Seismic attribute curve method and application method, device, equipment and medium

Abstract

This invention relates to the field of geophysics, proposing a method, application method, apparatus, equipment, and medium for seismic attribute curve mapping. The method includes: constructing a wellbore geological model framework based on a directional well trajectory; acquiring seismic attribute volumes and converting them into depth-domain seismic attribute volumes; sampling and matching each attribute value of the depth-domain seismic attribute volumes into the wellbore geological model framework to generate a wellbore attribute model; and based on the wellbore attribute model, acquiring attribute vector values ​​and corresponding depth values ​​within an arbitrary distance range around the directional well trajectory, and generating seismic attribute curves based on both. This solution achieves the goal of seismic attribute curve mapping, enabling multi-dimensional transformation from seismic graphics to seismic curves, from horizontal planes to vertical lines, and from vertical lines to data points. It realizes a multi-dimensional, interactive wellbore geological description method that integrates volumes, lines, planes, and points, meeting the comprehensive requirements of directional wellbore geological description for both breadth and accuracy.

Description

Technical Field

[0001] This invention relates to the field of geophysics, and more particularly to a method, application method, apparatus, equipment, and medium for earthquake attribute curve mapping. Background Technology

[0002] In oil and gas exploration, a detailed understanding of the geological conditions surrounding the well, including strata, structures, and rock characteristics, helps determine whether there are underground strata with the potential to store oil and gas. For example, describing the lithology of the surrounding strata can identify the development of porous rocks such as sandstone, as sandstone typically has high porosity and is a good carrier for oil and gas storage. Analyzing structural features can reveal geological structures such as anticlines and faults. The top of anticlines is often a favorable location for oil and gas accumulation, while faults may serve as channels for oil and gas migration or act as seals, helping explorers pinpoint potential oil and gas enrichment areas and improve exploration success rates.

[0003] A directional well is a well drilled according to a pre-defined wellbore trajectory and direction. During drilling, the wellbore trajectory is monitored and adjusted in real time using techniques such as measurement-while-drilling (MWD) and logging-while-drilling (LOD) based on various factors such as formation conditions and target points. When describing the geological conditions around a directional well, three-dimensional images constructed from seismic attribute data are typically used. These images, to a certain extent, demonstrate the general state of the geological strata in the area where the directional well is located. For example, they can visually present the overall spatial distribution of strata and key information about large geological structures such as faults and folds.

[0004] However, when describing the geological conditions around directional wells using 3D images constructed based on seismic attribute data, there is a problem that it is difficult to convert them into curve-based information distributed along the wellbore that can be compared with well logging data. Generally, it can only present a rough geological content over a wide range, making it difficult to accurately analyze the specific conditions of the strata around the well. In practical applications, it is difficult to meet the comprehensive requirements of breadth and accuracy for geological description around directional wells. Summary of the Invention

[0005] To address the problem that existing 3D image descriptions based on seismic attribute data, used to depict the geological conditions around directional wells, are difficult to convert into curvilinear information distributed along the wellbore and comparable to well logging data, they generally only present a broad range of coarse geological content and are difficult to accurately analyze the specific conditions of the surrounding strata. In practical applications, they are difficult to meet the comprehensive requirements of breadth and accuracy for geological description around directional wells. This invention proposes a method, application method, device, equipment, and medium for seismic attribute curvilinearization.

[0006] To achieve the above objectives, one aspect of the present invention provides a method for seismic attribute curve mapping, comprising: A well perimeter geological model framework was constructed based on directional well trajectories. Obtain the seismic attribute volume and convert it into a depth domain seismic attribute volume; The attribute values ​​of each attribute in the seismic attribute volume of the depth domain are sampled and matched into the well perimeter geological model framework to generate a well perimeter attribute model; Based on the well perimeter attribute model, the attribute vector values ​​and corresponding depth values ​​within any distance range around the directional well trajectory are obtained, and a seismic attribute curve is generated based on the two.

[0007] In some embodiments, the step of sampling and matching the attribute values ​​of the seismic attribute volume in the depth domain to the well perimeter geological model framework includes: The attribute values ​​of the seismic attribute volume in the depth domain are meshed based on the rectangular well perimeter geological model framework to obtain the meshed attribute values. The gridded attribute values ​​are sampled based on preset sampling rules and matched to the corresponding grid cells of the well perimeter geological model framework.

[0008] In some embodiments, the step of matching it to the corresponding grid cell of the well perimeter geological model framework includes: Based on the weights corresponding to each sampling point and each grid cell in the well perimeter geological model framework, the gridded attribute values ​​of each sampling point are matched to the corresponding grid cells.

[0009] In some embodiments, the process of determining the weights corresponding to each sampling point and each grid cell in the well perimeter geological model framework includes: Based on the distance relationship between each sampling point and each grid cell in the well perimeter geological model framework, the weights corresponding to each sampling point and each grid cell are determined.

[0010] In some embodiments, the step of constructing a well perimeter geological model framework based on directional well trajectories includes: Obtain the directional well trajectory of the study area and determine the section to be observed; The starting observation depth value, the ending observation depth value, and the thickness value of the target geological layer of the observation segment are obtained, and the first parameter of the well perimeter geological model grid in the vertical direction is determined based on the three. The second parameter of the well perimeter geological model frame in the horizontal direction is determined based on the well perimeter dimensions corresponding to the section to be observed. The matrix well perimeter geological model grid is constructed based on the first parameter and the second parameter, and the grid cells of the rectangular well perimeter geological model grid are determined based on the well perimeter dimensions and the thickness value of the target geological layer.

[0011] In some embodiments, the step of converting it into a depth-domain seismic attribute volume includes: By using a pre-established velocity field, the seismic attribute volume in the time domain is transformed into the seismic attribute volume in the depth domain.

[0012] In some embodiments, the step of obtaining attribute vector values ​​and corresponding depth values ​​within an arbitrary distance range around the directional well trajectory based on the well perimeter attribute model includes: Based on the well perimeter attribute model, the attribute values ​​and their corresponding depth values ​​within an arbitrary distance range around the directional well trajectory are extracted; Each attribute value within an arbitrary distance range around the directional well trajectory is vectorized to obtain the corresponding attribute vector value.

[0013] In some embodiments, the step of generating seismic attribute curves based on the two includes: Obtain the logging data table corresponding to the directional well; The attribute vector values ​​and their corresponding depth values ​​within any distance range around the directional well trajectory are added to the well logging data table, and then imported into a preset well logging data analysis software to generate seismic attribute curves.

[0014] A second aspect of this invention also provides a method for applying seismic attribute curves to fracture morphology prediction in hydraulic fracturing technology, comprising: Obtain the seismic sensitive attribute volume related to fracturing as the seismic attribute volume, and import it into the seismic attribute curve conversion method based on any of the foregoing embodiments, and use the generated seismic attribute curve as the sensitive attribute curve; The sensitivity attribute curves are graded according to different developmental morphologies of the cracks to generate sensitivity attribute grading curves, which are used to predict crack morphology.

[0015] In some embodiments, the process of determining the different developmental morphologies of the crack includes: If the crack is within the range of 0 < value on the sensitivity attribute curve < first threshold, the crack is determined to be in an underdeveloped state; If the crack is within the range of the first threshold ≤ value on the sensitivity attribute curve < second threshold, the crack is determined to be in a relatively developed state, wherein the second threshold is greater than the first threshold; If the crack is within the range of ≥ the second threshold, the crack is determined to be in a developmental stage.

[0016] In some embodiments, after the step of generating the sensitivity attribute curve, the method further includes: Obtain the logging curves corresponding to the directional well; The sensitive attribute curve and the logging curve are aligned by depth value based on the same depth value calibration rule, and then the fracture distribution is analyzed based on the two after depth value alignment.

[0017] A third aspect of the present invention also provides an apparatus for seismic attribute curve mapping, comprising: The first module is used to construct a well perimeter geological model framework based on directional well trajectories; The second module is used to obtain the seismic attribute volume and convert it into a depth domain seismic attribute volume; The third module is used to sample and match the attribute values ​​of the seismic attribute body in the depth domain to the well perimeter geological model framework to generate a well perimeter attribute model. The fourth module is used to obtain the attribute vector values ​​and corresponding depth values ​​within any distance range around the directional well trajectory based on the well perimeter attribute model, and to generate seismic attribute curves based on the two.

[0018] A fourth aspect of the present invention also provides an electronic device, including at least one processor; and a memory storing computer instructions executable on the processor, the instructions, when executed by the processor, implementing the steps of the seismic attribute curve shaping method described in any one of the preceding embodiments.

[0019] In another aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the method for seismic attribute curve mapping as described in any of the preceding embodiments.

[0020] The present invention has at least the following beneficial effects: The present invention proposes a method for seismic attribute curve mapping. Based on the directional well trajectory, a well perimeter geological model framework is constructed. Seismic attribute volumes are obtained and converted into depth-domain seismic attribute volumes. The attribute values ​​of each depth-domain seismic attribute volume are sampled and matched into the well perimeter geological model framework to generate a well perimeter attribute model. Then, based on the well perimeter attribute model, attribute vector values ​​and corresponding depth values ​​within any distance range around the directional well trajectory are obtained. Based on these two, a seismic attribute curve is generated, achieving the purpose of seismic attribute curve mapping. This transforms seismic attributes from three-dimensional solids to one-dimensional curve vectorization, realizing a multi-dimensional transformation from seismic graphics to seismic curves, from horizontal planes to vertical lines, and from vertical lines to data points. Through this series of transformations, a multi-dimensional integrated and interactive well perimeter geological description method of volume, line, surface, and point is realized to meet the comprehensive requirements of directional well perimeter geological description for breadth and accuracy.

[0021] This invention also proposes a device, equipment, and medium for earthquake attribute curve mapping, which can achieve the same technical effects as described above, and will not be elaborated further.

[0022] This invention also proposes a method for applying seismic attribute curves. It obtains seismically sensitive attribute volumes related to fracturing, and generates sensitive attribute curves based on the aforementioned seismic attribute curveization method. This transforms the lateral information of seismic data into curves that are distributed along the wellbore and comparable in depth to well logging data. Subsequently, the seismic attribute information and well logging information are integrated into the same geological analysis platform to comprehensively analyze the wellbore geological conditions. This includes both the lateral information of seismic data and the vertical information of well logging data, meeting the comprehensive requirements of wellbore geological description for both breadth and accuracy. This improves the prediction accuracy of fracture distribution and strongly supports the design of horizontal well fracturing construction schemes. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0024] Figure 1 The flowchart shown is a method for seismic attribute curve mapping provided in the first embodiment of the present invention; Figure 2 shows a schematic diagram of the construction of a well perimeter geological model framework provided in another embodiment of the present invention, wherein Figure 2(a) is a coal seam construction model, Figure 2(b) is a coal seam cross-section, and Figure 2(c) is a three-dimensional view of the well perimeter geological model framework; Figure 3 shows a perspective view of the time-depth conversion of the seismic attribute volume provided in another embodiment of the present invention, wherein Figure 3(a) shows the seismic attribute volume in the time domain and Figure 3(b) shows the seismic attribute volume in the depth domain. Figure 4 shows a schematic diagram of the construction of a well perimeter attribute model provided in another embodiment of the present invention, wherein Figure 4(a) is a schematic diagram of the meshing process of the seismic anisotropic attribute volume, and Figure 4(b) is a schematic diagram of the well perimeter attribute model; Figure 5 shows a schematic diagram of the attribute vector values ​​extracted from the well perimeter attribute model within an arbitrary distance range according to another embodiment of the present invention and their addition to the well logging data table. In Figure 5(a), the anisotropic seismic attributes and their corresponding seismic attribute curves are shown within a 50-meter radius around the well. Figure 5(b) shows the anisotropic seismic attributes and their corresponding seismic attribute curves within a 100-meter radius west of the well. Figure 5(c) shows the anisotropic seismic attributes and their corresponding seismic attribute curves within a 100-meter radius west of the well. Figure 5(d) is a schematic diagram of the well logging data table. Figure 6 The diagram shown is a schematic diagram of graded seismic attribute curves and a seismic attribute profile provided in another embodiment of the present invention; Figure 7 The diagram shown is a combined display profile of seismic multi-attribute curves and well logging GR curves provided according to another embodiment of the present invention, wherein... Figure 7 (a) is a planar schematic diagram of multiple seismic attribute curves. Figure 7 (b) is a cross-sectional view showing multiple seismic attribute curves and well logging curves. Figure 7 (c) is a three-dimensional view showing multiple seismic attribute curves and well logging curves together; Figure 8 A schematic diagram of a seismic attribute curve mapping device provided in an embodiment of the present invention is shown; Figure 9 The diagram shown is a schematic representation of an electronic device according to an embodiment of the present invention; Figure 10 The diagram shown is a schematic representation of a computer-readable storage medium provided according to an embodiment of the present invention. Detailed Implementation

[0025] The following describes embodiments of the present invention. However, it should be understood that the disclosed embodiments are merely examples, and other embodiments may take various alternative forms.

[0026] Furthermore, it should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or apparatus that comprises a list of elements may include not only those elements but also elements not expressly listed or inherent to such process, method, article, or apparatus.

[0027] One or more embodiments of this application will now be described with reference to the accompanying drawings.

[0028] Based on the above objectives, the first aspect of the present invention provides an embodiment of a method for seismic attribute curve mapping. Figure 1 The flowchart shown is a method for seismic attribute curve mapping according to an embodiment of the present invention, as illustrated below. Figure 1 As shown, a method for seismic attribute curve mapping includes: Step 101: Construct a well perimeter geological model framework based on the directional well trajectory; For details, please refer to Figure 1 -2. Determine the target point A of the directional well in the target geological layer, and determine the trajectory of the directional well based on the target point A. Directional wells include horizontal wells and inclined wells, etc. Taking a horizontal well as an example, we assume that the target geological layer in a certain study area is a coal seam, and the structural model of the target geological layer in this study area is shown in Figure 2(a). Based on the geological conditions of the coal seam, determine the target point A of the directional well in the target coal seam, and then establish a wellbore geological model framework from target point A to the bottom of the well.

[0029] Specifically, a wellbore geological model framework is a three-dimensional model framework constructed by digitally and structurally representing the geological bodies (including strata, rocks, structures, etc.) within a certain range around the wellbore. It achieves detailed description and analysis of geological information by dividing the wellbore geological space into a series of regular or irregular grid units. There are various structures for wellbore geological model frameworks, such as matrix wellbore geological model frameworks and cylindrical wellbore geological model frameworks, etc., and the appropriate structure can be flexibly selected based on the specific circumstances in practical applications. In this embodiment, a rectangular wellbore geological model framework constructed based on a directional well trajectory, with rectangular grid units as the dividing elements, will be used as an example for illustration.

[0030] Specifically, Figure 2(b) shows a cross-sectional view of the target geological layer. The cross-sectional view of the coal seam shows a thickness of 20 meters, i.e., a vertical thickness of 20 meters. Simultaneously, the observation section for the horizontal well is determined, and its corresponding observation depth and end observation depth are obtained, for example, 2536 meters and 3900 meters respectively. Based on the initial observation depth of 2536 meters, the end observation depth of 3900 meters, and the coal seam thickness of 20 meters, a well perimeter geological model framework is constructed. Identical grid units are divided within the well perimeter geological model framework, as shown in Figure 2(c). The lateral width of the grid units is determined based on the well perimeter dimensions, and the longitudinal width is determined based on the thickness of the target geological layer. For example, based on a well perimeter radius of 100 meters, the lateral width of the grid units is determined to be 200 meters, and based on the coal seam thickness of 20 meters, the longitudinal width of the grid units is determined to be 20 meters. A rectangular well perimeter geological model framework is constructed using the overall parameters of the well perimeter geological model framework and the parameters of the internal grid units.

[0031] Step 102: Obtain the seismic attribute volume and convert it into a depth domain seismic attribute volume; Specifically, a seismic attribute body is a collection of multiple seismic attributes. Seismic attributes are parameters or characteristic quantities extracted from seismic data (data obtained through seismic exploration regarding underground geological structures, stratigraphic characteristics, etc.) that reflect certain features of a geological body. Seismic attribute bodies can interpret geological conditions from different perspectives, such as the lithology, porosity, and permeability of strata, as well as the location and morphology of geological structures. For example, in fracturing technology, seismic attributes related to fracturing include amplitude attributes (instantaneous amplitude, average amplitude, etc.), frequency attributes (dominant frequency, bandwidth, etc.), and phase attributes (instantaneous phase, relative phase, etc.).

[0032] Specifically, seismic property bodies include anisotropic property bodies and isotropic property bodies. Due to differences in the internal structure of certain rocks and the influence of geological tectonic movements, the propagation speed, attenuation, and other properties of seismic waves vary depending on the direction of propagation when they propagate in these geological bodies. The collection of these properties is called anisotropic property body, and vice versa.

[0033] Specifically, seismic data is initially acquired in the time domain, possessing high lateral resolution and effectively reflecting differences in geological conditions. However, in practical geological analysis and modeling, depth information is preferred to clarify the true spatial location of underground geological structures and strata. Figure 3(a) shows the time-domain seismic attribute volume obtained from the coal seams in the aforementioned study area. It should be noted that this seismic attribute volume is an anisotropic volume (denoted as GXYX). ​​To convert the time-domain seismic attribute volume to the depth-domain seismic attribute volume, the known velocity field is used, based on the correspondence between time and depth, to transform the time-domain seismic data into depth-domain seismic data. The two-way travel time data corresponding to each data point or seismic trace in the seismic attribute volume is combined with the velocity value at the corresponding position in the average velocity formula, and depth is calculated sequentially to obtain the depth-domain seismic attribute volume. Figure 3(b) shows the seismic attribute volume converted from the time-domain to the depth-domain seismic attribute volume. It can be seen that the two have similar structural morphologies, realizing the conversion of the seismic attribute volume from lateral to vertical.

[0034] Specifically, the seismic attribute volume in the depth domain can be seamlessly integrated into the geological model construction process. Compared with the seismic attribute volume in the time domain, the seismic attributes in the depth domain reduce the interpretation uncertainty caused by the time domain conversion relationship, have higher interpretation efficiency, and make the seismic data interpretation results more reliable. They can directly provide accurate depth coordinates without the need for a complex time-depth conversion process, which helps to generate seismic attribute curves in the future.

[0035] Step 103: Sample and match the attribute values ​​of the seismic attribute volume in the depth domain to the well perimeter geological model framework to generate the well perimeter attribute model; Specifically, sampling methods include nearest neighbor sampling (assigning the value of the data point of the seismic attribute body closest to the center of the grid cell to the grid cell), linear interpolation sampling (calculating the value assigned to the grid cell by linear interpolation based on the values ​​of multiple seismic attribute data points around the grid cell, which is suitable for situations where the attribute values ​​change relatively smoothly), and Kriging interpolation sampling (considering the spatial correlation and variability of attribute values, performing interpolation sampling based on statistical principles, which is often used in scenarios where geological attributes have a certain degree of spatial randomness), etc.

[0036] Specifically, in conjunction with the foregoing, the seismic attribute volume in the depth domain is an anisotropic volume in the depth domain. Based on the inverse distance weighted statistical method, and according to the rectangular well-circumferential geological model framework constructed above and the rectangular grid cells divided within it, the seismic anisotropic attribute volume in the depth domain is meshed, as shown in Figure 4(a). Then, a suitable sampling method is selected, and for each grid cell in the rectangular well-circumferential geological model framework, the corresponding attribute value is obtained from the seismic attribute volume in the depth domain and assigned to the corresponding grid cell, so that the corresponding attribute values ​​are sampled and matched to the corresponding grid cells in the constructed well-circumferential geological model framework, resulting in the well-circumferential geological model, as shown in Figure 4(b). For example, for the seismic attribute of mean amplitude, the position corresponding to the grid cell of the rectangular well-circumferential geological model framework is found in the seismic attribute volume in the depth domain, the appropriate amplitude value is calculated using the selected sampling method, and then assigned to the grid cell. The above operation is performed sequentially for all required seismic attributes (such as amplitude, frequency, phase, etc.) so that each grid cell has the corresponding attribute value, and finally the well-circumferential attribute model is generated.

[0037] Compared to large-scale macroscopic displays relying solely on seismic attribute volumes or traditional regional geological models, well-perimeter attribute models can reveal subtle geological structures and stratigraphic variations within a region close to the wellbore (such as minor faults, thin interbedded layers, and localized fracture development). This provides high-resolution model support for a deeper understanding of the geological conditions around the well, aiding in the analysis of the impact of these geological features on drilling and production operations. Well-perimeter attribute models integrate multiple seismic attribute values; for example, they can simultaneously display the distribution of amplitude, frequency, and phase attributes in the well-perimeter area. This allows geologists to comprehensively analyze lithological variations, pore structure differences, and fluid distribution characteristics of the surrounding strata from multiple dimensions. For instance, by combining variations in amplitude and frequency attributes, the boundary extent and internal quality differences of a reservoir around the well can be more accurately determined, providing a more comprehensive basis for precise reservoir evaluation and production planning.

[0038] Step 104: Based on the well perimeter attribute model, obtain the attribute vector values ​​and corresponding depth values ​​within any distance range around the directional well trajectory, and generate seismic attribute curves based on the two.

[0039] Specifically, based on the aforementioned well perimeter attribute model, and taking the generation of a seismic attribute curve corresponding to a seismic attribute as an example, we extract attribute values ​​and corresponding depth values ​​for any radius around the well trajectory. For example, using the arithmetic mean method, we extract attribute vector values ​​and corresponding depth values ​​within a 50-meter radius (X start -50, end 50) around the well. Figure 5(a) shows a comparison of anisotropic seismic attributes and their corresponding generated seismic attribute curves within a 50-meter radius around the well. We also extract attribute vector values ​​and corresponding depth values ​​within a 100-meter radius west of the well (X start -100, end 0). Figure 5(b) shows a comparison of anisotropic seismic attributes and their corresponding generated seismic attribute curves within a 100-meter radius west of the well. Finally, we extract attribute vector values ​​and corresponding depth values ​​within a 100-meter radius east of the well (X start 0, end 100). Figure 5(c) shows a comparison of anisotropic seismic attributes and their corresponding generated seismic attribute curves within a 100-meter radius west of the well. The vectorized data of the extracted attribute is then added to the well logging data table, as shown in Figure 5(d). Starting from the first column, the well logging data table contains the measured depth (MD), the anisotropic attribute vector value at a radius of 50m around the well, the anisotropic attribute vector value at a distance of 100m west of the well trajectory, and the anisotropic attribute vector value at a distance of 100m east of the well trajectory. The well logging data table is then imported into the preset well logging data analysis software, which can then be used to generate seismic attribute curves.

[0040] Understandably, to generate seismic attribute curves corresponding to each seismic attribute of the seismic attribute body, the aforementioned steps can be repeated to generate the corresponding seismic attribute curves. Depending on the focus of the geological analysis and the geological information to be displayed, the curve can be plotted using one or more seismic attributes as dimensions. For example, to analyze the impact of stratigraphic lithology changes on amplitude, a curve can be plotted with amplitude as the ordinate and depth as the abscissa. To comprehensively study the relationship between multiple attributes and depth, multiple curves can be plotted or a multidimensional visualization method can be used for presentation.

[0041] The seismic attribute curves generated based on the wellbore attribute model achieve the goal of converting seismic attributes into curves, transforming seismic attributes from three-dimensional to one-dimensional vectorized curves. This involves multi-dimensional transformations from seismic graphics to seismic curves, from lateral planes to longitudinal lines, and then from longitudinal lines to data points. Through this series of transformations, a multi-dimensional, interactive wellbore geological description method integrating volumes, lines, surfaces, and points is achieved, meeting the comprehensive requirements of breadth and accuracy in directional wellbore geological description. It can present subtle geological structures and stratigraphic variations within a region close to the wellbore (such as minor faults, thin interbedded layers, and localized fracture development), as well as macroscopically display geological conditions over a large area, facilitating the analysis of the impact of these geological features on drilling and extraction operations.

[0042] According to several embodiments of the present invention, the step of sampling and matching the attribute values ​​of the seismic attribute volume in the depth domain to the well perimeter geological model framework includes: The attribute values ​​of the seismic attribute volume in the depth domain are meshed based on the rectangular well perimeter geological model framework to obtain the meshed attribute values. The attribute values ​​after gridding are sampled based on preset sampling rules and matched to the corresponding grid cells of the well perimeter geological model framework.

[0043] Specifically, please refer to Figure 4. The sampling rules include, for example, combining the inverse distance weighted statistical method and selecting a suitable sampling method from preset sampling methods (nearest neighbor sampling, linear interpolation sampling, Kriging interpolation sampling, etc.). In this embodiment, the wellbore geological model framework is a rectangular wellbore geological model framework, and the grid cells are regular rectangular grid cells. As shown in Figure 4(a), the aforementioned depth domain seismic anisotropic attribute volume is meshed based on rectangular grid cells to obtain meshed attribute values. Based on the sampling rules, the meshed attribute values ​​are sampled and matched to the corresponding grid cells of the rectangular wellbore geological model framework. That is, the corresponding attribute values ​​are obtained from the depth domain seismic attribute volume and assigned to the corresponding grid cells, so as to sample and match the corresponding attribute values ​​to the corresponding grid cells in the aforementioned constructed wellbore geological model framework. By meshing the attribute values ​​of the depth domain seismic attribute volume to match the grid form of the rectangular wellbore geological model framework, the data format is unified, so as to achieve matching to the corresponding grid cells of the wellbore geological model framework.

[0044] According to several embodiments of the present invention, the step of matching it to the corresponding grid cell of the well perimeter geological model framework includes: Based on the weights corresponding to each sampling point and each grid cell in the well perimeter geological model framework, the gridded attribute values ​​of each sampling point are matched to the corresponding grid cells.

[0045] Specifically, please refer to Figure 4. When using weights for matching, the influence of multiple factors on attribute values ​​can be considered, such as the reliability of different geological data sources and the importance of different seismic attributes in characterizing specific geological features. By reasonably allocating weights, data from different sources and of different types can be better integrated into the grid cells of the wellbore geological model framework. For example, in cases where both measured drilling data and seismic interpretation data are available, if the measured data is more accurate, it should be given a relatively larger weight, ensuring that it is ultimately matched to the appropriate grid cell.

[0046] According to several embodiments of the present invention, the weights corresponding to each sampling point and each grid cell in the well perimeter geological model framework are determined by an inverse distance weighted statistical method, the process of which includes: Based on the distance relationship between each sampling point and each grid cell in the well perimeter geological model framework, the weights corresponding to each sampling point and each grid cell are determined.

[0047] Specifically, please refer to Figure 4. Determining the weights of each sampling point and each grid cell based on the distance relationship helps to determine more matching attribute values ​​and corresponding grid cells in the future, so as to fill the attribute values ​​into the grid cells that match them better, which helps to build a more accurate well perimeter attribute model in the future.

[0048] According to several embodiments of the present invention, the step of constructing a well perimeter geological model framework based on a directional well trajectory includes: Obtain the directional well trajectory of the study area and determine the section to be observed; Obtain the starting observation depth, ending observation depth, and target geological layer thickness of the observation segment, and determine the first parameter of the well perimeter geological model framework in the vertical direction based on these three values. The second parameter of the well perimeter geological model framework in the horizontal direction is determined based on the well perimeter dimensions corresponding to the section to be observed. A matrix wellbore geological model framework is constructed based on the first and second parameters, and the grid cells of the rectangular wellbore geological model framework are determined based on the wellbore dimensions and the thickness of the target geological layer.

[0049] Specifically, referring to Figure 2, the study area and its target geological layer are first determined. The target geological layer in the study area is a coal seam. The structural model of the target geological layer in the study area is shown in Figure 2(a), and Figure 2(b) shows a cross-sectional view of the target geological layer. From the cross-sectional view of the coal seam, it can be seen that the thickness of the coal seam is 20 meters, that is, the vertical thickness is 20 meters. Based on the geological conditions of the coal seam, the target point A of the directional well in the target coal seam is determined, and a wellbore geological model framework from target point A to the bottom of the well is established. In this embodiment, the established wellbore geological model framework is a rectangular geological model framework. At the same time, the starting observation depth, ending observation depth, and thickness of the target geological layer corresponding to the section to be observed are determined to be 2536 meters, 3900 meters, and 20 meters, respectively. Based on these three, the first parameter of the wellbore geological model framework in the vertical direction is determined. At the same time, the second parameter of the wellbore geological model framework in the horizontal direction is determined according to the wellbore dimensions (wellbore radius of 100 meters) corresponding to the section to be observed. Then, a well perimeter geological model framework was constructed based on the first and second parameters. The size of the grid cells was determined based on a well perimeter radius of 100 meters and a coal seam thickness of 20 meters. This established the well perimeter geological model framework, which is helpful for subsequent modeling of seismic attributes. It enables the transformation of seismic attributes from three-dimensional solid to one-dimensional curve vectorization, and the multi-dimensional transformation from seismic graphics to seismic curves, from horizontal planes to vertical lines, and then from vertical lines to data points.

[0050] According to several embodiments of the present invention, the step of converting it into a seismic attribute volume in the depth domain includes: By using a pre-established velocity field, the seismic attribute volume in the time domain is transformed into the seismic attribute volume in the depth domain.

[0051] Specifically, please refer to Figure 3. As shown in Figure 3(a), the initially acquired seismic attribute data is time-domain seismic attribute data. The time-domain seismic attribute volume is then converted to a depth-domain seismic attribute volume. Using a pre-established velocity field, based on the correspondence between time and depth, the time-domain seismic data is transformed into depth-domain seismic data, as shown in Figure 3(b). It can be seen that the structural morphology of the time-domain and depth-domain seismic attribute volumes is similar. The specific process of time-depth conversion using the pre-established velocity field is as follows: the two-way travel time data corresponding to each data point in the seismic attribute volume (if based on a three-dimensional data volume) or each seismic trace (in the case of a two-dimensional profile) is combined with the velocity value at the corresponding position in the average velocity formula, and depth is calculated sequentially to obtain the depth-domain seismic attribute volume.

[0052] According to several embodiments of the present invention, the step of obtaining attribute vector values ​​and corresponding depth values ​​within an arbitrary distance range around a directional well trajectory based on a well perimeter attribute model includes: Based on the well perimeter attribute model, the attribute values ​​and their corresponding depth values ​​within an arbitrary distance range around the directional well trajectory are extracted; Each attribute value within an arbitrary distance range around the directional well trajectory is vectorized to obtain the corresponding attribute vector value.

[0053] Specifically, referring to Figure 5, the attribute values ​​and their corresponding depth values ​​within any distance range around the directional well trajectory can be extracted based on the well perimeter attribute model. The extracted attribute values ​​are then vectorized into corresponding attribute vector values. For example, Figure 5(a) shows a comparison of anisotropic seismic attributes and their corresponding generated seismic attribute curves within a 50-meter radius around the well; Figure 5(b) shows a comparison of anisotropic seismic attributes and their corresponding generated seismic attribute curves within a 100-meter radius west of the well; and Figure 5(c) shows a comparison of anisotropic seismic attributes and their corresponding generated seismic attribute curves within a 100-meter radius west of the well. The vectorization process for the attribute values ​​is as follows: The standardized attribute data (corresponding to specific spatial locations) is mapped sequentially as components of a vector according to the previously constructed vector space dimension. This mapping operation is performed on all spatial locations requiring vectorization (such as the locations of individual grid cells in the well perimeter attribute model), resulting in a series of attribute vector values. Converting attribute values ​​into vector attribute values ​​through vectorization helps transform seismic attributes from three-dimensional solids to one-dimensional curve vectors.

[0054] According to several embodiments of the present invention, the step of generating seismic attribute curves based on the two includes: Obtain the logging data table corresponding to the directional well; The attribute vector values ​​and their corresponding depth values ​​within any distance range around the directional well trajectory are added to the well logging data table, and then imported into the preset analysis software to generate seismic attribute curves.

[0055] Specifically, well logging is a geophysical exploration method that uses specialized instruments and equipment lowered into the wellbore to measure various physical parameters along the wellbore, and infers the lithology, physical properties, and oil-bearing capacity of the formation below the well based on changes in these physical parameters. During the well logging process, a well logging data table can be obtained, as shown in Figure 5(d). Starting from the first column, the well logging data table contains the measured depth (MD), anisotropic attribute vector values ​​at a radius of 50m around the well, anisotropic attribute vector values ​​at 100m west of the well trajectory, and anisotropic attribute vector values ​​at 100m east of the well trajectory. Importing the well logging data table into common well logging data analysis software can generate corresponding well logging curves. By adding the attribute vector values ​​and their corresponding depth values ​​within any distance range around the directional well trajectory to the well logging data table, and then importing it into well logging data analysis software, seismic attribute curves can be generated. This method transforms the lateral information of seismic data into curvilinear information distributed along the wellbore and comparable to well logging data in depth. It realizes the transformation of seismic attributes from three-dimensional to one-dimensional curve vectorization, and achieves multi-dimensional transformation from seismic graphics to seismic curves, from lateral surfaces to longitudinal lines, and from longitudinal lines to data points. Through this series of transformations, a well perimeter geological description method that integrates volume, line, surface, and point dimensions is realized to meet the comprehensive requirements of directional well perimeter geological description for breadth and accuracy.

[0056] Based on the above method of seismic attribute curve generation, the following explanation uses the generation of anisotropic body GXYX seismic attribute curves in the depth domain as an example. Please refer to Figure 2-5. The specific process is as follows. (1) Please refer to Figure 2 and construct a geological model grid around the well along a certain horizontal well trajectory. Based on the actual geological conditions of the coal seam in the study area, establish a geological model grid from the depth of target point A to the well perimeter, as shown in Figure 2(b). The initial measurement depth is 2536m to the end measurement depth is 3900m, and the vertical thickness is 20m. The parameters of the grid unit are shown in Figure 2(c). The grid unit has a horizontal width of 200m (well perimeter radius 100m) and a vertical width of 20m (coal seam vertical thickness) to construct a rectangular geological model grid around the well.

[0057] (2) Please refer to Figure 3. Establish the velocity field in advance, and convert the time domain seismic attribute volume (anisotropic volume GXYX) to depth to generate the depth domain seismic attribute volume. Figure 3(a) shows the time domain seismic data volume, and Figure 3(b) shows the converted depth domain seismic data volume. (3) Based on the well perimeter geological model framework constructed in step (1), combined with the inverse distance weighted statistical method, as shown in Figure 4(a), the seismic attribute volume GXYX in the depth domain is meshed and sampled, and its sampling is matched to the corresponding grid cell of the well perimeter geological model framework to generate the well perimeter attribute model, as shown in Figure 4(b).

[0058] (4) Based on the well perimeter attribute model obtained in step (3), extract the attribute values ​​and corresponding depth values ​​of any radius around the well trajectory. Combine the arithmetic mean method to extract them sequentially. For example, Figure 5(a) shows the well perimeter radius of 50m (X start -50, end 50), Figure 5(b) shows the well west of 100m (X start -100, end 0), and Figure 5(c) shows the well east of 100m (X start 0, end 100). Add the vectorized data to the horizontal well data table. This data table is a logging data table, as shown in Figure 5(d). In the horizontal well data table, starting from the first column, the values ​​are: depth MD, anisotropic attribute vector value of well perimeter radius of 50m, anisotropic attribute vector value of well trajectory 100m west, and anisotropic attribute vector value of well trajectory 100m east. Then import it into the logging data analysis software to generate the seismic attribute curve.

[0059] By repeating steps (2)-(4), earthquake attribute curves corresponding to different earthquake attributes in the earthquake attribute body can be generated.

[0060] A second aspect of the present invention provides a method for applying seismic attribute curves to fracture morphology prediction in hydraulic fracturing technology, comprising: Obtain the seismic sensitive attribute volume related to fracturing as the seismic attribute volume, and import it into the seismic attribute curve conversion method based on any of the foregoing embodiments, and use the generated seismic attribute curve as the sensitive attribute curve; Sensitive attribute curves are graded according to different developmental morphologies of cracks to generate sensitive attribute graded curves for predicting crack morphology.

[0061] Specifically, fracturing technology applies high pressure to the formation surrounding the wellbore, forcing the formation rock to fracture or expand existing fractures, thereby increasing formation permeability and allowing oil and gas to flow more easily into the wellbore, thus increasing oil and gas production. During fracturing design, excessively large fracturing operations lead to increased costs and construction risks; conversely, an inappropriate fracturing design resulting in uneven fracture expansion can lead to super-fractures, inhibiting the development of adjacent fractures. Obtaining seismically sensitive attributes related to fracturing is crucial, including amplitude attributes (instantaneous amplitude, average amplitude, etc.), frequency attributes (dominant frequency, bandwidth, etc.), and phase attributes (instantaneous phase, relative phase, etc.). Based on the acquired seismic sensitive attribute volume, sensitive attribute curves are generated using the aforementioned seismic attribute curveization method. The specific process will not be elaborated here. This method converts seismic sensitive attributes related to fracturing into one-dimensional curves, transforming the lateral information of seismic data into curves that are distributed along the wellbore and can be compared with well logging data in depth. Subsequently, the seismic attribute information and well logging information are integrated into the same geological analysis platform to comprehensively analyze the geological conditions around the well, meeting the comprehensive requirements of breadth and accuracy for wellbore geological description.

[0062] According to several embodiments of the present invention, the different degrees of crack development are predetermined, and the determination process includes: If the crack is within the range of 0 ≤ the value on the sensitivity attribute curve < the first threshold, the crack is determined to be in an underdeveloped state; If the crack is within the range of the first threshold ≤ the value on the sensitive attribute curve < the second threshold, the crack is determined to be in a relatively developed state, where the second threshold is greater than the first threshold; If the crack is within the range of ≥ the second threshold, the crack is determined to be in the developmental stage.

[0063] Specifically, based on a comprehensive analysis of factors including core samples, tectonic phases, field outcrops, array sonic logging, and pre-stack fracture prediction results, critical values ​​for the anisotropic properties of fractures are set, and the following judgment process is established based on these values: If 0 ≤ GXYX < 110, the crack is determined to be in an underdeveloped state. If 110≤GXYX<150, the crack is determined to be in a relatively developed state; If GXYX≥150, the crack is determined to be in the development stage.

[0064] The sensitive attribute curves are generated using the aforementioned seismic attribute curve generation method. The specific process will not be detailed here, but will be illustrated using Figures 5(a)-(c), taking the anisotropic seismic attribute curves within 100m west of the well (Figure 5(b)) and the anisotropic seismic attribute curves within 100m east of the well (Figure 5(c)) as examples. Figure 6Based on the above judgment process, conditional statements are written and run in the software to generate graded seismic attribute curves within a 100m range west of the well and within a 100m range east of the well. Figure 6 The middle part of the two graded curves is a graded profile of the sensitive attribute body (taking the anisotropic body GXYX as an example). The middle line of the anisotropic body GXYX is the horizontal well trajectory. The area below the line is the west well, and the area above the line is the east well. The value range of the curve corresponding to the west well is 70~207, and the value range of the curve corresponding to the east well is 79~212. By comparison, it can be seen that the trend magnitude of the curves on both sides of the horizontal well matches the strength of the attribute on both sides.

[0065] According to several embodiments of the present invention, after the step of generating the sensitivity attribute curve, the method further includes: Obtain the logging curves corresponding to the directional well; The sensitive attribute curve and the logging curve are aligned by depth value based on the same depth value calibration rule, and then the fracture distribution is analyzed based on the two after depth value alignment.

[0066] Specifically, if there are multiple seismic sensitive attributes, the aforementioned seismic attribute curve generation method is repeated to generate sensitive attribute curves. The specific process will not be elaborated further. For example, in this embodiment, there are four sensitive attributes. Figure 7 As shown in (a), corresponding seismic attribute curves are generated: fracture ant attribute curve, anisotropy attribute curve, maximum curvature attribute curve, and structural dip angle attribute curve. These four curves are analyzed on the same platform as the well logging GR (Gamma Ray Logging) curves, as shown in (a). Figure 7 (b) is a comparative profile of the four curves and the well logging GR curve at the same platform and the same MD depth. Figure 7 (c) is a three-dimensional comparison of the attribute curves and the well logging GR curve.

[0067] By comparing and analyzing the attribute curves and logging curves on the same platform, the lateral information of seismic data and the depth information of logging data can be fully combined. The logging data and seismic data can be highly integrated, meeting the comprehensive requirements of well perimeter geological description for breadth and accuracy, improving the prediction accuracy of fracture distribution, and supporting the design of horizontal well fracturing construction schemes.

[0068] A third aspect of the present invention provides an apparatus for seismic attribute curve mapping. Figure 8 A schematic diagram of a seismic attribute curve plotting device provided by an embodiment of the present invention is shown, as follows: Figure 8As shown, it includes: a first module 011, used to construct a well-circumferential geological model framework based on the directional well trajectory; a second module 012, used to acquire seismic attribute volumes and convert them into depth-domain seismic attribute volumes; a third module 013, used to sample and match the attribute values ​​of each attribute volume in the depth domain seismic attribute volume into the well-circumferential geological model framework to generate a well-circumferential attribute model; and a fourth module 014, used to acquire attribute vector values ​​and corresponding depth values ​​within any distance range around the directional well trajectory based on the well-circumferential attribute model, and generate seismic attribute curves based on the two.

[0069] A fourth aspect of the present invention provides an electronic device, Figure 9 The diagram shown is a schematic representation of an electronic device provided in an embodiment of the present invention. For example... Figure 9 As shown, an electronic device provided in an embodiment of the present invention includes the following modules: at least one processor 021; and a memory 022, the memory 022 storing computer instructions 023 that can be executed on the processor 021, the computer instructions 023 implementing the steps of the method for seismic attribute curveization as described above when executed by the processor 021.

[0070] A fifth aspect of the present invention also provides a computer-readable storage medium. Figure 10 The diagram shown is a structural schematic of a computer-readable storage medium provided in an embodiment of the present invention. Figure 10 As shown, computer-readable storage medium 031 stores a computer program 032 that, when executed by a processor, performs the steps of the method for seismic attribute curve shaping as described above. The method performed is the same as above.

[0071] Finally, it should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program for setting system parameters can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium for the program can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The above computer program embodiments can achieve the same or similar effects as any of the corresponding foregoing method embodiments.

[0072] Furthermore, the method disclosed in the embodiments of the present invention can also be implemented as a computer program executed by a processor, which may be stored in a computer-readable storage medium. When the computer program is executed by the processor, it performs the functions defined in the method disclosed in the embodiments of the present invention.

[0073] Furthermore, the above-described method steps and system units can also be implemented using a controller and a computer-readable storage medium for storing a computer program that enables the controller to perform the functions of the above-described steps or units.

[0074] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the functionality of various illustrative components, blocks, modules, circuits, and steps has been generally described. Whether this functionality is implemented as software or as hardware depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the embodiments disclosed herein.

[0075] In one or more exemplary designs, functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, functionality may be stored as one or more instructions or code on or transmitted via a computer-readable medium. Computer-readable media include computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one location to another. Storage media may be any available medium accessible to a general-purpose or special-purpose computer. By way of example, and not limitation, computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, or any other medium that may be used to carry or store the required program code in the form of instructions or data structures and is accessible to a general-purpose or special-purpose computer or a general-purpose or special-purpose processor. Furthermore, any connection may be appropriately referred to as computer-readable media. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the aforementioned coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are all included in the definition of media. As used herein, disks and optical discs include compact discs (CDs), laser discs, optical discs, digital multifunction discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0076] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0077] It should be understood that, as used herein, the singular form “a” is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, “and / or” refers to any and all possible combinations of one or more of the associated listed items.

[0078] The embodiment numbers disclosed in the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0079] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0080] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for seismic attribute curve mapping, characterized in that, include: A well perimeter geological model framework was constructed based on directional well trajectories. Obtain the seismic attribute volume and convert it into a depth domain seismic attribute volume; The attribute values ​​of each attribute in the seismic attribute volume of the depth domain are sampled and matched into the well perimeter geological model framework to generate a well perimeter attribute model; Based on the well perimeter attribute model, the attribute vector values ​​and corresponding depth values ​​within any distance range around the directional well trajectory are obtained, and a seismic attribute curve is generated based on the two.

2. The method for seismic attribute curve mapping according to claim 1, characterized in that, The step of sampling and matching the attribute values ​​of the seismic attribute volume in the depth domain to the well perimeter geological model framework includes: The attribute values ​​of the seismic attribute volume in the depth domain are meshed based on the rectangular well perimeter geological model framework to obtain the meshed attribute values. The gridded attribute values ​​are sampled based on preset sampling rules and matched to the corresponding grid cells of the well perimeter geological model framework.

3. The method for seismic attribute curve mapping according to claim 2, characterized in that, The step of matching it to the corresponding grid cell of the well perimeter geological model framework includes: Based on the weights corresponding to each sampling point and each grid cell in the well perimeter geological model framework, the gridded attribute values ​​of each sampling point are matched to the corresponding grid cells.

4. The method for seismic attribute curve mapping according to claim 3, characterized in that, The process of determining the weights of each sampling point and each grid cell in the well perimeter geological model framework includes: Based on the distance relationship between each sampling point and each grid cell in the well perimeter geological model framework, the weights corresponding to each sampling point and each grid cell are determined.

5. The method for seismic attribute curve mapping according to claim 2, characterized in that, The steps for constructing a well perimeter geological model framework based on directional well trajectories include: Obtain the directional well trajectory of the study area and determine the section to be observed; The starting observation depth value, the ending observation depth value, and the thickness value of the target geological layer of the observation segment are obtained, and the first parameter of the well perimeter geological model grid in the vertical direction is determined based on the three. The second parameter of the well perimeter geological model frame in the horizontal direction is determined based on the well perimeter dimensions corresponding to the section to be observed. The matrix well perimeter geological model grid is constructed based on the first parameter and the second parameter, and the grid cells of the rectangular well perimeter geological model grid are determined based on the well perimeter dimensions and the thickness value of the target geological layer.

6. The method for seismic attribute curve mapping according to claim 1, characterized in that, The step of converting it into a depth-domain seismic attribute volume includes: By using a pre-established velocity field, the seismic attribute volume in the time domain is transformed into the seismic attribute volume in the depth domain.

7. The method for seismic attribute curve mapping according to claim 1, characterized in that, The step of obtaining the attribute vector values ​​and corresponding depth values ​​within an arbitrary distance range around the directional well trajectory based on the well perimeter attribute model includes: Based on the well perimeter attribute model, the attribute values ​​and their corresponding depth values ​​within an arbitrary distance range around the directional well trajectory are extracted; Each attribute value within an arbitrary distance range around the directional well trajectory is vectorized to obtain the corresponding attribute vector value.

8. The method for seismic attribute curve mapping according to claim 1, characterized in that, The step of generating seismic attribute curves based on the two includes: Obtain the logging data table corresponding to the directional well; The attribute vector values ​​and their corresponding depth values ​​within any distance range around the directional well trajectory are added to the well logging data table, and then imported into a preset well logging data analysis software to generate seismic attribute curves.

9. A method for applying seismic attribute curves, characterized in that, Fracture morphology prediction applied in fracturing technology includes: Obtain the seismic sensitive attribute volume related to fracturing as the seismic attribute volume, and import it into the seismic attribute curve conversion method based on any one of claims 1-8, and use the generated seismic attribute curve as the sensitive attribute curve; The sensitivity attribute curves are graded according to different developmental morphologies of the cracks to generate sensitivity attribute grading curves, which are used to predict crack morphology.

10. The method for applying seismic attribute curves according to claim 9, characterized in that, The process of determining the different developmental morphologies of the cracks includes: If the crack is within the range of 0 < value on the sensitivity attribute curve < first threshold, the crack is determined to be in an underdeveloped state; If the crack is within the range of the first threshold ≤ value on the sensitivity attribute curve < second threshold, the crack is determined to be in a relatively developed state, wherein the second threshold is greater than the first threshold; If the crack is within the range of ≥ the second threshold, the crack is determined to be in a developmental stage.

11. The method for applying seismic attribute curves according to claim 9, characterized in that, Following the step of generating the sensitivity attribute curve, the method further includes: Obtain the logging curves corresponding to the directional well; The sensitive attribute curve and the logging curve are aligned by depth value based on the same depth value calibration rule, and then the fracture distribution is analyzed based on the two after depth value alignment.

12. A device for seismic attribute curve mapping, characterized in that, include: The first module is used to construct a well perimeter geological model framework based on directional well trajectories; The second module is used to obtain the seismic attribute volume and convert it into a depth domain seismic attribute volume; The third module is used to sample and match the attribute values ​​of the seismic attribute body in the depth domain to the well perimeter geological model framework to generate a well perimeter attribute model. The fourth module is used to obtain the attribute vector values ​​and corresponding depth values ​​within any distance range around the directional well trajectory based on the well perimeter attribute model, and to generate seismic attribute curves based on the two.

13. An electronic device, characterized in that, include: At least one processor; as well as A memory storing computer instructions executable on the processor, which, when executed by the processor, implement the steps of the method for seismic attribute curve mapping as described in any one of claims 1-8.

14. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for seismic attribute curve mapping as described in any one of claims 1-8.

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