A method for predicting a hydrogen resource prospective area
By constructing a parameter system and Bayesian analysis methods, combined with multi-source data integration and spatial overlay analysis, the problem of predicting distant areas in natural hydrogen resource exploration was solved, and efficient and accurate hydrogen resource exploration was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- OIL & GAS SURVEY CGS
- Filing Date
- 2026-03-09
- Publication Date
- 2026-06-19
Smart Images
Figure CN121787748B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of geological exploration technology, and in particular to a method for predicting hydrogen resource prospective areas. Background Technology
[0002] Currently, the exploration of natural hydrogen resources at home and abroad is still in the early exploratory stage. The existing natural hydrogen data is very limited, and there is a lack of directly usable data, making it difficult to effectively support the prediction of potential areas. Even when combined with the comprehensive analysis of previous data, the main methods are based on subjective experience and judgment, and there is a lack of quantitative prediction methods for potential areas.
[0003] Clearly, current research and exploration of natural hydrogen resources is still in its infancy, with very limited progress. Exploration techniques and methods are immature, lacking resource evaluation and site selection methods, including methods for predicting potential exploration areas. Furthermore, there is a lack of techniques for extracting key information on the correlation between natural hydrogen accumulation and reservoir formation from massive amounts of multi-source heterogeneous geological data, which hinders the discovery of natural hydrogen resources. Therefore, establishing predictive methods for potential natural hydrogen resource exploration areas is a critical technical issue that urgently needs to be addressed. Summary of the Invention
[0004] In view of this, the purpose of this application is to provide a method for predicting potential hydrogen resource areas, which can accurately predict potential resource areas rich in natural hydrogen, improve exploration efficiency, and reduce exploration risks.
[0005] This application provides a method for predicting potential hydrogen resource areas, the prediction method comprising:
[0006] Based on the knowledge of the generation and accumulation mechanism of natural hydrogen, a parameter system for predicting the prospect of hydrogen resources is constructed; wherein, the parameter system includes multiple hydrocarbon accumulation elements and multiple geological parameters corresponding to each hydrocarbon accumulation element;
[0007] According to the parameter system, multi-source data of the target area are obtained from multiple data sources and integrated. Through rasterization processing, a spatial-attribute data model corresponding to each hydrocarbon accumulation element in the parameter system in the target area is obtained. The spatial-attribute data model corresponding to each hydrocarbon accumulation element includes a spatial-attribute data sub-model corresponding to each geological parameter. The spatial-attribute data sub-model corresponding to each geological parameter includes a parameter layer corresponding to each data source.
[0008] Based on the spatial-attribute data model of each hydrocarbon accumulation element in the target area, the hydrogen resource attribute data of each hydrocarbon accumulation element in the target area are determined by Bayesian analysis.
[0009] A spatial overlay analysis based on a product model is performed on the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area to determine the potential hydrogen resource areas within the target area.
[0010] This application embodiment also provides a prediction device for hydrogen resource prospective areas, the device comprising:
[0011] The module is used to construct a parameter system for predicting the future prospects of hydrogen resources based on the knowledge of the generation and transport mechanism of natural hydrogen; wherein, the parameter system includes multiple hydrocarbon accumulation elements and multiple geological parameters corresponding to each hydrocarbon accumulation element;
[0012] The integration module is used to acquire and integrate multi-source data of the target area from multiple data sources according to the parameter system, and obtain the spatial-attribute data model of each hydrocarbon accumulation element in the parameter system in the target area through rasterization processing; wherein, the spatial-attribute data model corresponding to each hydrocarbon accumulation element includes a spatial-attribute data sub-model corresponding to each geological parameter; the spatial-attribute data sub-model corresponding to each geological parameter includes a parameter layer corresponding to each data source;
[0013] The determination module is used to determine the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area based on the spatial-attribute data model corresponding to each hydrocarbon accumulation element in the target area, using Bayesian analysis methods.
[0014] The overlay module is used to perform spatial overlay analysis based on a product model on the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area, and to determine the potential hydrogen resource areas within the target area.
[0015] This application also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the hydrogen resource prospect prediction method described above are performed.
[0016] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the hydrogen resource prospect prediction method described above.
[0017] This application provides a method for predicting potential hydrogen resource areas. It constructs a parameter system for predicting potential hydrogen resources, obtains spatial-attribute data models corresponding to hydrogen accumulation elements from multi-source heterogeneous data according to the parameter system, determines hydrogen resource attribute data through Bayesian analysis, and determines potential hydrogen resource areas by performing spatial overlay analysis among accumulation elements.
[0018] In this way, by constructing a parameter system, key information related to the formation of natural hydrogen reservoirs can be extracted from existing geological data. By combining this key information with quantitative analysis of potential resource areas, the potential hydrogen resource areas can be accurately predicted, which can indirectly improve exploration efficiency and reduce exploration risks.
[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0021] Figure 1 A flowchart of a method for predicting hydrogen resource prospect areas provided in an embodiment of this application is shown;
[0022] Figure 2 A schematic diagram of a parameter system provided in an embodiment of this application is shown;
[0023] Figure 3 This illustration shows a schematic diagram of the construction process of a spatial-attribute data model provided in an embodiment of this application;
[0024] Figure 4 This paper shows a schematic diagram of the structure of a hydrogen resource prospect prediction device provided in an embodiment of this application;
[0025] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.
[0027] Research has revealed that the exploration of natural hydrogen resources both domestically and internationally is still in its early stages. Existing data on natural hydrogen is very limited, lacking readily available data that can effectively support the prediction of potential exploration areas. Even when combined with comprehensive analysis of previous data, the main methods are based on subjective experience and lack quantitative prediction methods for potential exploration areas.
[0028] Clearly, current research and exploration of natural hydrogen resources is still in its infancy, with very limited progress. Exploration techniques and methods are immature, lacking resource evaluation and site selection methods, including methods for predicting potential exploration areas. Furthermore, there is a lack of techniques for extracting key information on the correlation between natural hydrogen accumulation and reservoir formation from massive amounts of multi-source heterogeneous geological data, which hinders the discovery of natural hydrogen resources. Therefore, establishing predictive methods for potential natural hydrogen resource exploration areas is a critical technical issue that urgently needs to be addressed.
[0029] Based on this, embodiments of this application provide a method for predicting potential hydrogen resource areas, so as to accurately predict potential resource areas rich in natural hydrogen.
[0030] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for predicting hydrogen resource potential areas provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the prediction method includes:
[0031] S101. Based on the knowledge of the generation and transport mechanism of natural hydrogen, construct a parameter system for predicting the future prospects of hydrogen resources.
[0032] The parameter system includes multiple hydrocarbon accumulation elements and multiple geological parameters corresponding to each hydrocarbon accumulation element.
[0033] It should be noted that the exploration of natural hydrogen resources both domestically and internationally is still in its early exploratory stage. Existing natural hydrogen data is very limited, lacking directly usable data, and exploration techniques are immature. Currently, judgments are mainly based on subjective experience and understanding of geological knowledge, lacking a complete and systematic method for hydrogen resource prediction. To address this technical problem, this application's embodiments first systematically analyze the mechanisms of natural hydrogen generation and accumulation, modeling key information related to natural hydrogen reservoir formation, and forming a parameter system for predicting future hydrogen resources.
[0034] In one possible implementation, step S101 may include:
[0035] S1011. Based on the knowledge of the generation and accumulation mechanism of natural hydrogen, the source region, reservoir, caprock and migration trap, which are the elements of natural hydrogen accumulation, are respectively used as the first-level indicators of the parameter system.
[0036] S1012. For each hydrocarbon accumulation element, determine the geological conditions formation judgment principle corresponding to that hydrocarbon accumulation element.
[0037] Among them, different geological conditions are determined for the source areas according to different natural hydrogen formation mechanisms. The geological conditions for the serpentinization hydrogen source area are determined by whether there is an iron-rich rock body. The geological conditions for the radiolysis hydrogen source area are determined by whether there is a uranium-rich rock body. The geological conditions for the deep degassing source area are determined by whether there is a deep source.
[0038] The principle for judging the formation of geological conditions corresponding to a reservoir is whether there are high-porosity strata.
[0039] The principle for judging the geological conditions corresponding to the caprock is whether there is a low-permeability stratum.
[0040] The geological conditions for determining the formation of migration traps are determined by at least one of the following: whether there is a migration path from the source region to the reservoir, the efficiency of the migration path, and whether there are potential geological conditions that lead to hydrogen retention or loss during migration.
[0041] S1013. Based on the judgment principle for each geological condition, determine at least one relevant geological parameter and the parameter index corresponding to each geological parameter according to the existing geological data.
[0042] It should be noted that, depending on the study area, the formation of each geological condition is related to multiple geological parameters. However, due to variations in actual geological conditions and data, existing geological data may not encompass all relevant parameters, resulting in missing or unavailable parameters. Therefore, this step requires selecting at least one geological parameter related to the formation criteria of each geological condition based on the available geological data to ensure the feasibility of the parameter system. After identifying the relevant geological parameters, corresponding parameter indicators can be determined based on their characteristics, such as indicative indicators of their existence and / or numerical indicators containing specific values. Furthermore, geological parameters can be further screened based on existing geological data for the target area to construct a more targeted parameter system.
[0043] S1014. Based on the natural hydrogen accumulation elements, the geological condition formation judgment principle corresponding to each accumulation element, at least one geological parameter related to each geological condition formation judgment principle, and the parameter index corresponding to each geological parameter, the parameter system is integrated to form the parameter system.
[0044] In one example, see Figure 2 , Figure 2 This is a schematic diagram of a parameter system provided in an embodiment of this application. For example... Figure 2As shown, for the source region, the geological parameters corresponding to the presence of iron-rich rock bodies include at least one of the following: ultramafic rock bodies, gravity and magnetic anomalies, banded iron-bearing formations, and chromium / magnetite deposits; the geological parameters corresponding to the presence of uranium-rich rock bodies include at least one of the following: Precambrian bedrock, uranium deposits, favorable uranium mineralization areas, and young granitic rock bodies; the geological parameters corresponding to the presence of deep sources include at least one of the following: deep fault zones, high heat flow zones, geologically active zones, tectonic suture zones, and large surface faults. For the reservoir, the geological parameters corresponding to the presence of high-porosity strata include at least one of the following: high-porosity and high-permeability sedimentary basins, high-porosity and high-permeability sedimentary rock layers, and fractured non-sedimentary rock layers (crystalline rock bodies / igneous rock bodies / bedrock). For the caprock, the geological parameters corresponding to the presence of low-permeability strata include at least one of the following: underground salt layers, dense low-porosity and low-permeability sedimentary basins, dense low-porosity and low-permeability sedimentary rock layers, and fractured non-sedimentary rock layers.
[0045] It is worth noting that migration traps are a key link connecting the "source" and the "reservoir". Therefore, this application also introduces migration traps and systematically constructs a parameter system corresponding to the migration trap reservoir element. This enables the assessment of the technical issues of whether hydrogen can flow to the reservoir and whether it can be retained, significantly reducing the risk of exploration gaps.
[0046] In practical implementation, the steps for determining relevant geological parameters based on the principles for judging the geological conditions of migration traps include:
[0047] Step a1: Determine whether there are candidate migration paths from the source region to the reservoir based on existing geological data; wherein, the candidate migration paths include fractures, unconformities and high-permeability layers.
[0048] Step a2: If present, evaluate the channel efficiency of each candidate transport path based on whether the fracture is closed or open, the transport efficiency of the unconformity surface, and the permeability of the high-permeability layer.
[0049] And step a3, if present, identify the hydrogen display on each candidate migration path and the geological elements in the migration path that cause hydrogen retention and loss, such as hydrogen escape.
[0050] Among them, the geological parameters can include the existence of the aforementioned topography and landforms, or related numerical values of the aforementioned topography and landforms. For example, for a favorable uranium mineralization area in the source region, the geological parameter can be whether the favorable uranium mineralization area exists (for example, 0 can represent non-existence and 1 can represent existence), or it can be the uranium content value of the favorable uranium mineralization area.
[0051] S102. According to the parameter system, obtain and integrate multi-source data of the target area from multiple data sources, and obtain the spatial-attribute data model of each hydrocarbon accumulation element in the parameter system in the target area through rasterization processing.
[0052] The spatial-attribute data model corresponding to each hydrocarbon accumulation element includes a spatial-attribute data sub-model corresponding to each geological parameter; the spatial-attribute data sub-model corresponding to each geological parameter includes a parameter layer corresponding to each data source.
[0053] In this step, after determining the parameter system, key data related to natural hydrogen accumulation can be extracted from existing geological data based on this parameter system, and a systematic spatial-attribute data model can be formed to facilitate further prospective area analysis.
[0054] In one possible implementation, step S102 may include:
[0055] S1021. Obtain the raw data of the target area from multiple data sources.
[0056] The data sources include geographic, geological, geochemical, geophysical, and remote sensing data. For example, in geology: 200,000–500,000 sq. m geological maps, fault / sequence stratigraphy data; lithological / uranium deposit locations (CGS open literature / industry data), etc. In geophysics: Bouguer gravity, aeromagnetic, aeromagnetic, and magnetotelluric sounding data; faults and layer thicknesses derived from seismic interpretation; MT / electrical resistivity (if applicable) data, etc. In geochemistry: He, H2, CO2 soil gas and geothermal gas; hydrochemical data (Eh, pH, dissolved hydrogen), etc. In remote sensing: DEM (SRTM / ASTER), Sentinel-2 / 1 extracted linear bodies, alteration information, etc.
[0057] S1022. For each geological parameter corresponding to each hydrocarbon accumulation element in the parameter system, based on the representation of the geological parameter in the data source, the corresponding extraction method is used to extract the data corresponding to the geological parameter in the target area from the original data of each data source.
[0058] In this step, data is extracted from each data source according to the geological parameters specified in the parameter system. Specifically, for each geological parameter, an appropriate extraction method is used based on its representation in different data sources such as geology, geophysics, geochemistry, and remote sensing: for point data (such as mineral deposit locations and hot spring points), it is converted into area data through spatial interpolation or buffer analysis; for linear data (such as fault zones), it is converted into area data through buffer analysis; for area data (such as rock mass distribution and sedimentary basins), its spatial extent is directly extracted; for raster data (such as gravity anomalies and magnetic anomalies), the raster values of the corresponding area are directly extracted. Furthermore, the extracted data needs to be unified with a spatial coordinate system to ensure that all data are processed in the same coordinate system.
[0059] S1023. Rasterize and encode the data corresponding to each geological parameter in each data source to form a rasterized parameter layer corresponding to each data source.
[0060] In this step, GIS-related technologies are used to integrate and fuse multi-source heterogeneous geological big data to achieve hierarchical and classified management. First, the data corresponding to each geological parameter in each data source is converted to raster to a uniform resolution (e.g., 1km). Then, the data can be uniformly processed through normalization, feature encoding, etc. For example, continuous variables are uniformly normalized to [0,1]; categorical variables are one-hot encoded.
[0061] Specifically, this method can be executed by a GIS platform integrating a Python scripting engine. The Python script calls the GIS interface to convert all vector data into raster data of uniform resolution (e.g., 1km × 1km). For continuous variables (e.g., porosity, fracture distance), the data is uniformly normalized to the [0,1] interval. For categorical variables (e.g., lithology), one-hot encoding is used to ensure the uniformity of data dimensions, providing a foundation for subsequent matrix operations.
[0062] In this rasterized parameter layer, each raster has a parameter index value for a geological parameter. Raster data is a method of dividing space into a regular grid, with each raster (grid) being a unit. Each unit is assigned a corresponding attribute value to represent an entity, and each raster is given a unique code and attribute value. The location of each unit is defined by its row and column numbers, and the location of the entity it represents is implicitly contained in the raster's row and column positions. In one example, converting a vector-formatted fault line layer into a 1km resolution raster distance map yields the Euclidean distance from each raster to the nearest deep fault, which is a parameter index value corresponding to the geological parameter of a deep fault zone.
[0063] S1024. Integrate the rasterized parameter layers corresponding to each geological parameter in each data source to form a spatial-attribute data sub-model corresponding to each geological parameter.
[0064] S1025. Integrate the spatial-attribute data sub-models of each geological parameter corresponding to each hydrocarbon accumulation element to obtain the spatial-attribute data model corresponding to each hydrocarbon accumulation element.
[0065] For steps S1024 and S1025 above, by merging the rasterized parameter layers corresponding to each geological parameter in each data source, and merging the spatial-attribute data sub-models of each geological parameter corresponding to each hydrocarbon accumulation element, a spatial-attribute data model corresponding to each hydrocarbon accumulation element is obtained. The final model system may include: the main model of the four major hydrocarbon accumulation elements, and multiple (e.g., more than ten to more than twenty) sub-models (parameter layers) of geological parameters selected according to the geological characteristics of the target area. Furthermore, for each parameter layer, according to the data source, it is divided into five categories of layers: geographic, geological, geophysical, geochemical, and remote sensing, for classification and management, and used for subsequent prospect evaluation.
[0066] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating the construction process of a spatial-attribute data model provided in an embodiment of this application. For example... Figure 3 As shown, various types of data from geography, geology, geochemistry, geophysics, and remote sensing are used to form a multi-source heterogeneous basic database through GeoCloud (a comprehensive geological information service system); Python technology is used to perform hierarchical classification management based on the constructed parameter system, ultimately forming a GIS spatial-attribute data model.
[0067] S103. Based on the spatial-attribute data model corresponding to each hydrocarbon accumulation element in the target area, determine the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area using Bayesian analysis.
[0068] In one possible implementation, step S103 may include:
[0069] S1031. For each geological parameter corresponding to each hydrocarbon accumulation element, determine the initial data sufficiency index value of each raster in the target area with respect to the geological parameter based on the rasterized parameter layer corresponding to each data source in the spatial-attribute data sub-model.
[0070] In this step, the data sufficiency index is related to the quantity and quality of data and its correlation with natural hydrogen. The data sufficiency index value can characterize the probability of hydrogen resource existence. In specific implementation, the initial data sufficiency index value of a raster with respect to a geological parameter can be determined based on the parameter value of the geological parameter at each raster point in the rasterized parameter layer corresponding to each data source, i.e., the raster is assigned a value. Corresponding to the above example, the initial data sufficiency index value can be converted according to the Euclidean distance from the raster to the nearest deep fault, thereby characterizing the probability of deep degassing indication. For example, Table 1 below is a mapping table of geological parameter values in the source area, Table 2 is a mapping table of geological parameter values in the reservoir, and Table 3 is a mapping table of geological parameter values in the caprock.
[0071] Table 1. Mapping table of geological parameter values in the source region
[0072]
[0073] Table 2 Mapping table of geological parameter values in reservoirs
[0074]
[0075] Table 3 Mapping table of geological parameter values in caprock
[0076]
[0077] Furthermore, as mentioned above, this embodiment of the application further classifies each parameter layer into five categories—geographic, geological, geophysical, geochemical, and remote sensing—based on the data source for categorized management. Considering the varying data quality from different data sources, weights can be assigned to each data source separately. Therefore, for a given raster, the initial data sufficiency index value can be determined by combining the parameter index values of the geological parameters with the weights of the data sources from which those index values originate.
[0078] S1032. Uncertainty quantification analysis is performed using Bayesian analysis, and the initial data sufficiency index value of each raster with respect to the geological parameter is iteratively updated based on the analysis results to obtain the data sufficiency index value of each raster with respect to the geological parameter.
[0079] S1033. The data sufficiency index value of each geological parameter corresponding to each hydrocarbon accumulation element in each grid cell of the target area shall be used as the hydrogen resource attribute data corresponding to the hydrocarbon accumulation element in the target area.
[0080] Regarding step S1033 above, it should be noted that when the geological parameter has data in multiple data sources, the average value of the data sufficiency index value corresponding to each data source can be taken according to the weight of each data source to obtain the data sufficiency index value of the raster for the geological parameter.
[0081] For each hydrocarbon accumulation element, the data sufficiency index value of each geological parameter under that element in each grid cell in the target area is summarized as the hydrogen resource attribute data corresponding to that hydrocarbon accumulation element in the target area.
[0082] Furthermore, step S1032 may include:
[0083] Events are categorized into complementary events based on geological parameters, and regional prior probabilities are set. For each grid cell, a likelihood function value is calculated using a pre-defined conditional probability table based on the initial data sufficiency index value of the geological parameters. This conditional probability table is constructed based on the statistical relationship between discovered hydrogen reservoirs or anomalies and geological parameters. A Bayesian analysis model is used to calculate the posterior probability based on the regional prior probability and the likelihood function value, and this posterior probability is used as the quantification uncertainty analysis result for that grid cell. The data sufficiency index value of that grid cell regarding the geological parameters is updated based on the quantification uncertainty analysis result, and the uncertainty quantification analysis is performed again until a preset condition is met. For example, the quantification uncertainty analysis result of the grid cell meets a preset threshold condition or the number of updates exceeds a preset number.
[0084] Here, the core of Bayesian analysis is uncertainty quantification and propagation analysis. In the embodiments of this application, multi-layered Bayesian analysis can be used to quantify the uncertainty of data in order to achieve more accurate and reliable predictions. For example, the uncertainty of the data sufficiency index value of a single geological parameter can be analyzed, as can the uncertainty of the hydrogen resource attribute value of each hydrocarbon accumulation element. Furthermore, the uncertainty of the superimposed results can be analyzed, that is, the uncertainty of the final result is quantified after multiple uncertain parameters are superimposed.
[0085] For example, Bayesian analysis is used to perform a quantitative uncertainty analysis on the attribute value of each parameter, and the attribute value of each parameter is further adjusted based on the results of this analysis. Figure 3Using the three-source model (S1 serpentinization, S2 radiation cleavage, S3 deep degassing) as complementary events, let the regional prior π = [0.33, 0.33, 0.34]; prior probabilities: P(S1 serpentinization) = 0.20; P(S2 deep degassing) = 0.60; P(S3 radiation cleavage) = 0.20; examples of likelihood functions: P("distance from deep fracture < 5km"|S2 deep degassing) = 0.85; P("H2 > 50ppm"|S2 deep degassing) = 0.70; P("high He anomaly"|S2 deep degassing) = 0.75.
[0086] S104. Perform spatial overlay analysis based on a product model on the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area to determine the potential hydrogen resource areas within the target area.
[0087] In one possible implementation, step S104 may include:
[0088] S1041. Based on the weight of geological parameters in the hydrocarbon accumulation elements, normalize and weight the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area to determine the hydrogen resource attribute value of each grid cell of each hydrocarbon accumulation element in the target area.
[0089] In this step, for each hydrocarbon accumulation element, the data sufficiency index values of all geological parameters under that element are first normalized to the [0,1] interval within the group. The normalization formula is: Normalized value = (original value - minimum value) / (maximum value - minimum value). Then, the data sufficiency index values of each normalized geological parameter are weighted and summed. The weight is the weight of the geological parameter in the hydrocarbon accumulation element, thus obtaining the hydrogen resource attribute value of the hydrocarbon accumulation element in the raster.
[0090] S1042. For each grid cell in the target area, perform spatial overlay analysis based on a product model on the hydrogen resource attribute values of each hydrocarbon accumulation element to determine the comprehensive favorable index of that grid cell. Here, the comprehensive favorable index (0-1) can be obtained using a product model. The formula for spatial overlay analysis based on a product model for each grid cell is expressed as follows:
[0091]
[0092] In the formula, This represents the overall favorable index. This indicates the hydrogen resource attribute value of the source region. This represents the hydrogen resource attribute value of the reservoir. This indicates the hydrogen resource attribute value of the caprock. This represents the hydrogen resource attribute value of the migration trap.
[0093] S1043. Based on the comprehensive advantage index of each grid cell in the target area, delineate the potential hydrogen resource areas within the target area.
[0094] In this step, the comprehensive advantage index of each grid cell can characterize the likelihood of that grid cell containing natural hydrogen. Therefore, different ranges can be set for the comprehensive advantage index, such as high, medium, and low; based on the distribution of grid cells with high comprehensive advantage indices, potential hydrogen resource areas within the target region can be delineated. Alternatively, regional fitting can be performed on the comprehensive advantage index of each grid cell to delineate potential hydrogen resource areas within the target region.
[0095] Furthermore, each raster in the spatial-attribute data model is associated with a link to the original data in the data source; therefore, step S1042 may further include:
[0096] For each grid cell in the target area, the formation time of the geological parameters corresponding to each hydrocarbon accumulation element in the original data is obtained based on the link of the original data in the data source associated with the grid cell. Based on the formation time of the geological parameters corresponding to each hydrocarbon accumulation element, the temporal consistency of the hydrogen resource attribute value of each hydrocarbon accumulation element is determined. If there is a discrepancy, a penalty function is applied to the comprehensive favorable index of the grid cell based on the temporal mismatch between each hydrocarbon accumulation element to obtain the corrected comprehensive favorable index of the grid cell.
[0097] Here, the coupling and superposition effects of various hydrocarbon accumulation elements can be analyzed, including spatiotemporal combinations and matching. If there is inconsistency, the temporal mismatch between the various hydrocarbon accumulation elements is determined, and a penalty term is introduced for the temporal consistency of "source → (migration trap) → reservoir → cap". For example, if the recent volcanic / fault activity is mismatched with the reservoir-cap formation period, a penalty function is applied, reducing the comprehensive favorable index by 0.05–0.15.
[0098] In one possible implementation, the overall favorable index of the raster can be obtained by applying a penalty function to the overall favorable index of the raster based on the temporal mismatch between each hydrocarbon accumulation element using the following formula:
[0099]
[0100] in, This indicates the temporal mismatch between various hydrocarbon accumulation elements. ,in, Indicates the source region generation period. Indicates the reservoir formation period. Indicates the period of cap formation. Indicates the formation period of the migration trap; This represents the environmental sensitivity coefficient, which is dynamically adjusted based on the tectonic background (such as fracture development density) of each grid cell. This represents the overall advantage index of the grid. This represents the overall favorable index after the raster correction. Among them, The higher the value, the greater the risk that the reservoir and caprock were not yet fully developed when hydrogen was generated. Thus, by introducing an exponential penalty factor based on time offset, rather than a fixed score, the non-linear growth of geological risk can be reflected.
[0101] Subsequently, the hydrogen resource prospective areas can be delineated based on the revised comprehensive favorable index.
[0102] Furthermore, step S104 may also include: combining Bayesian statistical methods and Monte Carlo simulation methods to perform uncertainty analysis on the superposition of hydrogen resource attribute values of various hydrocarbon accumulation elements, and determining potential hydrogen resource areas under different probability conditions. The core of this part is uncertainty quantification and propagation analysis. First, the uncertainty of individual parameters is analyzed, as the probability value of each hydrocarbon accumulation element (source, reservoir, cap, and transport) itself has uncertainty; second, the uncertainty of the superposition of the above results is quantified by superimposing multiple uncertain parameters.
[0103] In practical implementation, for each grid cell, a parameter probability distribution is defined: a prior distribution for each parameter is established, and a probability value and an uncertainty quantity representing the confidence level of each parameter in each grid cell are defined; a probability distribution type is selected, such as a Beta distribution; the conditional probability of each hydrocarbon accumulation element and the probability of ultimately forming a hydrogen reservoir is defined, forming a Bayesian network model; Monte Carlo simulation is performed on the Bayesian network model, and multiple rounds of random sampling are used to simulate the probability of matching various hydrocarbon accumulation elements; the Bayesian update formula and uncertainty analysis are integrated, and the probability of observing hydrogen reservoir appearance is simulated based on the known hydrogen reservoir appearance points and given combinations of hydrocarbon accumulation elements; the simulation results obtained from the multiple rounds of Monte Carlo simulation are calculated and statistically analyzed to generate probability distribution maps of potential hydrogen resource areas under different probability conditions.
[0104] By employing Bayesian techniques, uncertainty analysis was conducted on the coupled and superimposed effects of hydrocarbon accumulation factors. Finally, based on a comprehensive analysis combining GIS and Monte Carlo methods, potential areas for natural hydrogen resources were predicted under different probability conditions (5%, 50%, and 90%).
[0105] In one example, a prediction for a potential natural hydrogen resource area in a fault zone is made: the deep degassing potential and migration controlled by the major fault are significant; the pores in the Cenozoic basin sandstone / basalt within the zone and adjacent areas can serve as reservoirs, but the risks of caprock continuity and fault penetration coexist.
[0106] Key layer merit values: S3 (deep), T1 / T3 (migration), C3 is negative (risk of fracture penetration), which needs to be compensated by a high caprock thickness.
[0107] P50 Outlying Area (strip-shaped summary, approximately 22,000 km²):
[0108] The middle section of C1 (approximately 33.5-36.5°N, 118-120.5°E) covers an area of approximately 9,000 km², with a P50 of 0.48. It is mainly characterized by fault segmentation and turning points, as well as Cenozoic rift basins.
[0109] The northern section of C2 (approximately 36.5-40.5°N, 119-121.5°E) covers an area of approximately 8,000 km², with a P50 of 0.46. The main controlling factors are deep thermal anomalies and basic dikes.
[0110] C3 southern section (approximately 31-33°N, 117-119°E), area ~0.5 million km2, P50=0.44, main control factor: large difference in caprock thickness; medium to high uncertainty.
[0111] Risk warning: C3 (caprock continuity) is the main source of uncertainty. It is recommended to include the "fracture continuity probability" as a strong penalty in Monte Carlo simulation.
[0112] The present application provides a method for predicting potential hydrogen resource areas. Compared with the prior art, the core of the method of the present application is: (1) The present application realizes the simulation of dynamic reservoir formation process through the "spatial-attribute data model". The present application improves the evaluation accuracy from the zone level to the grid level through raster modeling, which solves the technical problem that the prior art cannot accurately delineate the exploration target area. (2) The present application adopts the product mode for spatial superposition analysis, which reflects the necessary conditions for geological reservoir formation. Given the high diffusivity of hydrogen, the spatiotemporal matching requirements are much higher than those for oil and gas. The present application creatively introduces a penalty function based on temporal consistency. (3) The prior art relies on mature well logging / seismic data, which is often difficult to obtain in the early stages of hydrogen exploration. The present application constructs a prediction mechanism that can work effectively in low-level exploration areas by building a parameter system, integrating the spatial-attribute data model, introducing data sufficiency indicators and Bayesian uncertainty analysis.
[0113] Please see Figure 4 , Figure 4 This is a schematic diagram of a hydrogen resource prospecting device provided in an embodiment of this application. Figure 4 As shown, the prediction device 400 includes:
[0114] Module 410 is used to construct a parameter system for predicting hydrogen resource prospects based on the knowledge of the generation and transport mechanism of natural hydrogen; wherein, the parameter system includes multiple hydrocarbon accumulation elements and multiple geological parameters corresponding to each hydrocarbon accumulation element;
[0115] The integration module 420 is used to acquire and integrate multi-source data of the target area from multiple data sources according to the parameter system, and obtain the spatial-attribute data model of each hydrocarbon accumulation element in the parameter system in the target area through rasterization processing; wherein, the spatial-attribute data model corresponding to each hydrocarbon accumulation element includes a spatial-attribute data sub-model corresponding to each geological parameter; the spatial-attribute data sub-model corresponding to each geological parameter includes a parameter layer corresponding to each data source;
[0116] The determination module 430 is used to determine the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area by Bayesian analysis based on the spatial-attribute data model corresponding to each hydrocarbon accumulation element in the target area.
[0117] The overlay module 440 is used to perform spatial overlay analysis based on a product model on the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area, and to determine the hydrogen resource prospective areas in the target area.
[0118] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.
[0119] The memory 520 stores machine-readable instructions that can be executed by the processor 510. When the electronic device 500 is running, the processor 510 and the memory 520 communicate via the bus 530. When the machine-readable instructions are executed by the processor 510, the steps of the hydrogen resource prospect prediction method in the above method embodiment can be performed. For specific implementation, please refer to the method embodiment, which will not be repeated here.
[0120] This application also provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it can execute the steps of the hydrogen resource prospect prediction method in the above method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.
[0121] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0122] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.
[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0124] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0125] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0126] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for predicting potential hydrogen resource areas, characterized in that, The prediction method includes: Based on the knowledge of the natural hydrogen generation and accumulation mechanism, a parameter system for predicting hydrogen resource prospects is constructed. The parameter system includes multiple hydrocarbon accumulation elements and multiple geological parameters corresponding to each hydrocarbon accumulation element. The primary indicators of the parameter system include source region, reservoir, caprock, and migration trap. The source region includes three types: serpentinization hydrogen source region, radiolysis hydrogen source region, and deep degassing source region, which correspond to different geological conditions and formation judgment principles. According to the aforementioned parameter system, multi-source data of the target area are acquired and integrated from multiple data sources. Through rasterization processing, a spatial-attribute data model corresponding to each hydrocarbon accumulation element in the parameter system is obtained for the target area. Each spatial-attribute data model for each hydrocarbon accumulation element includes a spatial-attribute data sub-model corresponding to each geological parameter. Each spatial-attribute data sub-model corresponding to each geological parameter includes parameter layers corresponding to each data source. The data sources include geographic, geological, geochemical, geophysical, and remote sensing data. Each raster in the spatial-attribute data model is linked to the original data in the data source. Based on the spatial-attribute data model of each hydrocarbon accumulation element in the target area, the hydrogen resource attribute data of each hydrocarbon accumulation element in the target area are determined by Bayesian analysis. A spatial overlay analysis based on a product model is performed on the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area to determine the potential hydrogen resource areas within the target area, including: Based on the weight of geological parameters in the hydrocarbon accumulation elements, the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area are normalized and weighted to determine the hydrogen resource attribute value of each grid cell of each hydrocarbon accumulation element in the target area. For each grid cell in the target area, a spatial overlay analysis based on a product model is performed on the hydrogen resource attribute values of each hydrocarbon accumulation element to determine the comprehensive favorable index of that grid cell. For each grid cell in the target area, the formation time of the geological parameters corresponding to each hydrocarbon accumulation element in the original data is obtained based on the link to the original data in the data source associated with the grid cell. Based on the formation time of the geological parameters corresponding to each hydrocarbon accumulation element, the temporal consistency of each hydrocarbon accumulation element is determined; If there is a discrepancy, a penalty function is applied to the comprehensive favorable index of the raster based on the temporal mismatch between each hydrocarbon accumulation element to obtain the corrected comprehensive favorable index of the raster. Based on the comprehensive favorable index corrected for each grid cell in the target area, the potential hydrogen resource area within the target area is delineated.
2. The method according to claim 1, characterized in that, According to the parameter system, multi-source data of the target area are acquired and integrated from multiple data sources. Through rasterization processing, a spatial-attribute data model corresponding to each hydrocarbon accumulation element in the parameter system in the target area is obtained, including: Obtain raw data of the target region from multiple data sources; For each geological parameter corresponding to each hydrocarbon accumulation element in the parameter system, based on the representation of the geological parameter in the data source, the corresponding extraction method is used to extract the data corresponding to the geological parameter in the target area from the original data of each data source; The data corresponding to each geological parameter in each data source is rasterized and encoded to form a rasterized parameter layer for each data source; wherein, each raster in the rasterized parameter layer has a parameter index value of the geological parameter; Integrate the rasterized parameter layers corresponding to each geological parameter from various data sources to form a spatial-attribute data sub-model for each geological parameter; By integrating the spatial-attribute data sub-models of each geological parameter corresponding to each hydrocarbon accumulation element, a spatial-attribute data model corresponding to each hydrocarbon accumulation element is obtained.
3. The method according to claim 1, characterized in that, Based on the knowledge of the natural hydrogen generation and transport mechanisms, a parameter system for predicting hydrogen resource prospects is constructed, including: Based on the knowledge of the generation and accumulation mechanism of natural hydrogen, the source region, reservoir, caprock and migration trap, which are the elements of natural hydrogen accumulation, are respectively used as the first-level indicators of the parameter system. For each hydrocarbon accumulation element, the geological conditions formation judgment principle corresponding to that element is determined. Specifically, for the source region, different geological conditions formation judgment principles are determined according to different natural hydrogen formation mechanisms. The geological conditions formation judgment principle for the serpentinization hydrogen source region is the presence of iron-rich rock bodies; the geological conditions formation judgment principle for the radiolysis hydrogen source region is the presence of uranium-rich rock bodies; and the geological conditions formation judgment principle for the deep degassing source region is the presence of deep sources. The geological conditions formation judgment principle for the reservoir is the presence of high-porosity strata; the geological conditions formation judgment principle for the caprock is the presence of low-permeability strata; and the geological conditions formation judgment principle for the migration trap is the presence of a migration path from the source region to the reservoir, the channel efficiency of the migration path, and at least one of the following potential geological conditions that lead to hydrogen retention or loss during migration: Based on the judgment principle for each geological condition, at least one relevant geological parameter and the corresponding parameter index for each geological parameter are determined according to existing geological data. The parameter system is formed by integrating the natural hydrogen accumulation elements, the geological condition formation judgment principles corresponding to each accumulation element, at least one geological parameter related to each geological condition formation judgment principle, and the parameter index corresponding to each geological parameter.
4. The method according to claim 3, characterized in that, For the source region, the geological parameters corresponding to the presence of iron-rich rock bodies include at least one of the following: ultramafic rock bodies, gravity and magnetic anomalies, banded iron-bearing formations, and chromium / magnetite deposits; the geological parameters corresponding to the presence of uranium-rich rock bodies include at least one of the following: Precambrian bedrock, uranium deposits, favorable uranium mineralization areas, and young granitic rock bodies; the geological parameters corresponding to the presence of deep sources include at least one of the following: deep fault zones, high heat flow zones, geological activity zones, tectonic suture zones, and large surface faults. For reservoirs, the geological parameters corresponding to high-porosity strata include at least one of the following: high-porosity and high-permeability sedimentary basins, high-porosity and high-permeability sedimentary rock layers, and fractured non-sedimentary rock layers. For the caprock, the geological parameters corresponding to the low-permeability strata include at least one of the following: underground salt layer, dense low-porosity and low-permeability sedimentary basin, dense low-porosity and low-permeability sedimentary rock layer, and non-sedimentary rock layer with underdeveloped fractures. Based on the principles for determining the geological conditions that form migration traps, the steps for determining relevant geological parameters include: Based on existing geological data, determine whether there are candidate migration paths from the source region to the reservoir; wherein, the candidate migration paths include fractures, unconformities, and high-permeability layers; If present, the channel efficiency of each candidate transport path is evaluated based on whether the fracture is closed or open, the transport efficiency of the unconformity surface, and the permeability of the high-permeability layer. Identify hydrogen features along each candidate migration path and the geological elements along the migration path that cause hydrogen retention and loss.
5. The method according to claim 2, characterized in that, Based on the spatial-attribute data model corresponding to each hydrocarbon accumulation element in the target area, the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target area are determined using Bayesian analysis, including: For each geological parameter corresponding to each hydrocarbon accumulation element, the initial data sufficiency index value of each raster in the target area is determined based on the rasterized parameter layer corresponding to each data source in the spatial-attribute data sub-model. Uncertainty quantification analysis was performed using Bayesian analysis, and the initial data sufficiency index value of each raster with respect to the geological parameter was iteratively updated based on the analysis results to obtain the data sufficiency index value of each raster with respect to the geological parameter. The data sufficiency index value of each geological parameter corresponding to each hydrocarbon accumulation element in each grid cell of the target area is used as the hydrogen resource attribute data corresponding to that hydrocarbon accumulation element in the target area.
6. The method according to claim 5, characterized in that, Uncertainty was quantitatively analyzed using Bayesian analysis, and the initial data sufficiency index value for each raster with respect to the geological parameter was iteratively updated based on the analysis results, resulting in the data sufficiency index value for each raster with respect to the geological parameter, including: Complementary events are classified based on geological parameters, and regional prior probabilities are set. For each grid cell, the likelihood function value is calculated using a preset conditional probability table based on the initial data sufficiency index value of the geological parameters; the conditional probability table is constructed based on the statistical relationship between the discovered hydrogen reservoirs or anomalies and the geological parameters. Using a Bayesian analysis model, the posterior probability is calculated based on the prior probability and likelihood function value of the region, and the posterior probability is used as the result of the quantization uncertainty analysis of the grid. The initial data sufficiency index value of the raster with respect to the geological parameter is updated based on the results of the quantitative uncertainty analysis of the raster, and the uncertainty quantitative analysis is performed again until the preset conditions are met to obtain the data sufficiency index value of the raster with respect to the geological parameter.
7. The method according to claim 1, characterized in that, The formula for spatial superposition analysis based on the product model is expressed as follows: In the formula, This represents the overall favorable index. This indicates the hydrogen resource attribute value of the source region. This represents the hydrogen resource attribute value of the reservoir. This indicates the hydrogen resource attribute value of the caprock; This represents the hydrogen resource attribute value of the migration trap.
8. The method according to claim 1, characterized in that, The corrected comprehensive favorable index of the raster is obtained by applying a penalty function to the comprehensive favorable index of the raster based on the temporal mismatch between each hydrocarbon accumulation element using the following formula: in, This indicates the temporal mismatch between various hydrocarbon accumulation elements. ,in, Indicates the source region generation period. Indicates the reservoir formation period. Indicates the period of cap formation. Indicates the formation period of the migration trap; This represents the environmental sensitivity coefficient, which is dynamically adjusted based on the construction context of each grid cell. This represents the overall advantage index of the grid. This represents the overall favorable index after the grid is corrected.
9. The method according to claim 1, characterized in that, To determine potential hydrogen resource areas within the target region, a spatial overlay analysis based on a product model is performed on the hydrogen resource attribute data corresponding to each hydrocarbon accumulation element in the target region. This also includes: For each grid cell, define the parameter probability distribution: establish the prior distribution of each parameter, define the probability value and the uncertainty representing the confidence of each parameter in each grid cell; select the probability distribution type; define the conditional probability of each hydrocarbon accumulation element and the probability of finally forming a hydrogen reservoir, and form a Bayesian network model. Monte Carlo simulations were performed on the Bayesian network model, and multiple rounds of random sampling were used to simulate the probability of matching various hydrocarbon accumulation elements. By integrating Bayesian update formulas and uncertainty analysis, and given a combination of hydrocarbon accumulation elements based on known hydrogen reservoir show points, the probability of observing hydrogen shows is simulated. The simulation results from multiple rounds of Monte Carlo simulation were statistically analyzed, and probability distribution maps of hydrogen resource prospective areas under different probability conditions were generated.