far lower than the 3.5-4.0 m³ / t of the mineralized woody swamp.

[0007] In summary, existing coal facies type restoration techniques (based on single coal microscopic component parameters and single geochemical indicators) suffer from limitations in parameters when identifying coal facies, failing to fully reflect key characteristics. This results in low reliability of the restored coal facies type classification and its spatiotemporal distribution patterns, making it difficult to effectively guide deep coal and gas exploration. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of the prior art by providing a method and system for identifying coal facies in delta-tidal flat-lagoon coal seams, thereby solving the problems in the prior art.

[0009] This invention specifically provides the following technical solution: a method for identifying coal facies in delta-tidal flat-lagoon coal seams, comprising the following steps: Coal samples from the Late Paleozoic era were collected, and their total sulfur content (St), ash yield (Ad), pH index (H), and sedimentary environment were determined as parameters for analysis. The peat bog where the coal sample is located was classified into primary coal facies types according to the measured parameters. The primary coal facies type classification result of Ad was used as the standard. The number of consistency results between Ad and St, H and sedimentary environment was counted. The confidence of the primary coal facies type was determined based on the number of consistency. For coal seams in the Late Paleozoic delta-tidal flat-lagoon facies, the analysis focused on the subtypes of vitrinite within the coal seams and microscopic components directly related to vegetation. The Plant Tissue Preservation Index (TPI) was revised by removing non-structural components such as matrix vitrinite and detrital vitrinite. The Vegetation Index (VI) was revised by introducing filamentous and cutinite components to distinguish between woody and non-woody contributions. Based on the revised TPI and VI, secondary coal facies types were classified according to the primary coal facies type. The consistency of the two secondary coal facies type classifications was assessed to determine the reliability of the secondary coal facies type classification. The final confidence level of the coal phase type is obtained by productting the confidence level of the primary coal phase type and the confidence level of the secondary coal phase type, and the coal phase type is determined based on the final confidence level.

[0010] Preferably, in the primary coal phase type classification, the discrimination thresholds for each measured parameter are as follows: When the total sulfur content St < 1.0%, peat bogs are classified as rain-fed oligotrophic bogs; when 1.0% ≤ St ≤ 2.0%, peat bogs are classified as mixed-recharge mesotrophic bogs; when St > 2.0%, peat bogs are classified as mineral-fed eutrophic bogs. When the ash yield Ad < 10%, the peat bog is classified as a rain-fed oligotrophic bog; when 10% ≤ Ad ≤ 20%, the peat bog is classified as a mixed-recharge mesotrophic bog; and when Ad > 20%, the peat bog is classified as a mineral-fed eutrophic bog. When the pH index H < 0.5, peat bogs are classified as rain-fed oligotrophic bogs; when 0.5 ≤ H ≤ 1.5, peat bogs are classified as mixed-recharge mesotrophic bogs; when H > 1.5, peat bogs are classified as mineral-fed eutrophic bogs. When the depositional environment is a deltaic plain, peat bogs correspond to rain-fed oligotrophic bogs; when it is a delta front tidal flat, peat bogs correspond to mixed-recharge mesotrophic bogs; and when it is a delta front tidal flat lagoon, peat bogs correspond to mineral-fed eutrophic bogs.

[0011] Preferably, the revision of the plant tissue preservation index (TPI) by removing non-structural components such as matrix vitrinite and detrital vitrinite, and the revision of the vegetation index (VI) by introducing filamentous and cuticle tissues to distinguish the contributions of woody and non-woody plants, specifically involves: The revised expression for the Plant Tissue Preservation Index (TPI) is as follows: TPI = (structural vitrinite) / (matrix vitrinite + detrital vitrinite); The revised expression for the vegetation index VI is as follows: VI = (structural vitrinite content + filamentous material) / (matrix vitrinite content + cuticle material).

[0012] Preferably, the classification result of the primary coal facies type of Ad is used as the standard, and the number of consistent results between Ad and St, H, and sedimentary environment is counted. The confidence level of the primary coal facies type is determined based on the number of consistent results. Specifically: If the determination result of ash yield Ad is consistent with the classification results of at least two parameters, then the determination confidence of the primary coal phase type is 90%; wherein the parameters include St, H, and sedimentary environment; If it matches only one parameter, the confidence level for determining the primary coal phase type is 80%. If all three parameters are consistent, the confidence level for determining the primary coal phase type is 100%.

[0013] Preferably, the consistency judgment based on the secondary coal phase type classification results of the revised TPI and revised VI, and the determination of the reliability of the secondary coal phase type classification, specifically involves: If a unique secondary type is identified based on the revised TPI, the basic confidence level is 70%. If a unique secondary type is identified based on the revised VI, the basic confidence level is 60%. If the revised TPI and the revised VI classification results are consistent, the basic confidence level is 80%. If each classification result is met, the base confidence level is increased by 10% to obtain the final confidence level for the secondary coal phase type.

[0014] Preferably, the secondary coal phase types include rain-fed woody swamps, rain-fed moss swamps, rain-fed mixed woody and moss swamps; mixed herbaceous woody mixed swamps, mixed moss, woody and fern mixed swamps, mixed herbaceous swamps; mineral-fed herbaceous swamps, mineral-fed woody swamps, and mineral-fed aquatic swamps.

[0015] This invention provides a coal facies identification system for delta-tidal flat-lagoon coal seams, comprising: The data acquisition module is used to collect coal samples from the Late Paleozoic era and determine their total sulfur content (St), ash yield (Ad), acidity / alkalinity index (H), and sedimentary environment as measurement parameters. The primary coal facies type classification module is used to classify the peat bog where the coal sample is located according to the measured parameters. Taking the primary coal facies type classification result of Ad as the standard, the module counts the number of consistency results between Ad and St, H, and sedimentary environment discrimination results, and determines the discrimination reliability of primary coal facies type based on the number of consistency results. The secondary coal facies classification module is used for Late Paleozoic delta-tidal flat-lagoon coal seams. It analyzes vitrinite subtypes and microscopic components directly related to vegetation within the coal seam. The module revises the Plant Tissue Preservation Index (TPI) by removing non-structural components such as matrix vitrinite and detrital vitrinite, and revises the Vegetation Index (VI) by introducing filamentous and cutinite to differentiate between woody and non-woody contributions. Based on the revised TPI and VI, secondary coal facies classification is performed on the basis of primary coal facies types. The consistency of the two secondary coal facies classification results is assessed to determine the reliability of the secondary coal facies classification. The credibility generation module is used to obtain the final credibility of the coal phase type by product of the credibility of the primary coal phase type and the credibility of the secondary coal phase type, and to perform coal phase discrimination based on the final credibility.

[0016] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described method for identifying coal facies in delta-tidal flat-lagoon coal seams.

[0017] The present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for coal facies identification of delta-tidal flat-lagoon coal seams.

[0018] Compared with the prior art, the present invention has the following significant advantages: This invention innovatively adopts a two-level classification scheme: the primary coal facies type classification uses four core parameters—ash yield (Ad), total sulfur (St), pH index (H), and sedimentary environment—to identify the primary type from three key dimensions of "nutrient supply-water medium environment-exogenous material input" in coal-forming swamps. The secondary coal facies type classification further refines the coal-forming plant types and sedimentary microenvironment through the revised plant tissue preservation index (TPI) and vegetation index (VI). Furthermore, the coal facies is classified using a multi-angle, multi-parameter cross-validation method, which effectively avoids the subjective bias of a single method and makes the identification results more comprehensive and closer to the geological reality. Attached Figure Description

[0019] Figure 1 This is a plant tissue preservation index diagram provided in an embodiment of the present invention; wherein, Figure 1 (a) represents the structure of the vitrinite (t, reflected light). Figure 1 (b) is the matrix vitrinite (cd, reflected light). Figure 1 (c) represents detrital vitrinite (Vd, reflected light); Figure 2 This is a vegetation index map provided in an embodiment of the present invention; wherein, Figure 2 (a) is a filamentous material (F, reflective). Figure 2 (b) is keratin (Cu, reflective). Figure 2 (c) is a cuticle (Cu, fluorescence); Figure 3 This is a macroscopic coal and petrographic composition diagram of the present invention; wherein, Figure 3 (a) is vitreous coal and bright coal. Figure 3 (b) represents bright coal and dark coal. Figure 3 (c) is charcoal; Figure 4 These are diagrams of four macroscopic coal and rock types according to the present invention; wherein, Figure 4 (a) is bright coal. Figure 4 (b) is a semi-bright briquette. Figure 4 (c) is a semi-dark briquette. Figure 4 (d) is dark coal; Figure 5 This is a scatter plot showing the correlation between Ad and St in this invention; Figure 6 This is a scatter plot showing the correlation between H and Ad in this invention; Figure 7 This is a scatter plot showing the correlation between St and H in this invention; Figure 8 This is a flowchart of the coal-phase peat swamp type identification method of the present invention; Figure 9 This is a schematic diagram of the coal facies distribution in the delta-tidal flat-lagoon environment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] The core differences between this invention and existing technologies (such as the TPI-GI index method, GWI-VI index method, etc.) are as follows: Innovative parameter combination: Breaking through the limitations of traditional single microscopic components or geochemical indicators, for the first time, "total sulfur (St) + ash yield (Ad) + acidity (H) + sedimentary environment" are used as the core parameters for classifying primary coal facies types, covering four dimensions: coal-forming water source, material input, water medium, and sedimentary environment.

[0022] Formula Revision and Innovation: In response to the abundant characteristics of woody plants in the Late Paleozoic, the TPI formula (removing non-in-situ components such as homogeneous vitrinite and retaining only structural vitrinite / (matrix vitrinite + detrital vitrinite)) and the VI formula (focusing on the ratio of woody-derived components (structural vitrinite + filamentous material) to non-woody-derived components (matrix vitrinite + cuticle material)) have been revised to solve the problem of interference from non-woody components in the traditional formula.

[0023] Innovation in Quantitative System: The first dual-dimensional quantitative model of "discrimination credibility of primary coal phase type × discrimination credibility of secondary coal phase type" is created, which makes up for the deficiency of traditional methods that only perform qualitative analysis without credible quantitative evaluation. The above innovations are not disclosed in existing technologies and have non-obviousness.

[0024] Experimental Basis and Results: In the application of Late Paleozoic delta-tidal flat-lagoon coal seams in North China, this method uses "water source conditions + vegetation type" as the main discrimination parameters, combined with multiple parameters such as coal-forming sedimentary environment, coal and petrographic composition and type, and pollen inversion vegetation type. The accuracy of coal facies identification results is significantly better than the effect of single parameter classification in existing technologies. At the same time, the quantitative reliability rating provides a traceable geological basis for the accurate identification of coal facies and the efficient selection of favorable target areas for coal-rock gas.

[0025] Currently, research on the classification and distribution patterns of coal facies types worldwide is very weak. Some scholars have conducted some studies in the past, but their classification parameters and principles vary greatly, the parameter sources are singular, and the application is quite extensive. This has led to significant differences in the research results of different regions or different scholars in the same region, resulting in low credibility and making it difficult to effectively guide the classification of coal facies and coal and gas exploration practices in certain key ages and sedimentary environments.

[0026] This invention addresses the hot topic of deep coal and gas exploration in Late Paleozoic coal seams. Based on the actual conditions of the coal-forming sedimentary environment and coal-forming vegetation types at that time, this method for coal facies identification has been developed, striving to more specifically, accurately, and efficiently identify the coal facies types of Late Paleozoic delta-tidal flat-lagoon coal seams. A schematic diagram of the coal facies distribution in the delta-tidal flat-lagoon environment is shown below. Figure 9 As shown, this reveals its characteristics, spatiotemporal distribution, and evolution patterns, thereby guiding efficient exploration of deep coal and rock gas.

[0027] like Figure 8 As shown, the present invention adopts the following technical solution, including the following steps: Step 1: Collect coal samples from the Late Paleozoic era and determine their total sulfur content (St), ash yield (Ad), pH index (H), and sedimentary environment as measurement parameters.

[0028] Before classifying peat bogs, it is necessary to collect and organize basic data to provide an accurate basis for subsequent classification at each level. The following is a detailed explanation of the core parameters, data sources, and relevant calculation rules for each level of classification:

[0029] Parameters for classifying primary coal phase types: 1) Ash yield (Ad, %; reflects mineral / nutrient supply intensity): According to GB / T19619-2004 "Method for Determination of Ash Content in Peat", the ash content is calculated by the ignition method: (Ad, %) = (mass of residue after ignition m1 / mass of dried material before ignition m0) × 100%, where m0 needs to be dried to constant weight at 105±2℃. The core of ash yield is the total residual amount of inorganic components (minerals, salts) in coal, and the source of inorganic components is entirely determined by the type of replenishment: rain-fed swamps rely solely on atmospheric precipitation for replenishment, and the core characteristic of precipitation is "high purity with almost no external material input"; mineral-fed swamps rely on groundwater (especially groundwater connected to the sea) or surface water for replenishment, and the core characteristic of the replenishment water source is "rich in external materials"; mixed swamps are in a transitional state between the two.

[0030] 2) Total sulfur (St, % reflects the degree of seawater influence): The total sulfur content was determined using chemical analysis (combustion-neutralization titration method LY / T1251-202) or instrumental analysis (EA, XRF, ICP-MS / ICP-OES), with a relative deviation of ≤5%. The core difference in total sulfur content lies in the "source of sulfur replenishment," which must be considered in conjunction with the reducing environment of the swamp (swamps are generally reducing environments, providing conditions for sulfur preservation). Sulfur in rain-fed swamps comes solely from the "coal-forming plants themselves," with no external sulfur input. Sulfur in mineral-fed swamps mainly comes from "sulfates brought by the replenishment water source," and is largely preserved in a reducing environment. Mixed swamps are in a transitional state between the two.

[0031] 3) pH index (H; reflects the acidity or alkalinity of swamp water): According to GB / T 15555.2-1995 Determination of Copper, Zinc, Lead and Cadmium in Solid Waste by Atomic Absorption Spectrophotometry, the mass fractions of oxides (CaO, MgO, Fe2O3) of Ca, Mg, and Fe in coal samples were determined using an atomic absorption spectrophotometer. The molar numbers of Ca²⁺, Mg²⁺, and Fe²⁺ were then calculated using molar mass conversion. The calculation (Ca...) 2+ +Mg 2+ ) / Fe 2+ The ratio is used to establish a quantitative correspondence between the ratio and pH value: when the ratio is <0.5, the corresponding pH is <4.5 (strongly acidic); when the ratio is =0.5-1.5, the corresponding pH is 4.5≤pH≤6.5 (weakly acidic to neutral); when the ratio is >1.5, the corresponding pH is >6.5 (weakly alkaline). The pH value is ultimately expressed as the ion molar ratio and the corresponding pH range, ensuring a direct correlation with the industry standard pH definition.

[0032] Sedimentary Environments: Delta Plain: The core terrestrial area of ​​the delta, with distributary channels as its framework and widespread interdistributary bays and depressions. Swamps are mostly rain-fed or mixed swamps, primarily fed by precipitation with limited groundwater recharge. They are characterized by a mix of woody, mossy, and herbaceous plants, with well-preserved structures, and were the main coal-forming environment of the Late Paleozoic. Delta Front: The underwater front of the delta, where rivers converge with basin waters, resulting in hydrodynamic fluctuations. Interdistributary depressions easily accumulate water to form mixed swamps, recharged by a mixture of rivers and basin waters. Herbaceous and woody plants are mixed, and the fluctuations in water cover lead to moderate structural decomposition, with some areas developing thin coal seams. Tidal Flat: The coastal tidal-dominated area, divided into supratidal, intertidal, and subtidal zones. Supratidal depressions and low-lying intertidal areas easily develop mineralized swamps, recharged by seawater and groundwater, high in sulfur and ash, dominated by herbaceous and aquatic plants, with alternating redox reactions and strong structural decomposition. Lagoon: A closed, shallow water area formed by barriers, primarily a still water environment. Mineral-aquatic marshes develop in the surrounding depressions or shallow water areas at the bottom of the lake. They are highly reducing and have a high water cover. Aquatic plants and mosses are dominant, with high sulfur content and complete decomposition of plant structures, often accompanied by layers of saprophytic mud.

[0033] Step 2: Classify the primary coal facies type of the peat bog where the coal sample is located according to the measured parameters. Using the primary coal facies type classification result of Ad as the standard, count the number of consistency results between Ad and St, H, and sedimentary environment. Determine the confidence of the primary coal facies type classification based on the number of consistency results. Classify the coal facies into rain-fed oligotrophic bog, mixed-recharge mesotrophic bog, and mineral-fed eutrophic bog.

[0034] Determination of Primary Peat Bog Type: The determination of primary peat bog type (i.e., primary coal facies classification) is based on three core parameters: St, H, and Ad. The specific determination process and rules are as follows: (1) Preliminary parameter discrimination: Based on the classification criteria of the three parameters St, H and Ad, the peat bog samples were independently classified to obtain preliminary classification results based on a single parameter: 1) Total sulfur content (St, %): When St < 1.0%, it is classified as a rainfed oligotrophic swamp (Type I). Basis: Primarily supplied by precipitation, with no exogenous sulfate input, resulting in extremely low sulfur content. Referring to data from coal seam No. 5 of the Shanxi Formation (Late Permian), its total sulfur content ranges from 0.10% to 1.49%, averaging 0.53%, with low-sulfur samples exhibiting typical rainfed swamp characteristics. Furthermore, combined with existing research, the average SO3 content of coal seam No. 8 of the Benxi Formation (Late Carboniferous) is 2.74% (corresponding to low total sulfur), further validating the low-sulfur nature of rainfed swamps.

[0035] When 1.0% ≤ St ≤ 2.0%, it is classified as a nutrient swamp in the II mixed recharge stage. Basis: Sulfur content is intermediate between precipitation and groundwater / seawater recharge. Reference studies show that the total sulfur content of coal No. 5 in well S13 is 0.52%–1.49%, with some samples ranging from 1.0% to 2.0%, indicating a transitional state; studies of coal from the Shanxi Formation (Early Permian) show a total sulfur content of 0.71%–2.08%, with samples in the middle range corresponding to mixed swamps.

[0036] When St > 2.0%, it is classified as a III-level mineral-aquatic eutrophic swamp. Basis: Influenced by seawater or deep hydrothermal fluids, sulfate reduction is strong, resulting in a significant increase in sulfur content. Reference studies show that the average organic sulfur content in Late Permian marine carbonate interbedded coal in Guizhou is 5.19%, with high overall total sulfur content; studies of Taiyuan Formation (Late Carboniferous-Early Permian) coal show total sulfur content of 1.88%–3.86%, mostly > 2.0%, which is typical of the sulfur content characteristics of mineral-aquatic swamps.

[0037] 2) pH level (H): When H < 0.5, it is classified as a rain-fed, oligotrophic swamp. Basis: Primarily supplied by atmospheric precipitation, with no external Ca or Mg input. Due to extremely low Fe content from pyrite precipitation, the (Ca+Mg) / Fe ratio is high, and H represents its inverse characteristic. This is corroborated by studies on No. 8 coal from the Benxi Formation in Ordos (average Fe2O3 0.32%, average CaO 1.18%, (Ca+Mg) / Fe > 1.5) and low-sulfur, low-ash coal samples from the Shanxi Formation (average Fe2O3 0.96%, relatively enriched (CaO+MgO), H < 0.5).

[0038] When 0.5 ≤ H ≤ 1.5, it is classified as a nutrient swamp in the II mixed recharge range. Basis: Due to mixed recharge from precipitation and groundwater / seawater, the input of Ca, Mg, and Fe is between that of rain-fed and mineral-fed swamps, and the (Ca+Mg) / Fe ratio exhibits transitional characteristics. Referring to the research conclusions on intermediate ash coal samples from the Ordos Shanxi Formation (0.32%~1.25% (Fe2O3), 0.36%~0.44% (CaO+MgO), (Ca+Mg) / Fe 0.8~1.5), and on some transitional coal samples from the Benxi Formation (0.5%~1.0% (Fe2O3), (1.0%~1.5% (CaO+MgO)), both confirm that the H value conforms to this transitional range.

[0039] When H > 1.5, it is classified as a III-type mineral-rich eutrophic swamp. The basis for this classification is: replenishment by seawater or deep hydrothermal fluids, resulting in a large input of Ca and Mg; and a strong reducing environment promoting the precipitation of Fe sulfides (pyrite), leading to a relatively high Fe content and a low (Ca+Mg) / Fe ratio. Referring to relevant studies, the Late Permian mineral-rich swamp coals in Guizhou have high Fe2O3 content due to the enrichment of CaO (from marine carbonate rocks) and pyrite, with (Ca+Mg) / Fe < 0.8. The Taiyuan Formation mineral-rich swamp coals in the Ordos Basin have high Fe2O3 content (contributed by pyrite) and the Fe increase exceeds that of CaO+MgO, both confirming that their H value > 1.5.

[0040] 3) Ash yield (Ad, %): When Ad < 10%, it is classified as a Class I rain-fed oligotrophic swamp; the basis for this classification is that it is only replenished by atmospheric precipitation, has no external debris input, and has extremely low ash content. An ash content of less than 10% is generally considered the standard for classifying high-altitude peat swamps. Furthermore, existing studies have shown that the Ad content of rain-fed swamp coal samples from the Shanxi Formation ranges from 8.2% to 13.6%, with an average of 10.8%; reference studies have shown that the Ad content of rain-fed swamp coal from the Benxi Formation is consistently < 10%.

[0041] When 10%≤Ad≤20%, it is classified as a mixed-replenishment nutrient swamp in the II type; the basis is that the average ash content of coal in the Taiyuan Formation of the Qinshui Basin is 11.19%, but the ash content of mixed swamp samples (such as the transition zone of the delta front) is concentrated in 15%~20%, reflecting the mixed input of precipitation and terrigenous debris.

[0042] When Ad > 20%, it is classified as a III-level mineral-aquatic eutrophic swamp. Basis: Groundwater or seawater carries a large amount of terrestrial debris (clay, quartz, etc.), resulting in increased ash content. Referring to existing research, the ash content of Taiyuan Formation coal is 23.04%–26.16%, with most exceeding 20%; reference studies show that the proportion of low-temperature ash (LTA) in coal is 18.4%–65.5%, corresponding to an overall high ash content, characteristic of mineral-aquatic swamps.

[0043] 4) Sedimentary environment: When the depositional environment is a deltaic plain: it corresponds to a rain-fed oligotrophic swamp of type I. Basis: It is only replenished by atmospheric precipitation, with no input of terrigenous clastic material, and the depositional medium is stable (weakly reducing-weakly oxidizing). It corresponds to a low-lying peat swamp in the deltaic plain far from the sediment source, with vegetation mainly consisting of tall woody plants (Lepidoptera / Sealia), which is consistent with the depositional background of type I "rain-fed woody swamp" in Table 1.

[0044] When the depositional environment is delta front-tidal flat: it corresponds to nutrient marshland in mixed recharge type II; the basis is that it is recharged by a mixture of precipitation and terrigenous clastic material, and the depositional medium is affected by tides (periodic hydrodynamic fluctuations), corresponding to the delta front transition zone or tidal flat peat marshland, and the vegetation is mainly a mixed community of "rain-fed woody-herbaceous / moss", which matches the depositional environment characteristics of type II "mixed herbaceous-woody marshland" in Table 1.

[0045] When the sedimentary environment is delta front-tidal flat-tidal lacustrine: it corresponds to the III mineral-rich eutrophic swamp; the basis is: the tidal lacustrine / tidal flat environment is strongly fed by seawater / deep hydrothermal fluids, with strong input of terrigenous clastic and chemical substances, and the sedimentary medium is strongly reducing (pyrite-enriched), which corresponds to the delta front near-source or tidal-controlled peat swamp, and the vegetation is mainly mineral-rich herbaceous / aquatic plants (charophytes / aquatic ferns), which is consistent with the sedimentary background of the III type "mineral-rich aquatic swamp" in Table 1.

[0046] Correlation analysis of typical parameters was performed using scatter plots of the correlations between three parameters: ash yield (Ad), total sulfur (St), and pH (H). Figure 5 , Figure 6 , Figure 7 As shown, the results are as follows: The data points are highly dispersed with no obvious linear clustering trend, and the correlation coefficient R²≈0.05 (the closer R² is to 0, the weaker the correlation), indicating that Ad and St have almost no linear correlation and are independent geological parameters. The data points are irregularly distributed and do not show a synchronous rising or falling trend; the correlation coefficient R²≈0.03 indicates that the linear correlation between H and Ad is extremely weak, and they are independent geological parameters. Similarly, the data points are randomly dispersed with no significant correlation characteristics; the correlation coefficient R²≈0.02 indicates that St and H also have no obvious linear correlation and are independent geological parameters.

[0047] In summary, the correlation coefficients of the three parameters Ad, St, and H are all much less than 0.3, indicating that there is almost no linear correlation between them. Therefore, they can be used as independent indicators for coal phase classification.

[0048] Consistency Analysis of Results: Considering the four parameters Ad, St, H, and deposition environment, and given that Ad has the highest weighting (70%), the consistency analysis of the primary type discrimination results is as follows: If the discrimination result of Ad is inconsistent with the other three parameters (St, H, and sedimentary environment), it indicates a significant contradiction in the data. In this case, the discrimination result of Ad should be used as the preliminary basis, but the final determination should be suspended. The testing process, calculation methods, and data records of auxiliary parameters such as St, H, and sedimentary environment should be thoroughly checked to eliminate possible errors. If the discrimination result of Ad is consistent with the classification results of ≥2 other parameters, the primary type of the peat bog is identified as the type corresponding to Ad. The confidence level of the primary type classification is 90%. If the discrimination result of Ad is consistent with the classification result of only one other parameter, the discrimination result of Ad should still be taken as the standard. The confidence level of the primary type classification is 80%. If the discrimination result of Ad is completely consistent with the classification results of all other three parameters, the primary type of the peat bog is directly confirmed as the type corresponding to the consistent result. The confidence level of the primary type classification is 100%.

[0049] Step 3: For coal seams of the Late Paleozoic delta-tidal flat-lagoon facies, the analysis focuses on the subtypes of vitrinite within the coal seams and microscopic components directly related to vegetation. The Plant Tissue Preservation Index (TPI) is revised by removing non-structural components such as matrix vitrinite and detrital vitrinite. The Vegetation Index (VI) is revised by introducing filamentous and cutinite to distinguish the contributions of woody and non-woody plants. Based on the revised TPI and VI, secondary coal facies types are classified on the basis of primary coal facies types. The consistency of the two secondary coal facies type classification results is judged to determine the reliability of the secondary coal facies type classification.

[0050] Secondary coal phase type classification parameters: (1) Plant Tissue Preservation Index (TPI): Original calculation formula: TPI = (structural vitrinite + homogeneous vitrinite + hemifilamentous vitrinite + filamentous vitrinite) / (matrix vitrinite + coarse granules + inert granules).

[0051] The revised calculation formula is: TPI = (structural vitrinite) / (matrix vitrinite + detrital vitrinite).

[0052] Revision basis: 1) Emphasizing "In-situ Plant Structural Integrity": The woody plant communities of the Late Carboniferous-Early Permian in North China, dominated by *Lepidoptera* and *Cordaia*, show that structural vitrinite directly originates from the in-situ preservation of plant stems / roots, undamaged by transportation. In contrast, matrix vitrinite is a gelled product of plant decomposition, and detrital vitrinite is fragmented vitrinite transported from elsewhere. The revised term "structural vitrinite" more accurately reflects the "degree of decomposition and transportation distance" during plant burial than "decomposition / transportation vitrinite," as detailed below. Figure 1 As shown.

[0053] 2) Avoiding interference from non-woody components: Traditional TPI may contain components of lower plants such as cuticles and sporophytes, while woody plants are absolutely dominant in North China during this period. The revised version focuses on the subtypes inside the vitrinite, which is in line with the regional vegetation characteristics.

[0054] 3) Geological environment information reflected: The core indicator is "plant decomposition intensity and burial rate". TPI>1.5: indicates a high proportion of structural vitrinite, corresponding to a rapid burial and weak decomposition environment (such as a deltaic distributary bay buried by a sudden flood); TPI<1: indicates a high proportion of matrix / detrital vitrinite, corresponding to a slow burial and strong decomposition environment (such as a long-term exposed rain-fed swamp high-level area); 1≤TPI≤1.5: represents an intermediate state, corresponding to medium-speed burial and moderate decomposition. Literature basis for TPI parameter: 1. TPI<1.0: humid forest swamp; basis: the TPI value of No. 2 coal of Shanxi Formation (Late Permian) in the Yanchuan South Block of the southeastern Ordos Basin is mostly concentrated between 0.5 and 0.8, corresponding to a deep lacustrine and forest swamp environment with strong plant decomposition. 2. TPI > 1.5: Dry forest swamp; Basis: The main coal seams (Jurassic) of the Yan'an Formation on the northwestern margin of the Ordos Basin have TPI values ​​ranging from 1.21 to 3.36, averaging about 2.2. Some coal seams have TPI values ​​reaching 1.2 to 1.8, indicating an oxidizing environment with predominantly woody vegetation and shallow water, corresponding to the dry forest swamp facies (swamps are located above the water table and are often in a weakly oxidizing environment). 3. 1.0 ≤ TPI ≤ 1.5: Transitional type; Basis: The TPI value of No. 5 coal seam (Late Permian) in the Shanxi Formation of the central and eastern Ordos Basin is 0.8 to 1.3, reflecting a weakly oxidizing-reducing transitional environment in the interdistributary bay of the delta plain.

[0055] (2) Vegetation Index (VI): Original formula: VI = (structural vitrinite + homogeneous vitrinite + hemifilamentous vitrinite + fungal vitrinite + secretory vitrinite + resinous vitrinite) / (matrix vitrinite + detrital vitrinite + algal vitrinite + detrital chitinous vitrinite + colloidal vitrinite) (Calder., 1994); Revised formula: VI = (structural vitrinite content + filamentous vitrinite) / (matrix vitrinite content + cutinite), using the average of parallel samples and calculating the standard deviation. The basis for the revision is as follows:

[0056] 1) Distinguishing between "woody and non-woody vegetation contributions": Late Carboniferous-Early Permian vegetation in North China was dominated by "woody plants (derived from structural vitrinite)," but with localized development of herbaceous / lower plants (derived from cuticle). Structural vitrinite and filamentous tissue both originated from woody plants (filamentous tissue is a product of woody plant oxidation), while matrix vitrinite can originate from herbaceous decomposition. Cuticle is a characteristic component of herbaceous / lower plants. The revised model accurately characterizes vegetation community structure by comparing "woody-derived components" with "non-woody-derived components," as detailed below. Figure 2 As shown.

[0057] 2) Avoid “misjudgment of inert components”: Traditional VI may include non-vegetation structural components such as sporophytes. The revised version focuses on microscopic components directly related to vegetation, which is in line with the regional vegetation evolution background (the Late Carboniferous to Early Permian was the period of global woody plant prosperity).

[0058] 3) The geological environment information reflected, with the core indicator being "vegetation type and community dominance". VI>1.5: indicates that the woody source component is dominant, corresponding to woody swamps (such as Lepidoptera forest swamps); VI<1: indicates that the non-woody source component is dominant, corresponding to herbaceous / lower plant swamps (such as reed swamps, algae swamps); 1≤VI≤1.5: indicates that the woody-non-woody components are mixed, corresponding to woody-herbaceous mixed swamps.

[0059] (3) Macroscopic coal and petrographic composition and macroscopic coal and petrographic type, such as Figure 3 As shown, macroscopic coal petrographic components include vitreous coal, bright coal, dull coal, and fibrous charcoal. Vitreous coal: Deep black in color with a strong luster, it is the darkest and most lustrous component of coal. Its microscopic composition is relatively simple, making it a straightforward macroscopic coal petrographic component. Fiberous charcoal: Resembling charcoal in appearance, it is grayish-black in color, with a distinct fibrous structure and silky luster. Fiberous charcoal is loose and porous, brittle and easily broken, and can stain fingers. Bright coal: Its luster is second only to vitreous coal, generally black in color. Bright coal has a more complex composition. It is formed under reducing conditions of overlying water, through the gelation of plant lignocellulosic tissue, and the addition of other components and mineral impurities brought by water or wind, resulting in a complex composition. Dull coal: Dull in luster, generally grayish-black in color. Dull coal has a more complex composition. It is formed under conditions of flowing water and oxygen, enriched with chitinous groups, inert groups, or with the addition of a significant amount of minerals.

[0060] like Figure 4 Based on the combination of macroscopic coal and petrographic components and the average luster intensity they reflect, coal and petrographic types can be divided into four types: bright coal, semi-bright coal, semi-dark coal, and dull coal.

[0061] Bright coal: This is the stratum with the strongest luster in the coal seam. It is mainly composed of vitreous and bright coal (>80%), with a very strong luster. Semi-bright coal: Bright and vitreous coal constitute the majority (50%–80%), containing dark coal and fibrous char, with a slightly weaker luster intensity than bright coal. Semi-dull coal: Vitreous and bright coal content is relatively low (50%–20%), while dark coal and fibrous char content is relatively high, resulting in a relatively dull luster. Dull coal: Vitreous and bright coal content is very low (<20%), with dark coal being the main component, sometimes containing a relatively high amount of fibrous char, resulting in a dull luster. Coal facies classification: Late Paleozoic delta-tidal flat-lagoon coals can be classified into 3 primary types and 9 secondary types based on multiple different indicators (see Table 1 below).

[0062] Table 1. Secondary coal facies classification of Late Paleozoic delta-tidal flat-lagoon coals. Note: When classifying secondary coal phases, based on the primary coal phases, the coal phases are first classified according to TPI, and the classified secondary coal phases are assigned 70 points; if TPI cannot classify secondary coal phases, then VI is used to classify the coal phases, and the classified secondary coal phases are assigned 60 points; if the coal phases classified by TPI and VI are consistent, they are assigned 80 points.

[0063] The identification of secondary peat bog types is based on two core parameters, TPI and VI, combined with confirmatory indicators for auxiliary identification. The specific process and rules are as follows: (I) Core parameters and their discrimination intervals: (1) TPI discrimination of coal facies: First, the peat bog samples were classified into secondary types based on the classification criteria of plant tissue preservation index (TPI): When the primary type is rain-fed oligotrophic swamp: if TPI=1.5-1.8, it is classified as 1. rain-fed woody swamp; if TPI=0-0.1, it is classified as 2. rain-fed moss swamp; if TPI=0.3-0.6, it is classified as 3. rain-fed woody-moss mixed swamp.

[0064] When the primary type is a mixed-suspension nutrient marsh: if TPI=1.2-1.5, it is classified as 4. Mixed herbaceous-woody wetland marsh; if TPI=0.9-1.2, it is classified as 5. Mixed moss-woody-fern wetland marsh; if TPI=0-0.9, it is classified as 6. Mineral-aquatic herbaceous marsh.

[0065] When the primary type is mineral-rich eutrophic marsh: if TPI=0-0.3, it is classified as 7. Mineral-rich woody marsh; if TPI=0.6-0.9, it is classified as 8. Mineral-rich aquatic marsh; if TPI=0-0.1, it is classified as 9. Mineral-rich aquatic marsh.

[0066] If a unique type result can be clearly obtained, the confidence level of the secondary type determination is 70%.

[0067] (2) VI Discrimination of Coal Phase: If a unique type cannot be determined based on the TPI parameter (e.g., the TPI value is in the critical range of the classification criteria, or the discrimination result is ambiguous due to data errors), then the vegetation index (VI) is used for supplementary discrimination: When the primary type is rain-fed oligotrophic swamp: if VI=2.0-2.6, it is classified as 1. rain-fed woody swamp; if VI=0-0.4, it is classified as 2. rain-fed moss swamp; if VI=0.4-1.2, it is classified as 3. rain-fed woody-moss mixed swamp.

[0068] When the primary type is a mixed-species nutrient marsh: if VI=1.2-1.5, it is classified as 4. Mixed herbaceous-woody wetland marsh; if VI=1.2-1.6, it is classified as 5. Mixed moss-woody-fern wetland marsh; if VI=0-0.9, it is classified as 6. Mixed herbaceous marsh.

[0069] When the primary type is mineral-rich eutrophic marsh: if VI=0-0.3, it is classified as 7. mineral-rich herbaceous marsh; if VI=0.6-0.9, it is classified as 8. mineral-rich woody marsh; if VI=0-0.1, it is classified as 9. mineral-rich aquatic marsh.

[0070] If a unique type result can be clearly obtained, the reliability of the secondary type determination is 60%. Assuming a good correlation between TPI and VI, if the secondary coal phase types determined by TPI and VI are consistent, the reliability increases to 80%.

[0071] (3) Correlation analysis between TPI and VI: The data points show an overall trend of TPI increasing with the increase of VI, which reflects a certain positive correlation. The correlation coefficient between the two is calculated to be R²≈0.66. The closer it is to 1, the stronger the positive correlation. This indicates that there is a moderate positive linear correlation between TPI and VI.

[0072] (II) Confirmatory Indicators for Assisted Judgment: After determining the secondary type through core parameters (TPI, VI), confirmatory indicators are introduced for auxiliary judgment. The specific rule is to check whether the sample data conforms to the secondary type characteristics corresponding to each confirmatory indicator: 1. Rain-fed woody swamp: Verification index 1: Macroscopic coal type is mainly bright coal; Verification index 2: Paleobotanical type is tall woody plants (Lepidoptera / Sealia / Korda) >80%. 2. Rain-fed moss swamp: Verification index 1: Macroscopic coal type is mainly dark to dull coal; Verification index 2: Paleobotanical type is moss (Hornweed / Mugwort) >70%. 3. Rain-fed mixed woody-moss swamp: Verification index 1: Macroscopic coal type is mainly dark coal; Verification index 2: Paleobotanical type is moss 40-60%; small woody plants (Korda seedlings / seed ferns, etc.) 30-50%. 4. Mixed herbaceous woody wetland swamp: Verification index 1: Macroscopic coal type is mainly dark coal; Verification index 2: Paleobotanical type is herbaceous community (small ferns + moss, etc.) 40-60%; associated (woody) <50%. 5. Mixed moss-woody-fern wetland swamp: Verification index 1: Macroscopic coal type is mainly semi-bright briquettes; Verification index 2: Paleobotanical type is woody (Koda tree seedlings / small Lepidoptera) 30-50%; moss 20-40%. 6. Mixed herbaceous swamp: Verification index 1: Macroscopic coal type is mainly semi-dull to dark briquettes; Verification index 2: Paleobotanical type is herbaceous community (small ferns / wedge-leaved plants) >70%. 7. Mineral-cultivated herbaceous swamp: Verification index 1: Macroscopic coal type is mainly semi-dull to dark briquettes; Verification index 2: Paleobotanical type is herbaceous community (small ferns + moss) >60%. 8. Mineral-cultivated woody swamp: Verification index 1: Macroscopic coal type is mainly semi-bright briquettes; Verification index 2: Paleobotanical type is tall woody (Lepidoptera / Sealing Wood / Koda tree, etc.) >70%. 9. Mining-based aquatic marshes: Verification indicator 1: Macroscopic coal and rock type is mainly semi-dark to dark coal; Verification indicator 2: Paleobotanical type is aquatic plants (charophytes / aquatic ferns) >80%.

[0073] For each feature requirement that meets a confirmatory indicator (classification result), 10 points will be added to the credibility of the secondary type based on the core parameter discrimination result (the credibility of the core parameter discrimination is the base score, such as 70% of the TPI discrimination result or 60% of the VI discrimination result). The maximum score is 100%, that is, when the credibility reaches 100% after adding points, no more points will be added.

[0074] Step 4: Obtain the final confidence level of the coal phase type by product of the confidence level of the primary coal phase type and the confidence level of the secondary coal phase type, and use the final confidence level to determine the coal phase.

[0075] The final reliability of the coal phase type classification is obtained by multiplying the reliability of the primary coal phase type by the reliability of the secondary coal phase type, and the calculation formula is as follows: The final confidence level of coal phase = confidence level of primary coal phase type × confidence level of secondary coal phase type.

[0076] For example: if the confidence level of the primary coal phase type is 95%; the confidence level of the secondary type is 70% based on the TPI and meets two confirmatory indicators (additional 20% bonus), then the final confidence level of the secondary type is 90%; the final confidence level of the coal phase = 95% × 90% = 86%.

[0077] The present invention will be further described below with reference to specific embodiments.

[0078] I. Case Background: This example selects 10 coal samples from wells Mi172 and Jin26 in the Ordos Basin, covering the three main coal-bearing strata of the Late Paleozoic Benxi Formation, Shanxi Formation, and Taiyuan Formation. The lithology of the coal samples is mainly semi-dull and semi-bright coal. The goal is to complete the identification and reliability assessment of coal facies types using the technical solution of this invention, providing a basis for the selection of target areas for deep coal and gas exploration. Data collection and processing: The core parameters required for the primary and secondary coal facies type classification of the 10 coal samples were tested and calculated. The data are shown in Table 2 below.

[0079] Table 2. Primary coal facies type and reliability determination table based on coal samples from wells Mi172 and Jin26. Table 2 (Continued) shows the primary coal facies types and reliability determination based on coal samples from wells Mi172 and Jin26. Note: All parameter tests follow the corresponding standards: St is determined by combustion-neutralization titration (LY / T1251-2021), H is calculated by the (Ca+Mg) / Fe ratio (according to GB / T6920-1986), Ad is determined by ignition method (GB / T19619-2004), and organic microscopic components are identified and statistically analyzed under a microscope.

[0080] The complete process for identifying primary coal phase types is illustrated using three typical coal samples—Mi172-2, JinJ26 2-3, and Mi172-12—as examples, following the procedure of "preliminary parameter identification → result consistency analysis → reliability determination." 1. Coal sample 172-2 (Taiyuan Formation): Single parameter discrimination: St=8.30% (>2%) → mineral-rich eutrophic swamp; H=0.078 (<0.5) → mineral-rich eutrophic swamp; Ad=22.84% (>20%) → mineral-rich eutrophic swamp. The depositional environment is delta front-tidal flat-lagoon → mineral-rich eutrophic swamp. Consistency analysis: St and Ad are consistent with the depositional environment results (3 parameters). The H result is abnormal. The H test data was checked and found to be correct (Fe²⁺ content is significantly increased due to pyrite precipitation, resulting in a low (Ca+Mg) / Fe ratio; H is a reverse characterization). Primary type determination: mineral-rich eutrophic swamp, confidence level 90% (consistency standard for 3 parameters).

[0081] 2. Jin 26-3 coal sample (Shanxi Formation): Single parameter discrimination: St=0.1% (<1.0%) → rain-fed oligotrophic swamp; H=1.589 (>1.5%) → mineral-fed eutrophic swamp; Ad=27.85% (>20%) → mineral-fed eutrophic swamp, depositional environment is delta front-tidal flat-lagoon → mineral-fed eutrophic swamp. Consistency analysis: H and Ad are consistent with the depositional environment results (3 parameters), St shows anomalies, and the St test data is verified to be correct. Primary type determination: mineral-fed eutrophic swamp, confidence level 90% (consistency standard of 3 parameters).

[0082] 3. Coal sample 172-12 (Benxi Formation): Single parameter discrimination: St=2.13% (>2%) → mineral-rich eutrophic swamp; H=0.926 (0.5-1.5) → nutrient swamp in mixed recharge; Ad=17.47% (10%-20%) → nutrient swamp in mixed recharge, depositional environment: delta front-tidal flat-lagoon → mineral-rich eutrophic swamp. Consistency analysis: H and Ad are consistent with the depositional environment results (3 parameters), St shows an anomaly, and the St test data is verified to be correct. Primary type determination: nutrient swamp in mixed recharge, confidence level 90%.

[0083] Secondary coal phase type identification: Based on the primary type results, the secondary coal phase type classification is completed through the process of "TPI priority identification → VI supplementary identification → verification index assistance". 1. Coal sample 172-2 (Level 1: Mineral-fed eutrophic swamp): Core parameter calculation: TPI = structural vitrinite / (matrix vitrinite + detrital vitrinite) = 0.00 / (3.76 + 0.7) = 0; VI = (structural vitrinite + filamentous material) / (matrix vitrinite + cutinite) = (0.00 + 4.51) / (35.76 + 1.39) = 0.12. Core parameter discrimination: TPI = 0 (0-0.1) → mineral-aquatic marshland (70% confidence); VI = 0.12 (0-0.4) → mineral-aquatic marshland (60% confidence); the results are consistent, and the confidence of the core parameter discrimination is increased to 80%. Assisted verification indicators: The macroscopic coal and rock type is mainly semi-dull coal.

[0084] Calculation of the confidence level for identifying secondary coal phase types: 80% confidence level for consistency of core parameters + 10% bonus for 1 verification indicator → upper limit of 100%, and finally 90% confidence level for identifying secondary coal phase types.

[0085] 2. Jin 26-3 coal sample (Level 1: mineral-fed eutrophic swamp): Core parameter calculation: TPI = 0 / (33.67 + 0) = 0; VI = (0 + 15.67) / (33.67 + 0) = 0.47. Core parameter discrimination: TPI = 0 (0-0.3) → mineral-aquatic herbaceous swamp (70% confidence); VI = 0.47 (0.4-1.2) → mineral-aquatic herbaceous swamp (60% confidence). The results are consistent, and the confidence of the core parameter discrimination is increased to 80%. Assisted verification indicators: The macroscopic coal and rock type is mainly semi-dull coal.

[0086] Calculation of the confidence level for the identification of secondary coal phase types: 80% confidence level of core parameters + 10% bonus for 1 verification indicator → 90%, and the final confidence level for the identification of secondary coal phase types is 90%.

[0087] 3. Coal sample 172-12 (Level 1: Nutrient marshland in mixed recharge): Core parameter calculation: TPI = 0 / (24.73 + 8.8) = 0; VI = (0 + 18.64) / (24.73 + 0) = 0.76. Core parameter discrimination: TPI = 0 (0-0.9) → Mixed herbaceous swamp (confidence 70%); VI = 0.76 (0.4-1.2) → Mixed herbaceous swamp (confidence 60%). The results are consistent, and the confidence of core parameter discrimination is increased to 80%. Validation indicator auxiliary: Semi-dark coal is the main component, which does not meet the parameter discrimination. Calculation of confidence of secondary coal phase type discrimination: Core parameter confidence 80% → 80%, final confidence of secondary coal phase type discrimination 80%. Final coal phase type and confidence conclusion: According to the calculation of "final confidence of coal phase = confidence of primary coal phase type discrimination × confidence of secondary coal phase type discrimination", the results of three typical coal samples are as follows:

[0088] M172-2 coal sample: mineral-fed aquatic swamp, final confidence level = 90% × 90% = 81%; Jin26-3 coal sample: mineral-fed herbaceous swamp, final confidence level = 90% × 90% = 81%; M172-12 coal sample: mixed herbaceous swamp, final confidence level = 80% × 80% = 64%. The remaining 7 groups of coal samples were all identified according to the above procedure, covering three types of secondary coal facies: mineral-fed aquatic swamp, mixed herbaceous woody wetland swamp, and rain-fed moss swamp. The final confidence levels of all samples were above 60%, verifying the stability and accuracy of the technical solution of this invention.

[0089] Example Verification: This example fully presents the entire process of "data collection → primary discrimination → secondary discrimination → credibility calculation," clarifying the testing basis, calculation logic, and discrimination criteria for each parameter. It perfectly matches the two-level classification system and multi-parameter cross-validation rules in the technical solution. Results show that this technology can accurately identify nine coal facies types in Late Paleozoic marine-continental transitional facies to marine coal seams. Furthermore, through quantitative credibility assessment, it addresses the pain point of traditional methods that "only provide type, not credibility," providing accurate target area prediction basis for deep coal and gas exploration. The technical effects are as follows:

[0090] 1. Multi-parameter comprehensive judgment improves the comprehensiveness of recognition.

[0091] Traditional coal facies classification methods often rely on a single parameter (such as coal petrographic composition alone), resulting in incomplete and unreliable identification results that fail to capture the complexity of coal-forming environments (such as water mixing and diverse vegetation types in marine-terrestrial transitional facies). This invention innovatively employs a two-level classification scheme: the primary coal facies type classification uses four core parameters—ash yield (Ad), total sulfur (St), pH index (H), and sedimentary environment—to pinpoint the primary type from three key dimensions: nutrient supply, aquatic environment, and exogenous material input in coal-forming swamps. The secondary coal facies type classification further refines the coal-forming plant types and sedimentary microenvironment through a revised Tissue Preservation Index (TPI) and Vegetation Index (VI). This multi-angle, multi-parameter cross-validation approach effectively avoids the subjective bias of single methods, resulting in more comprehensive identification results that are closer to geological reality. In the example, sample Mi172-2 was identified as a mineral-fed eutrophic swamp using four parameters: St, Ad, H, and sedimentary environment (90% confidence level for identifying primary coal facies type). Further verification using three indicators—TPI, VI, organic microscopic components, paleobotanical type, and sedimentary environment—confirmed it as a mineral-fed aquatic swamp (90% confidence level for identifying secondary coal facies type), resulting in a final confidence level of 81%. Compared to traditional single-parameter identification (e.g., using only Ad), this sample avoids the misclassification of high-ash samples as terrigenous rainfed swamps. The traditional single TPI method has a coal facies identification accuracy of 42% in the Late Paleozoic Taiyuan Formation coal seams of the Ordos Basin (5 out of 10 coal samples matched actual drilling results). This invention, through multi-parameter coupling, achieved an accuracy of 81.8% (8 out of 10 coal samples matched actual drilling coal gas content), significantly outperforming traditional methods. Furthermore, it can simultaneously reconstruct the complete coal-forming background, whereas traditional methods can only yield a single conclusion about high-ash coal facies.

[0092] 2. A quantitative weighting and scoring system is implemented to achieve objective evaluation.

[0093] Traditional coal phase classification is mostly qualitative, outputting only a single result for a certain coal phase, lacking reliability assessment, leading to significant discrepancies in the classification results of different scholars for the same coal sample. This invention constructs a three-level quantitative system:

[0094] In the classification of primary coal facies types, based on the scoring principles in Table 1 (Ad is the core parameter, accounting for 70% of the score), the data reliability boundary is clearly defined: the four parameters for primary coal facies type classification include three quantitative geochemical indicators (Ad, St, H) and one qualitative sedimentary background indicator (sedimentary environment). The sedimentary environment is determined using lithological assemblage, paleontological fossils, etc., and the matching degree between the determination result and the three quantitative indicators is included in the credibility assessment. Specifically, the credibility is 100% when all four parameters are completely consistent; 80% when the two core parameters (Ad + any one auxiliary parameter) are consistent; and 70% when only the Ad parameter is valid.

[0095] In the classification of secondary coal facies types, the TPI (Transmission Point Index) is used as the primary discrimination base score (70%), and the VI (Valuation Point Index) is used as the supplementary discrimination base score (60%). If both are consistent, the score increases to 80%. An additional 10 points are added for each verification indicator met (up to a maximum of 100%). The final overall reliability is calculated as "the reliability of the primary coal facies type classification × the reliability of the secondary coal facies type classification" (e.g., 95% × 90% = 85.5%). This system not only provides dual outputs of type and reliability, but each reliability value also corresponds to a clear parameter support logic (e.g., 85% reliability corresponds to "Ad + 1 consistent auxiliary parameter + TPI / VI discrimination + 1 verification indicator"), addressing the pain points of traditional methods where results are untraceable and disputes lack evidence. In deep coal and gas exploration practice, this quantitative system can directly provide threshold references for target area selection—prioritizing favorable coal facies with an overall reliability ≥ 70%, significantly improving the exploration success rate compared to traditional experience-based screening and reducing ineffective drilling costs.

[0096] 3. Classification of coal phases by grade, with strong practical applicability: The two-level classification system of this invention is designed to meet the accuracy requirements of different exploration stages: the primary coal facies type classification (rain-fed, mixed, and mineral-fed types) does not rely on complex microscopic component testing and can be completed using only conventional geochemical indicators, which is suitable for the need for rapid delineation of favorable coal facies zones over a large area in the early stages of exploration; the secondary coal facies type classification (9 sub-types) combines organic microscopic components and verification indicators, which is suitable for the need for refined evaluation of target areas in the middle and later stages of exploration.

[0097] Taking the Late Paleozoic coal and gas exploration in the Ordos Basin as an example, the initial stage quickly identified the mineralized eutrophic swamp zone, which accounts for 32% of the coal-bearing area of ​​the basin, through primary coal facies classification, providing direction for large-scale exploration deployment. Later, secondary coal facies classification was carried out for wells Mi172 and Jin26 in this zone, accurately identifying sub-types such as mineralized aquatic swamp and mixed herbaceous woody wetland swamp. Among them, the mineralized aquatic swamp has a high vitrinite content (65%-80%), providing high-quality reservoir space for coal and gas adsorption, and becoming a key drilling target area, realizing the full-process adaptation from large-scale delineation to small-scale focusing.

[0098] Meanwhile, the parameter classification standards of this system can be adjusted according to the coal-forming environment of different regions (e.g., the St classification threshold for Late Permian marine coals in southern China can be adjusted to "St<0.8% indicates rain-fed oligotrophic swamp"), possessing cross-basin and cross-era scalability, and is not limited to the Ordos Basin. Advantages: Strong targeting, adaptable to the coal-forming characteristics of Late Paleozoic marine-continental transitional facies-marine coal seams; revised TPI and VI calculation formulas (eliminating interference from non-woody components and highlighting the contribution of woody plants); parameter thresholds referencing measured data from the Benxi Formation and Shanxi Formation in the Ordos Basin, resulting in stronger adaptability; comprehensive multi-parameter analysis leads to more reliable results; quantitative rating ensures traceable credibility; primary coal facies classification relies on conventional chemical analysis (combustion titration, ignition method, etc.), while core parameters for secondary coal facies classification can be obtained through microscopic coal petrography identification, with mature testing methods and controllable costs. The parameter system can adjust the threshold according to the coal-forming environment of different regions: for example, in the Late Permian marine coal in the Liupanshui Basin of Guizhou, the St classification threshold can be adjusted to 'st < 0.8% in rain-fed swamps and St > 2.0% in mineral-fed swamps' because the influence of seawater is stronger; in the Mesozoic Jurassic terrestrial coal (Junggar Basin), the St parameter can be removed because there is no seawater supply, and only H and Ad are used for primary coal facies classification, which is applicable to all types of coal seams in marine-terrestrial transitional facies, marine facies, and terrestrial facies.

[0099] Based on the above description, the present invention also provides a coal facies identification system for delta-tidal flat-lagoon coal seams, comprising: The data acquisition module collects Late Paleozoic coal samples and measures their total sulfur content (St), ash yield (Ad), pH index (H), and sedimentary environment as measurement parameters. The primary coal facies classification module classifies the peat bog where the coal samples are located according to the measured parameters. Using the primary coal facies classification result of Ad as the standard, it counts the number of consistency results between Ad and the determinations of St, H, and sedimentary environment, and determines the reliability of the primary coal facies classification based on the number of consistency results. The secondary coal facies classification module targets Late Paleozoic delta-tidal flat-lagoon facies coal seams, focusing on the subtypes and focusing of vitrinite within the coal seam, which are directly related to vegetation. The microscopic components were used as the analysis objects. The plant tissue preservation index (TPI) was revised by removing non-structural components such as matrix vitrinite and detrital vitrinite. The vegetation index (VI) was revised by introducing filamentous tissue and cuticle to distinguish the contributions of woody and non-woody plants. Based on the revised TPI and revised VI, secondary coal facies types were classified on the basis of primary coal facies types. The consistency of the two secondary coal facies type classification results was judged to determine the discrimination confidence of the secondary coal facies types. The confidence generation module was used to obtain the final confidence of the coal facies type by multiplying the discrimination confidence of the primary coal facies type and the discrimination confidence of the secondary coal facies type. The coal facies was then classified based on the final confidence.

[0100] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described method for identifying coal facies in delta-tidal flat-lagoon coal seams.

[0101] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).

[0102] The present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for coal phase identification of delta-tidal flat-lagoon coal seams.

[0103] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0104] The above description, in conjunction with specific preferred embodiments, provides a more detailed explanation of the present invention. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for identifying coal facies in delta-tidal flat-lagoon coal seams, characterized in that, Including the following steps: Coal samples from the Late Paleozoic era were collected, and their total sulfur content (St), ash yield (Ad), pH index (H), and sedimentary environment were determined as parameters for analysis. The peat bog where the coal sample is located was classified into primary coal facies types according to the measured parameters. The primary coal facies type classification result of Ad was used as the standard. The number of consistency results between Ad and St, H and sedimentary environment was counted. The confidence of the primary coal facies type was determined based on the number of consistency. For coal seams in the Late Paleozoic delta-tidal flat-lagoon facies, the analysis focused on the subtypes of vitrinite within the coal seams and microscopic components directly related to vegetation. The Plant Tissue Preservation Index (TPI) was revised by removing non-structural components such as matrix vitrinite and detrital vitrinite. The Vegetation Index (VI) was revised by introducing filamentous and cutinite components to distinguish between woody and non-woody contributions. Based on the revised TPI and VI, secondary coal facies types were classified according to the primary coal facies type. The consistency of the two secondary coal facies type classifications was assessed to determine the reliability of the secondary coal facies type classification. The final confidence level of the coal phase type is obtained by productting the confidence level of the primary coal phase type and the confidence level of the secondary coal phase type, and the coal phase type is determined based on the final confidence level.

2. The method for coal facies identification of Late Paleozoic delta-tidal flat-lagoon coal seams as described in claim 1, characterized in that, In the classification of primary coal phase types, the discrimination thresholds for each measured parameter are as follows: When the total sulfur content St < 1.0%, peat bogs are classified as rain-fed oligotrophic bogs; when 1.0% ≤ St ≤ 2.0%, peat bogs are classified as mixed-recharge mesotrophic bogs; when St > 2.0%, peat bogs are classified as mineral-fed eutrophic bogs. When the ash yield Ad < 10%, the peat bog is classified as a rain-fed oligotrophic bog; when 10% ≤ Ad ≤ 20%, the peat bog is classified as a mixed-recharge mesotrophic bog; and when Ad > 20%, the peat bog is classified as a mineral-fed eutrophic bog. When the pH index H < 0.5, peat bogs are classified as rain-fed oligotrophic bogs; when 0.5 ≤ H ≤ 1.5, peat bogs are classified as mixed-recharge mesotrophic bogs; when H > 1.5, peat bogs are classified as mineral-fed eutrophic bogs. When the depositional environment is a deltaic plain, peat bogs correspond to rain-fed oligotrophic bogs; when it is a delta front tidal flat, peat bogs correspond to mixed-recharge mesotrophic bogs; and when it is a delta front tidal flat lagoon, peat bogs correspond to mineral-fed eutrophic bogs.

3. The method for coal facies identification of Late Paleozoic delta-tidal flat-lagoon coal seams as described in claim 1, characterized in that, The plant tissue preservation index (TPI) is revised by removing non-structural components such as matrix vitrinite and detrital vitrinite, and the vegetation index (VI) is revised by introducing filamentous and cuticle tissues to distinguish the contributions of woody and non-woody plants. Specifically: The revised expression for the Plant Tissue Preservation Index (TPI) is as follows: TPI = (structural vitrinite) / (matrix vitrinite + detrital vitrinite); The revised expression for the vegetation index VI is as follows: VI = (structural vitrinite content + filamentous material) / (matrix vitrinite content + cuticle material).

4. The method for coal facies identification of delta-tidal flat-lagoon coal seams as described in claim 1, characterized in that, The method uses the primary coal facies type classification result of Ad as the standard, counts the number of consistency results between Ad and St, H, and sedimentary environment discrimination results, and determines the discrimination reliability of primary coal facies type based on the number of consistency results. Specifically: If the determination result of ash yield Ad is consistent with the classification results of at least two parameters, then the determination confidence of the primary coal phase type is 90%; wherein the parameters include St, H, and sedimentary environment; If it matches only one parameter, the confidence level for determining the primary coal phase type is 80%. If all three parameters are consistent, the confidence level for determining the primary coal phase type is 100%.

5. The method for coal facies identification of delta-tidal flat-lagoon coal seams as described in claim 1, characterized in that, The consistency judgment of the two obtained secondary coal phase type classification results is performed to determine the reliability of the secondary coal phase type classification. Specifically: If a unique secondary type is identified based on the revised TPI, the basic confidence level is 70%. If a unique secondary type is identified based on the revised VI, the basic confidence level is 60%. If the revised TPI and the revised VI classification results are consistent, the basic confidence level is 80%. If each classification result is met, the base confidence level is increased by 10% to obtain the final confidence level for the secondary coal phase type.

6. The method for coal facies identification of delta-tidal flat-lagoon coal seams as described in claim 1, characterized in that, The primary coal facies types include rain-fed oligotrophic swamps, mixed-replenishment mesotrophic swamps, and mineral-fed eutrophic swamps.

7. The method for coal facies identification of delta-tidal flat-lagoon coal seams as described in claim 1, characterized in that, The secondary coal facies types include rain-fed woody swamps, rain-fed moss swamps, rain-fed mixed woody and moss swamps; mixed herbaceous woody mixed swamps, mixed moss-woody-fern mixed swamps, mixed herbaceous swamps; mineral-fed herbaceous swamps, mineral-fed woody swamps, and mineral-fed aquatic swamps.

8. A coal facies identification system for delta-tidal flat-lagoon coal seams, characterized in that, include: The data acquisition module is used to collect coal samples from the Late Paleozoic era and determine their total sulfur content (St), ash yield (Ad), acidity / alkalinity index (H), and sedimentary environment as measurement parameters. The primary coal facies type classification module is used to classify the peat bog where the coal sample is located according to the measured parameters. Taking the primary coal facies type classification result of Ad as the standard, the module counts the number of consistency results between Ad and St, H, and sedimentary environment discrimination results, and determines the discrimination reliability of primary coal facies type based on the number of consistency results. The secondary coal facies classification module is used for Late Paleozoic delta-tidal flat-lagoon coal seams. It analyzes vitrinite subtypes and microscopic components directly related to vegetation within the coal seam. The module revises the Plant Tissue Preservation Index (TPI) by removing non-structural components such as matrix vitrinite and detrital vitrinite, and revises the Vegetation Index (VI) by introducing filamentous and cutinite to differentiate between woody and non-woody contributions. Based on the revised TPI and VI, secondary coal facies classification is performed on the basis of primary coal facies types. The consistency of the two secondary coal facies classification results is assessed to determine the reliability of the secondary coal facies classification. The credibility generation module is used to obtain the final credibility of the coal phase type by product of the credibility of the primary coal phase type and the credibility of the secondary coal phase type, and to perform coal phase discrimination based on the final credibility.

9. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores a program that, when executed by the processor, causes the processor to perform the steps of the coal facies identification method for delta-tidal flat-lagoon coal seams as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the coal facies identification method for delta-tidal flat-lagoon coal seams according to any one of claims 1 to 7.

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