A method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints, electronic equipment, and computer-readable storage media.

Abstract

This invention discloses a method, electronic device, and computer-readable storage medium for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints. The method includes: first, standardizing the basic geological data of the target tight sandstone gas reservoir area to form a standardized geological database; then, based on the standardized geological database, identifying structural features, lithological features, and sequence stratigraphy, establishing a quantitative coupling control model of "geological structure-sedimentary microfacies-high-frequency sequence stratigraphy," and calculating the comprehensive quantitative value of each feature; finally, based on the comprehensive quantitative value of each feature, classifying favorable fracture development zones. This method, electronic device, and computer-readable storage medium establish a scientific model for predicting fracture development in tight sandstone, improving prediction accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method for predicting fracture development in tight sandstone based on the coupling constraints of multiple geological parameters, electronic equipment, and computer-readable storage media. Background Technology

[0002] Accurate prediction of fracture development patterns is one of the core factors driving the efficient development of tight sandstone gas reservoirs. Traditional methods for predicting fracture development in tight sandstone often rely on single geological parameters or geophysical properties for analysis, making it difficult to adapt to geological backgrounds involving multiple tectonic movements and complex lithological combinations, resulting in insufficient accuracy and reliability of prediction results. While some comprehensive fracture development prediction methods attempt to incorporate multi-factor analysis, their accuracy and reliability still need improvement due to the lack of a clear quantitative coupling model and the failure to focus on the core control logic of fracture development—"geological structure-sedimentary microfacies-high-frequency sequence stratigraphy."

[0003] It is evident that traditional methods for predicting fracture development in tight sandstone are insufficient to provide accurate geological data for exploration deployment, horizontal well trajectory optimization, and fracturing design, resulting in high exploration costs. At the same time, the proportion of inefficient and dry wells is too high, which seriously affects the economic benefits and development efficiency of tight sandstone oil and gas reservoirs. Summary of the Invention

[0004] Based on this, the present invention proposes a method, electronic device and computer-readable storage medium for predicting the development of fractures in tight sandstone based on the coupling constraints of multiple geological parameters, aiming to solve the problem that the prediction accuracy and reliability of traditional methods for predicting the development of fractures in tight sandstone still need to be improved.

[0005] To solve the above problems, the present invention adopts the following technical solution: This invention proposes a method for predicting fracture development in tight sandstone based on the coupling constraints of multiple geological parameters, comprising: Acquire basic geological data of the target tight sandstone gas reservoir area and standardize the basic geological data to form a standardized geological database; Based on the standardized geological database, high-resolution sequence stratigraphy was used to identify multi-level sequence boundaries to delineate each sequence boundary. Combined with fracture development parameter analysis and based on the range of each sequence boundary, the dominant fracture development segments were identified, and the vertical distribution range and lateral spread characteristics of the dominant segments were clarified. Based on the standardized geological database, combined with core observation and well logging response characteristics, and utilizing the spatial constraints of the seismic fusion attribute map, a sedimentary microfacies distribution map of the dominant stratigraphic interval was drawn, and the spatial distribution range and boundary characteristics of different sedimentary microfacies were clarified. Based on the standardized geological database, the formation curvature parameters are extracted using a multi-scale volume curvature calculation method, and the straight-line distance from each well location to the fault is calculated through fault interpretation and fine characterization. Based on the formation curvature parameters and the straight-line distance, combined with tectonic stress field analysis, the predicted fracture development zone is delineated along the dominant strata and a predicted fracture development zone map is generated, and the boundary range, fracture development intensity level, and spatial distribution characteristics of the predicted fracture development zone are clarified. The development of fractures in imaging logging was statistically analyzed, and quantitative values ​​of structural features, lithological features, and sequence characteristics were obtained based on the statistical results. These quantitative values ​​were then normalized. The normalized quantitative values ​​of each feature were compared with the statistical results to determine the influence weights of structural features, lithological features, and sequence characteristics on fracture development. Based on these influence weights, a weighted combination method was used to calculate the comprehensive quantitative value of each feature. The predicted fracture development zone map is spatially overlaid with the sedimentary microfacies distribution map, and combined with the comprehensive quantitative value, the favorable fracture development zone is classified into different levels.

[0006] Beneficial effects: This method, electronic equipment, and computer-readable storage medium for predicting fracture development in tight sandstone based on the coupling constraints of multiple geological parameters establish a scientific prediction model for fracture development in tight sandstone. This significantly improves the accuracy and reliability of predictions and effectively overcomes the technical bottlenecks of traditional prediction methods that are dominated by a single factor, rely mainly on qualitative analysis, and have insufficient prediction accuracy. Ultimately, it provides scientific and reliable technical support for the selection of exploration targets, drilling path planning, and fracturing scheme optimization in tight sandstone gas reservoirs, significantly improving the exploration success rate, reducing development risks, and promoting the efficient and economical development of complex tight sandstone gas reservoirs. Attached Figure Description

[0007] Figure 1 This is a flowchart illustrating the method for predicting fracture development in tight sandstone based on the coupling constraints of multiple geological parameters, as provided in an embodiment of the present invention. Figure 2 A structural block diagram of a tight sandstone fracture development prediction system based on multi-geological parameter coupling constraints provided in an embodiment of the present invention; Figure 3 This is a seismic profile and sequence stratigraphic map of Anyue area provided in an embodiment of the present invention; Figure 4 This is a sedimentary microfacies distribution map of the periphery of the largest lacustrine flooding surface in the Xusan 2 Member area of ​​Anyue region, provided as an embodiment of the present invention. Figure 5 A fracture likelihood diagram based on structural features of the periphery of the maximum lacustrine flooding surface in the Xusan 2 section of Anyue region, provided for an embodiment of the present invention. Figure 6Map showing the favorable area for fracture development in the Xusan 2 section of Anyue region, provided for embodiments of the present invention. Detailed Implementation

[0008] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0009] See Figure 1 As shown, it is a flowchart illustrating the method for predicting fracture development in tight sandstone based on the coupling constraints of multiple geological parameters provided in this embodiment of the invention.

[0010] In this embodiment, the method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints includes: Step S100: Obtain basic geological data of the target tight sandstone gas reservoir area, and standardize the basic geological data to form a standardized geological database; Step S201: Based on the standardized geological database, high-resolution sequence stratigraphy is used to identify multi-level sequence boundaries to delineate each sequence boundary; combined with fracture development parameter analysis, and based on the range of each sequence boundary, the dominant fracture development segment is identified, and the vertical distribution range and lateral spread characteristics of the dominant segment are clarified. Step S202: Based on the standardized geological database, combined with core observation and well logging curve response characteristics, and using the spatial constraints of the seismic fusion attribute map, draw the sedimentary microfacies distribution map of the dominant layer, and clarify the spatial distribution range and boundary characteristics of different sedimentary microfacies; Step S203: Based on the standardized geological database, extract the formation curvature parameters using a multi-scale volume curvature calculation method, and calculate the straight-line distance from each well location to the fault through fault interpretation and fine characterization; based on the formation curvature parameters and the straight-line distance, combined with tectonic stress field analysis, delineate the predicted fracture development zone along the dominant strata and generate a predicted fracture development zone map, and clarify the boundary range, fracture development intensity level, and spatial distribution characteristics of the predicted fracture development zone; Step S300: Statistically analyze the fracture development in imaging logging, and obtain quantitative values ​​for structural features, lithological features, and sequence stratigraphy based on the statistical results. Then, normalize the quantitative values ​​for each feature. Compare the normalized quantitative values ​​of each feature with the statistical results to determine the influence weights of structural features, lithological features, and sequence stratigraphy on fracture development. Based on the influence weights, calculate the comprehensive quantitative value of each feature using a weighted combination method. Step S400: Spatially overlay the predicted fracture development zone map with the sedimentary microfacies distribution map, and combine the comprehensive quantitative value to classify the favorable fracture development zone.

[0011] In some embodiments of this application, the basic geological data includes two-dimensional seismic data, three-dimensional seismic data, well core sample data, imaging logging data, conventional logging data, and geological survey reports.

[0012] In some embodiments of this application, the standardization process includes: Step S101: Perform denoising and amplitude correction preprocessing on the two-dimensional seismic data and the three-dimensional seismic data respectively; Step S102: Perform depth matching and outlier removal on the logging curves in the conventional logging data; Step S103: Perform lithological identification and fracture parameter statistics on the drill core sample data, and establish the correspondence between the core and logging response; wherein, the fracture parameters include the dip angle, strike, density and filling characteristics of the fractures.

[0013] In some embodiments of this application, step S201 for determining the sequence features includes: Step S2011: Based on the standardized geological database, high-resolution sequence stratigraphy is used, combined with well logging cycle characteristics, seismic reflection termination relationships, and core sedimentary sequence analysis, to identify multi-level sequence boundaries, thereby delineating each sequence boundary and clarifying its extent, genetic type, and distribution pattern. The delineated sequence boundaries include at least one of short-term cycle boundaries, medium-term cycle boundaries, and lacustrine flooding surfaces. The short-term and medium-term cycle boundaries are sedimentary cycle boundaries defined according to different time scales, corresponding to sedimentary cycles of different lengths. The lacustrine flooding surface is a key interface in sequence stratigraphy for terrestrial lacustrine basins, representing the isochronous interface corresponding to the highest point of the lake level and the furthest advance of the lake shore towards the land within a cycle. Step S2012: By statistically analyzing the fracture development parameters of each sequence boundary and cycle position, the constraint mechanism of sequence structure on fracture development is analyzed; based on the range of each sequence boundary, the dominant fracture development segment is identified, and the longitudinal distribution range and lateral spread characteristics of the dominant segment are clarified; wherein, the fracture development parameters include fracture density and effectiveness.

[0014] In some embodiments of this application, step S202 for determining lithological characteristics includes: Step S2021: Obtain drilling core sample data and conventional logging data from the standardized geological database; Step S2022: Based on the acquired data, identify the lithological type of the dominant strata through core observation, analyze the rock structure, grain size sorting and mineral composition of the dominant strata, and establish a lithological identification model based on the logging curve response characteristics to classify the lithological assemblage type of the dominant strata, and classify sedimentary microfacies according to the lithological assemblage type; Step S2023: Use seismic fusion attribute maps to establish spatial constraints and clarify the spatial distribution range and boundary characteristics of different sedimentary microfacies; Step S2024: Draw a sedimentary microfacies distribution map of the dominant stratigraphic interval to present the spatial distribution pattern of the lithological assemblage and sedimentary microfacies.

[0015] In some embodiments of this application, step S203 for defining the construction features includes: Step S2031: Obtain two-dimensional seismic data, three-dimensional seismic data, imaging logging data, and conventional logging data from the standardized geological database; Step S2032: Based on the acquired data, the formation curvature parameters are extracted using a multi-scale volume curvature calculation method, and a positive correlation quantitative relationship between the formation curvature parameters and the number of fractures is established through statistical analysis; wherein, the formation curvature parameters include the average formation curvature, the maximum positive curvature, and the minimum negative curvature, the number of fractures includes the total number of fractures and the number of fractures of each type, the number of fractures of each type includes the number of oblique fractures and the number of high-angle fractures, oblique fractures are fractures with dip angles between 15 degrees and 45 degrees, and high-angle fractures are fractures with dip angles greater than 45 degrees relative to the horizontal plane; Step S2033: Through fault interpretation and fine characterization, calculate the straight-line distance from each drilling location to the main fracture-controlling fault, and analyze the stress gradient distribution law around the fault to clarify the negative correlation between each straight-line distance and fracture development density; wherein, the main fracture-controlling fault is determined by fault interpretation and fracture development statistical analysis; Step S2034: Based on the tectonic stress field analysis, and combined with the stratum curvature parameters and the straight-line distance, construct a quantitative response model for tectonic and fracture development; Step S2035: Based on the quantized response model, a structure-guided fracture likelihood prediction algorithm is adopted, which combines structure-guided filtering with the C2 coherence algorithm, and the similarity results of seismic data are calculated through weighted smoothing operation to obtain the maximum likelihood attribute data volume; Step S2036: Take the dominant segment as the target layer for fracture likelihood prediction and mark the target layer; along the marked target layer, use the layer attribute extraction function of the seismic data interpretation platform to extract the maximum likelihood attribute value of all seismic samples at the depth of the target layer to form a planarized stratigraphic slice. Step S2037: Construct a fracture likelihood result map based on the stratigraphic slice, delineate the predicted fracture development zone along the dominant stratigraphic segment on the fracture likelihood result map and generate a predicted fracture development zone map, and clarify the boundary range, fracture development intensity level and spatial distribution characteristics of the predicted fracture development zone.

[0016] In some embodiments of this application, the construction-guided crack likelihood prediction algorithm combines construction-guided filtering with the C2 coherent algorithm, and the formula used is as follows: in, This indicates that in a given area, the side lengths are respectively and Within the three-dimensional calculation window, the amplitude of each seismic sample point is... The result represents the coherence at the center point within the three-dimensional calculation window, and s represents the array. The weighted coherence value.

[0017] In some embodiments of this application, step S300 includes: Step S301: Statistically analyze the fracture development in imaging logging, and obtain quantitative values ​​for structural features, lithological features, and sequence characteristics based on the statistical results. Normalize the quantitative values ​​for structural features, lithological features, and sequence characteristics respectively. The statistical results of fracture development in imaging logging are obtained by identifying and statistically analyzing fracture features in imaging logging data from the standardized geological database. Step S302: Compare the normalized quantified values ​​of structural features, lithological features, and sequence stratigraphy with the statistical results to determine the weights of the influence of structural features, lithological features, and sequence stratigraphy on fracture development. Step S303: Based on the influence weights, calculate the comprehensive quantification value of each feature using a weighted combination method. The calculation formula is: G = 0.6G1 + 0.4G2; Y = 0.7Y1 + 0.3Y2; and C = 0.8C1 + 0.2C2. Wherein, G is the comprehensive quantitative value of structural features, G1 is the quantitative value of curvature with a corresponding influence weight of 0.6, G2 is the quantitative value of straight-line distance with a corresponding influence weight of 0.4, Y is the comprehensive quantitative value of lithological features, Y1 is the quantitative value of lithological type with a corresponding influence weight of 0.7, Y2 is the quantitative value of lithological combination with a corresponding influence weight of 0.3, C is the comprehensive quantitative value of sequence characteristics, C1 is the quantitative value of sequence interface type with a corresponding influence weight of 0.8, and C2 is the degree of fit of dominant interval with a corresponding influence weight of 0.2; the dimensions of the comprehensive quantitative value of structural features G, the comprehensive quantitative value of lithological features Y, and the comprehensive quantitative value of sequence characteristics C are all 0-10.

[0018] In some embodiments of this application, the comprehensive quantitative value is used to reflect the differences in fracture development potential among different sedimentary microfacies and lithological combinations, as well as the intensity level of structurally predicted fracture development zones, further clarifying the main influencing factors of fracture development in the study area.

[0019] In some embodiments of this application, the favorable fracture development zone is classified according to the magnitude of the fracture development potential value A and the intensity value B of the structurally predicted fracture development zone. The favorable areas for crack development are classified into three categories: The first type of favorable area is the overlapping part of the sedimentary microfacies where A takes the maximum value and the region where B is located within a preset first range; The second type of favorable area is the overlapping part of the sedimentary microfacies B located within a preset second range and the region B located within a preset third range; wherein, the minimum value of the preset first range is greater than the maximum value of the preset third range; The third type of favorable zone refers to the areas where structurally predicted fracture development zones exist, excluding the first and second types of favorable zones.

[0020] See Figure 2 The diagram shown is a structural block diagram of a tight sandstone fracture development prediction system based on multi-geological parameter coupling constraints provided in an embodiment of the present invention. This embodiment of the tight sandstone fracture development prediction system shares the same concept as the aforementioned embodiment of the tight sandstone fracture development prediction method, and will not be described in detail here.

[0021] In this embodiment, the tight sandstone fracture development prediction system based on multi-geological parameter coupling constraints includes a database construction module 10, a sequence characteristic definition module 21, a lithological characteristic definition module 22, a structural characteristic definition module 23, a multi-parameter coupling module 30, and a hierarchical classification module 40. The database construction module 10 is used to acquire basic geological data of the target tight sandstone gas reservoir area and to standardize the basic geological data to form a standardized geological database. The sequence feature identification module 21 is used to identify multi-level sequence interfaces based on the standardized geological database using high-resolution sequence stratigraphy methods, so as to delineate each sequence interface; combined with fracture development parameter analysis, and based on the range of each sequence interface, to identify the dominant fracture development segment, and to clarify the vertical distribution range and lateral spread characteristics of the dominant segment. The lithological characteristics identification module 22 is used to draw a sedimentary microfacies distribution map of the dominant layer based on the standardized geological database, combined with core observation and well logging curve response characteristics, and using the spatial constraints of the seismic fusion attribute map, and to identify the spatial distribution range and boundary characteristics of different sedimentary microfacies. The structural feature definition module 23 is used to extract formation curvature parameters based on the standardized geological database using a multi-scale volume curvature calculation method, and to calculate the straight-line distance from each well location to the fault through fault interpretation and fine characterization; based on the formation curvature parameters and the straight-line distance, combined with tectonic stress field analysis, to delineate the predicted fracture development zone along the dominant stratum and generate a predicted fracture development zone map, and to define the boundary range, fracture development intensity level and spatial distribution characteristics of the predicted fracture development zone; The multi-parameter coupling module 30 is used to statistically analyze fracture development in imaging logging and obtain quantified values ​​of structural features, lithological features, and sequence features based on the statistical results. These quantified values ​​are then normalized. The normalized quantified values ​​of each feature are compared with the statistical results to determine the influence weights of structural features, lithological features, and sequence features on fracture development. Based on these influence weights, a weighted combination method is used to calculate the comprehensive quantified value of each feature. The classification module 40 is used to spatially overlay the predicted fracture development zone map with the sedimentary microfacies distribution map, and combine the comprehensive quantification value to classify the fracture development favorable area.

[0022] The present invention will be further described in detail below with reference to embodiments: Taking the Xujiahe Formation, Xusan 2 Member, a small-layered tight sandstone reservoir in the Anyue area of ​​central Sichuan Basin as the target tight sandstone gas reservoir region (hereinafter referred to as the target region), this invention is used to predict and classify areas with favorable fracture development. The specific steps are as follows: (1) Basic geological data collection and standardization: Basic geological data of Anyue area were collected, including 2D seismic data, 3D seismic data, core sample data from 32 wells, imaging logging data, conventional logging data, and geological survey reports. This basic geological data was standardized to form a standardized geological database. "Geological structure-sedimentary microfacies-high frequency sequence stratigraphy" was selected as the core controlling factor for the development of fractures in tight sandstone, based on the logical chain of the innate material basis, acquired dynamic source, and vertical boundary constraints of fracture formation. Sedimentary microfacies determine lithology, grain size, and mechanical properties, providing the necessary material conditions for fracture initiation; geological structure provides the direct stress dynamics for fracture formation; high frequency sequence stratigraphy interfaces, through abrupt changes in the sedimentary environment, form differences in mechanical properties, thereby constraining the vertical distribution of fractures. The three factors precisely match the geological background of the target area, which is characterized by gentle tectonic structure and multiple sedimentary superpositions, effectively breaking through the limitations of traditional predictions relying on a single factor.

[0023] (2) Sequence boundary identification and dominant segment determination: By analyzing the cycle characteristics of well logging curves and the seismic reflection termination relationship, short-term cycle boundaries, medium-term cycle boundaries, and the maximum flooding surface were identified. The sequence boundaries within short-term cycles and the transition points between rising and falling half-cycles were determined to be the dominant segments with fracture development. Among these, the area surrounding the maximum flooding surface is the dominant segment within the target area where fracture development is most significant and concentrated. (See also...) Figure 3 As shown, it is a seismic profile and stratigraphic division map of Anyue area provided in an embodiment of the present invention.

[0024] (3) Sedimentary microfacies identification and mapping: By studying the core sample data, imaging logging data, and conventional logging data from 32 wells in the target area, lithologies such as fine sandstone, siltstone, medium-coarse sandstone, and mudstone were identified. Five lithological combinations were further defined: interbedded coarse and fine sandstone, thick layers of fine sandstone interbedded with siltstone, thick layers of fine siltstone interbedded with thin and medium layers of mudstone, and interbedded thin and medium layers of fine siltstone and mudstone. Sedimentary environments such as the inner delta front, outer delta front, and pre-delta-semi-deep lacustrine were identified. Based on the logging curve response characteristics, a sedimentary microfacies identification model was established. Using the spatial constraints of the seismic fusion attribute map, a sedimentary microfacies distribution map of the periphery of the maximum flooding surface in the Xusan 2 section was drawn. (See reference...) Figure 4 As shown, it is a sedimentary microfacies distribution map of the periphery of the largest lacustrine flooding surface in the Xusan 2 section of Anyue area provided in an embodiment of the present invention.

[0025] (4) Structural parameter extraction and analysis: Using a multi-scale volumetric curvature calculation method, the average curvature, maximum positive curvature, and minimum negative curvature parameters of the formation in the Xusan 2 section were extracted. Statistical analysis revealed that as the average curvature, maximum positive curvature, and minimum negative curvature parameters changed from low to high, the total number of fractures gradually increased. The straight-line distance from each well location to the main east-west trending fault was calculated. Based on the relationship between the fracture development direction of a single well and the distance to fractures in the same trend, it was determined that each straight-line distance was negatively correlated with the fracture development density. Quantitative fracture likelihood results based on structural characteristics were generated along the periphery of the maximum lacustrine flooding surface in the Xusan 2 section, and the predicted fracture development zone based on structural characteristics was delineated. (See reference...) Figure 5 As shown, it is a fracture likelihood result diagram based on tectonic features of the periphery of the largest lacustrine flooding surface in the Xusan 2 section of Anyue area provided in an embodiment of the present invention.

[0026] (5) Establishment of multi-parameter coupling results: Select the well section of the target area that has been completed by imaging logging, extract the fracture development of specific drilling, statistically analyze the fracture development of imaging logging, and obtain the influence weight and comprehensive quantitative value of structural features, lithological features and sequence features. Among them, the comprehensive quantitative value of structural features G = 0.6G1 (curvature quantitative value, 0-10) + 0.4G2 (straight distance quantitative value, 0-10), the comprehensive quantitative value of lithological features Y = 0.7Y1 (lithological type quantitative value, 0-10, assigned according to fine sandstone, siltstone, etc.) + 0.3Y2 (lithological combination quantitative value, 0-10, assigned according to lithological interbedded / interlayer type), and the comprehensive quantitative value of sequence characteristics C = 0.8C1 (sequence interface type quantitative value, 0-10 points, assigned according to flooding surface, etc.) + 0.2C2 (dominant interval fit, 0-10 points, assigned according to whether it is a fracture dominant interval). The dimensions of G, Y, and C are unified to 0-10, which is used to characterize the influence on fracture development. Based on the imaging logging data, regression analysis was performed to determine that structural features are the main controlling factor for fracture development in the Xusan 2 section of the target area.

[0027] (6) Delineation of favorable fracture development areas: The predicted fracture development zone map based on tectonic features is spatially overlaid with the sedimentary microfacies distribution map. Combined with the comprehensive quantitative values ​​reflecting the differences in fracture development potential among different sedimentary microfacies and lithological assemblages, and the intensity level of the predicted fracture development zone, favorable fracture development areas are delineated. Specifically, areas where the inner delta front overlaps with the predicted fracture development zone based on tectonic features are classified as Class I favorable areas; areas where the outer delta front overlaps with the predicted fracture development zone based on tectonic features are classified as Class II favorable areas; and the remaining predicted fracture development zones based on tectonic features are classified as Class III favorable areas. (See also...) Figure 6 As shown, it is a map of the favorable area for fracture development in the Xusan 2 section of Anyue area provided in the embodiment of the present invention.

[0028] The advantages of this invention are: (1) This invention integrates multiple geological parameters of structure, lithology and sequence to establish a joint control model of "geological structure-sedimentary microfacies-high frequency sequence", which overcomes the limitations of traditional prediction methods that rely on single factors and significantly improves the prediction accuracy of areas favorable for fracture development. (2) By quantitatively analyzing each feature, the prediction and division of favorable areas for crack development are realized, realizing the transformation from qualitative description to quantitative prediction, and the prediction results are more scientific and reliable. (3) This invention is applicable to the prediction of fracture development in various tight sandstone gas reservoirs. It has significant advantages, especially for tight sandstone reservoirs with multiple superimposed structures and complex lithology. It can effectively reduce exploration risks and improve resource utilization efficiency, and has broad application prospects.

[0029] The following are embodiments of the electronic device provided by the present invention. The embodiments of the electronic device and the embodiments of the above-described method for predicting the development of fractures in tight sandstone belong to the same concept. Details not fully described in the embodiments of the electronic device can be found in the embodiments of the above-described method for predicting the development of fractures in tight sandstone.

[0030] In this embodiment, an electronic device includes: Memory, used to store program instructions; and A processor is used to execute the program instructions to implement the steps of the tight sandstone fracture development prediction method based on multi-geological parameter coupling constraints as described above.

[0031] The following are embodiments of the computer-readable storage medium provided by the present invention. The embodiments of the computer-readable storage medium belong to the same concept as the embodiments of the tight sandstone fracture development prediction method and electronic device described above. Details not described in detail in the embodiments of the computer-readable storage medium can be found in the embodiments of the tight sandstone fracture development prediction method and electronic device described above.

[0032] In this embodiment, a computer-readable storage medium stores computer-executable instructions thereon, which, when executed by a processor, implement the steps of the tight sandstone fracture development prediction method based on multi-geological parameter coupling constraints as described above.

[0033] The electronic device and computer-readable storage medium of the present invention are both capable of implementing the steps of the above-described method for predicting the development of fractures in tight sandstone. Therefore, they at least have all the beneficial effects brought about by the technical solutions of the above-described embodiments of the method for predicting the development of fractures in tight sandstone, which will not be repeated here.

Claims

1. A method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints, characterized in that, include: Acquire basic geological data of the target tight sandstone gas reservoir area and standardize the basic geological data to form a standardized geological database; Based on the standardized geological database, high-resolution sequence stratigraphy was used to identify multi-level sequence boundaries to delineate each sequence boundary. Combined with fracture development parameter analysis and based on the range of each sequence boundary, the dominant fracture development segments were identified, and the vertical distribution range and lateral spread characteristics of the dominant segments were clarified. Based on the standardized geological database, combined with core observation and well logging response characteristics, and utilizing the spatial constraints of the seismic fusion attribute map, a sedimentary microfacies distribution map of the dominant stratigraphic interval was drawn, and the spatial distribution range and boundary characteristics of different sedimentary microfacies were clarified. Based on the standardized geological database, the formation curvature parameters are extracted using a multi-scale volume curvature calculation method, and the straight-line distance from each well location to the fault is calculated through fault interpretation and fine characterization. Based on the formation curvature parameters and the straight-line distance, combined with tectonic stress field analysis, the predicted fracture development zone is delineated along the dominant strata and a predicted fracture development zone map is generated, and the boundary range, fracture development intensity level, and spatial distribution characteristics of the predicted fracture development zone are clarified. The development of fractures in imaging logging was statistically analyzed, and quantitative values ​​of structural features, lithological features, and sequence characteristics were obtained based on the statistical results. These quantitative values ​​were then normalized. The normalized quantitative values ​​of each feature were compared with the statistical results to determine the influence weights of structural features, lithological features, and sequence characteristics on fracture development. Based on these influence weights, a weighted combination method was used to calculate the comprehensive quantitative value of each feature. The predicted fracture development zone map is spatially overlaid with the sedimentary microfacies distribution map, and combined with the comprehensive quantitative value, the favorable fracture development zone is classified into different levels.

2. The method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints according to claim 1, characterized in that, The basic geological data includes two-dimensional seismic data, three-dimensional seismic data, well core sample data, imaging logging data, conventional logging data, and geological survey reports; The standardization process includes: The two-dimensional seismic data and the three-dimensional seismic data are respectively subjected to denoising and amplitude correction preprocessing; Depth matching and outlier removal are performed on the logging curves in the conventional logging data; The drilling core sample data are subjected to lithological identification and fracture parameter statistics, and a correspondence between the core and logging response is established; wherein, the fracture parameters include the dip angle, strike, density and filling characteristics of the fractures.

3. The method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints according to claim 2, characterized in that, Based on the standardized geological database, high-resolution sequence stratigraphy is used to identify multi-level sequence boundaries to delineate each sequence boundary. Combined with fracture development parameter analysis and based on the range of each sequence boundary, the dominant fracture-developing segments are identified, and the vertical distribution and lateral extension characteristics of these dominant segments are defined, including: Based on the standardized geological database, high-resolution sequence stratigraphy was employed, combined with well logging cycle characteristics, seismic reflection termination relationships, and core sedimentary sequence analysis, to identify multi-level sequence boundaries. This process aimed to delineate each sequence boundary and clarify its extent, genetic type, and distribution pattern. The identified sequence boundaries include at least one of short-term cycle boundaries, medium-term cycle boundaries, and lacustrine flooding surfaces. The short-term and medium-term cycle boundaries are sedimentary cycle boundaries defined according to different time scales, corresponding to sedimentary cycles of varying lengths. The lacustrine flooding surface is a key interface in sequence stratigraphy for terrestrial lacustrine basins, representing the isochronous interface corresponding to the highest point of the lake level and the furthest advance of the lake shore towards the land within a cycle. By statistically analyzing the fracture development parameters at each sequence boundary and cycle position, the constraint mechanism of sequence structure on fracture development is analyzed; based on the range of each sequence boundary, the dominant fracture development segments are identified, and the longitudinal distribution range and lateral spread characteristics of the dominant segments are clarified; wherein, the fracture development parameters include fracture density and effectiveness.

4. The method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints according to claim 3, characterized in that, Based on the standardized geological database, combined with core observations and well logging response characteristics, and utilizing the spatial constraints of seismic fusion attribute maps, a sedimentary microfacies distribution map of the dominant stratigraphic interval is drawn, and the spatial distribution range and boundary characteristics of different sedimentary microfacies are clarified, including: Obtain drilling core sample data and conventional logging data from the standardized geological database; Based on the acquired data, the lithological type of the dominant strata is identified through core observation, the rock structure, grain size sorting and mineral composition of the dominant strata are analyzed, and a lithological identification model is established based on the logging curve response characteristics to classify the lithological assemblage type of the dominant strata, and sedimentary microfacies are classified according to the lithological assemblage type. Spatial constraints were applied using seismic fusion attribute maps to clarify the spatial distribution range and boundary characteristics of different sedimentary microfacies; Draw a sedimentary microfacies distribution map of the dominant stratigraphic intervals to present the spatial distribution pattern of the lithological assemblage and sedimentary microfacies.

5. The method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints according to claim 4, characterized in that, Based on the standardized geological database, a multi-scale volume curvature calculation method is used to extract formation curvature parameters, and the straight-line distance from each well location to the fault is calculated through fault interpretation and fine characterization. Based on the formation curvature parameters and the straight-line distance, combined with tectonic stress field analysis, predicted fracture development zones are delineated along the dominant stratigraphic intervals, and a predicted fracture development zone map is generated. The boundary range, fracture development intensity level, and spatial distribution characteristics of the predicted fracture development zones are defined, including: Acquire two-dimensional seismic data, three-dimensional seismic data, imaging logging data, and conventional logging data from the standardized geological database; Based on the acquired data, a multi-scale volume curvature calculation method was used to extract formation curvature parameters, and a positive correlation between formation curvature parameters and the number of fractures was established through statistical analysis. The formation curvature parameters include the average formation curvature, the maximum positive curvature, and the minimum negative curvature. The number of fractures includes the total number of fractures and the number of fractures of each type. The number of fractures of each type includes the number of oblique fractures and the number of high-angle fractures. Oblique fractures are fractures with dip angles between 15 degrees and 45 degrees, and high-angle fractures are fractures with dip angles greater than 45 degrees relative to the horizontal plane. By interpreting and finely characterizing faults, the straight-line distances from each drilling location to the main fracture-controlling faults are calculated, and the stress gradient distribution around the faults is analyzed to clarify the negative correlation between each straight-line distance and fracture development density; wherein, the main fracture-controlling faults are determined by fault interpretation and statistical analysis of fracture development. Based on the tectonic stress field analysis, combined with the stratum curvature parameters and the straight-line distance, a quantitative response model for tectonic and fracture development is constructed. Based on the quantized response model, a structure-guided fracture likelihood prediction algorithm is adopted, which combines structure-guided filtering with the C2 coherence algorithm, and calculates the similarity results of seismic data through weighted smoothing operation to obtain the maximum likelihood attribute data volume. The dominant stratigraphic segment is used as the target stratigraphic segment for fracture likelihood prediction, and the target stratigraphic segment is marked. Along the marked target stratigraphic segment, the maximum likelihood attribute value of all seismic sample points at the target stratigraphic depth is extracted using the stratigraphic attribute extraction function of the seismic data interpretation platform to form a planarized stratigraphic slice. Based on the stratigraphic slices, a fracture likelihood map is constructed. On the fracture likelihood map, a predicted fracture development zone is delineated along the dominant stratigraphic segment and a predicted fracture development zone map is generated. The boundary range, fracture development intensity level, and spatial distribution characteristics of the predicted fracture development zone are then defined.

6. The method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints according to claim 5, characterized in that, The structure-guided crack likelihood prediction algorithm combines structure-guided filtering with the C2 coherence algorithm, and the formula used is as follows: in, This indicates that in a given area, the side lengths are respectively and Within the three-dimensional calculation window, the amplitude of each seismic sample point is... The result represents the coherence at the center point within the three-dimensional calculation window, where s represents the array. The weighted coherence value.

7. The method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints according to claim 5, characterized in that, The statistical imaging logging data shows fracture development, and based on the statistical results, quantitative values ​​of structural features, lithological features, and sequence characteristics are obtained. These quantitative values ​​are then normalized. The normalized quantitative values ​​of each feature are compared with the statistical results to determine the influence weights of structural features, lithological features, and sequence characteristics on fracture development. Based on the aforementioned influence weights, a weighted combination method is used to calculate the comprehensive quantification value of each feature, including: The development of fractures in imaging logging was statistically analyzed, and quantitative values ​​of structural features, lithological features, and sequence characteristics were obtained based on the statistical results. These values ​​were then normalized. The statistical results of fracture development in imaging logging were obtained by identifying and statistically analyzing fracture features in imaging logging data from the standardized geological database. The normalized quantified values ​​of structural features, lithological features, and sequence features were compared with the statistical results to determine the weights of the influence of structural features, lithological features, and sequence features on fracture development. Based on the aforementioned influence weights, a weighted combination method is used to calculate the comprehensive quantitative value of each feature. The calculation formulas are: G = 0.6G1 + 0.4G2; Y = 0.7Y1 + 0.3Y2; and C = 0.8C1 + 0.2C2. Wherein, G is the comprehensive quantitative value of structural features, G1 is the quantitative value of curvature with a corresponding influence weight of 0.6, G2 is the quantitative value of straight-line distance with a corresponding influence weight of 0.4, Y is the comprehensive quantitative value of lithological features, Y1 is the quantitative value of lithological type with a corresponding influence weight of 0.7, Y2 is the quantitative value of lithological combination with a corresponding influence weight of 0.3, C is the comprehensive quantitative value of sequence characteristics, C1 is the quantitative value of sequence interface type with a corresponding influence weight of 0.8, and C2 is the degree of fit of dominant interval with a corresponding influence weight of 0.2; the dimensions of the comprehensive quantitative value of structural features G, the comprehensive quantitative value of lithological features Y, and the comprehensive quantitative value of sequence characteristics C are all 0-10.

8. The method for predicting fracture development in tight sandstone based on multi-geological parameter coupling constraints according to claim 7, characterized in that, The favorable areas for fracture development are classified according to the magnitude of the fracture development potential value A and the intensity value B of the structurally predicted fracture development zone. The favorable areas for crack development are classified into three categories: The first type of favorable area is the overlapping part of the sedimentary microfacies where A takes the maximum value and the region where B is located within a preset first range; The second type of favorable area is the overlapping part of the sedimentary microfacies B located within a preset second range and the region B located within a preset third range; wherein, the minimum value of the preset first range is greater than the maximum value of the preset third range; The third type of favorable zone refers to the areas where structurally predicted fracture development zones exist, excluding the first and second types of favorable zones.

9. An electronic device, characterized in that, include: Memory, used to store program instructions; as well as A processor for executing the program instructions to implement the steps of the method for predicting fracture development in tight sandstone based on the coupling constraints of multiple geological parameters as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the steps of the method for predicting fracture development in tight sandstone based on the coupling constraints of multiple geological parameters as described in any one of claims 1 to 8.

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