Methods and apparatus for determining reservoir boundaries

Abstract

This application discloses a method and apparatus for determining reservoir boundaries, belonging to the field of oil and gas exploration. The method provided in this application first acquires attribute data corresponding to multiple seismic attributes, then binarizes the attribute data corresponding to each seismic attribute to obtain binarized data. A planar map is then drawn based on the binarized data, and multiple isopleths corresponding to preset boundary values ​​are determined on the planar map. The attribute regions of the seismic attributes are determined based on the multiple isopleths, and finally, the reservoir boundary is determined based on the attribute regions corresponding to at least one target seismic attribute. Therefore, this method can automatically determine reservoir boundaries using binarized data, eliminating the need for manual boundary picking and thus improving the efficiency of reservoir boundary determination.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration technology, and in particular to a method and apparatus for determining reservoir boundaries. Background Technology

[0002] Oil and gas reservoir boundaries are crucial data for calculating oil and gas trap areas, reservoir volumes, and reserves, playing a vital role in reservoir prediction. Therefore, determining oil and gas reservoir boundaries has become a key research focus.

[0003] In related technologies, the process of determining reservoir boundaries is as follows: acquiring seismic data, then manually drawing a plan view based on the seismic data, and picking the reservoir boundaries on the plan view.

[0004] However, manually picking reservoir boundaries takes a long time, resulting in low efficiency in determining reservoir boundaries. Summary of the Invention

[0005] This application provides a method and apparatus for determining reservoir boundaries, which can improve the efficiency of determining reservoir boundaries. The technical solution is as follows:

[0006] On the one hand, a method for determining reservoir boundaries is provided, the method comprising:

[0007] Acquire attribute data corresponding to various seismic attributes within the target work area;

[0008] For each earthquake attribute, the attribute data corresponding to the earthquake attribute is binarized to obtain binarized data; wherein, the binarized data includes a first value and a second value, and the first value is less than the second value;

[0009] Based on the binarized data, a first planar map corresponding to the seismic attributes is drawn;

[0010] The first planar image is meshed to obtain the second planar image;

[0011] In the second planar view, multiple target grids are defined, wherein each target grid is a grid that simultaneously includes the first value and the second value;

[0012] Based on the multiple target grids, multiple isopleth points corresponding to preset boundary values ​​are determined in the second planar diagram; wherein, the preset boundary value is greater than the first value and less than the second value;

[0013] Based on the multiple isopleths, the first attribute region corresponding to the seismic attribute is determined in the second planar map;

[0014] Determine at least one target seismic attribute from the multiple seismic attributes;

[0015] The reservoir boundary is determined based on the first attribute region corresponding to the at least one target seismic attribute.

[0016] On the other hand, a reservoir boundary determination apparatus is provided, the apparatus comprising:

[0017] The acquisition module is used to acquire attribute data corresponding to various seismic attributes within the target work area;

[0018] The first processing module is used to perform binarization processing on the attribute data corresponding to each earthquake attribute to obtain binarized data; wherein the binarized data includes a first value and a second value, and the first value is less than the second value;

[0019] A drawing module is used to draw a first planar map corresponding to the seismic attributes based on the binarized data;

[0020] The second processing module is used to perform gridding processing on the first plan view to obtain the second plan view;

[0021] A first determining module is used to determine multiple target grids in the second planar diagram, wherein the target grids are grids that simultaneously include the first value and the second value;

[0022] The second determining module is used to determine multiple isopleth points corresponding to preset boundary values ​​in the second planar diagram based on the multiple target grids; wherein the preset boundary value is greater than the first value and less than the second value;

[0023] The third determining module is used to determine the first attribute region corresponding to the seismic attribute in the second planar map based on the plurality of isopleth points;

[0024] The fourth determining module is used to determine at least one target seismic attribute among the multiple seismic attributes;

[0025] The fifth determining module is used to determine the reservoir boundary based on the first attribute region corresponding to the at least one target seismic attribute.

[0026] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one piece of program code, which is loaded and executed by the processor to implement the reservoir boundary determination method described in any of the preceding claims.

[0027] On the other hand, a computer-readable storage medium is provided, wherein at least one piece of program code is stored in the computer-readable storage medium, the at least one piece of program code being loaded and executed by a processor to implement the reservoir boundary determination method described in any of the preceding claims.

[0028] On the other hand, a computer program product is provided, wherein at least one piece of program code is stored in the computer program product, the at least one piece of program code being loaded and executed by a processor to implement the reservoir boundary determination method described in any of the preceding claims.

[0029] This application provides a method for determining reservoir boundaries. The method first acquires attribute data corresponding to multiple seismic attributes, then binarizes the attribute data for each seismic attribute to obtain binarized data. A planar map is then drawn based on the binarized data, and multiple contour points corresponding to preset boundary values ​​are determined on the planar map. The attribute regions of the seismic attributes are determined based on these contour points. Finally, the reservoir boundary is determined based on the attribute regions corresponding to at least one target seismic attribute. For each seismic attribute, since the preset boundary value lies between the first and second values ​​of the binarized data, the attribute region determined by the contour points corresponding to the preset boundary value is the reservoir region possessing that seismic attribute, thus determining the boundary of reservoir regions simultaneously possessing at least one seismic attribute. Therefore, this method can automatically determine reservoir boundaries without manual boundary picking, thereby improving the efficiency of reservoir boundary determination.

[0030] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the implementation environment of a reservoir boundary determination method provided in an embodiment of this application;

[0032] Figure 2 This is a flowchart of a reservoir boundary determination method provided in an embodiment of this application;

[0033] Figure 3 This is a flowchart of a reservoir boundary determination method provided in an embodiment of this application;

[0034] Figure 4 This is a schematic diagram illustrating the distribution characteristics of attribute data in a third planar view, provided by an embodiment of this application.

[0035] Figure 5 This application provides a method for processing data based on binarized data 0 and 1. Figure 4 A schematic diagram of the first planar view obtained after transforming the third planar view shown;

[0036] Figure 6 This is a schematic diagram of contour tracing provided in an embodiment of this application;

[0037] Figure 7 This is a schematic diagram of a plurality of first attribute regions provided in an embodiment of this application;

[0038] Figure 8 This is a schematic diagram illustrating the deletion of a smaller first attribute region according to an embodiment of this application;

[0039] Figure 9 This is a schematic diagram illustrating the determination of reservoir boundaries provided in an embodiment of this application;

[0040] Figure 10 This is a schematic diagram of a reservoir boundary determination device provided in an embodiment of this application;

[0041] Figure 11 This is a structural block diagram of a terminal provided in an embodiment of this application. Detailed Implementation

[0042] To make the technical solution and advantages of this application clearer, the embodiments of this application will be described in further detail below.

[0043] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0044] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the earthquake data and attribute data involved in this application were obtained with full authorization.

[0045] Figure 1 This is a schematic diagram illustrating the implementation environment of a reservoir boundary determination method provided in this application embodiment. See also... Figure 1 The implementation environment includes electronic devices, which can be provided as terminal 101, or as a combination of terminal 101 and server 102, without specific limitations.

[0046] If the electronic device is provided as terminal 101, and the target application is installed on terminal 101, the user can log in to the target application and then determine the reservoir boundary using the method provided in this application. The reservoir can be an oil layer, a gas layer, an oil-water layer, a gas-water layer, etc., without specific limitations.

[0047] If the electronic devices are provided as terminal 101 and server 102, then terminal 101 and server 102 can be connected via a wireless or wired network. Accordingly, a target application is installed on terminal 101, and server 102 is the server corresponding to the target application. When the user determines the reservoir boundary through the target application, server 102 provides background services. In this embodiment, only terminal 101 is used as an example for explanation.

[0048] Among them, terminal 101 is at least one of the following: mobile phone, tablet computer, PC (Personal Computer) device, intelligent voice interaction device, and vehicle terminal. Server 102 is at least one of the following: a single server, a server cluster consisting of multiple servers, a cloud server, a cloud computing platform, and a virtualization center.

[0049] Figure 2 This is a flowchart of a reservoir boundary determination method provided in an embodiment of this application, executed by an electronic device. See also... Figure 2 The method includes:

[0050] Step 201: The electronic device acquires attribute data corresponding to various seismic attributes within the target work area.

[0051] The target study area is the region where the reservoir under study is located. The number of various seismic attributes can be set and changed as needed, without specific limitations. For example, seismic attributes can be amplitude, phase, frequency, porosity, clay content, etc., or other types of seismic attributes, without specific limitations.

[0052] Electronic equipment obtains detailed interpretation results of horizons and faults during the structural interpretation process based on seismic data. Then, based on the detailed interpretation results of horizons and faults, it extracts attribute data corresponding to multiple seismic attributes from the seismic data along the horizons, or extracts attribute data corresponding to multiple seismic attributes through inversion, without making specific limitations on this.

[0053] Step 202: For each earthquake attribute, the electronic device performs binarization processing on the attribute data corresponding to the earthquake attribute to obtain binarized data; wherein, the binarized data includes a first value and a second value, and the first value is less than the second value.

[0054] For each seismic attribute, there are multiple corresponding attribute data. For each attribute data, the electronic device can determine whether the attribute data is within a valid threshold range. If it is within the valid threshold range, the attribute data is assigned a second value; if it is not within the valid threshold range, the attribute data is assigned a first value, thereby achieving binarization of the attribute data.

[0055] The first and second values ​​can be set and changed as needed. For example, if the first value is 0 and the second value is 1, the electronic device can assign the attribute data to 0 or 1 based on the relationship between the attribute data and the effective threshold range.

[0056] Step 203: The electronic device draws the first planar map corresponding to the seismic attributes based on the binarized data.

[0057] The electronic device draws a first planar graph based on a first value and a second value. If the first value is 0 and the second value is 1, then after binarization, the attribute data will only have two values: 0 and 1. The electronic device draws the first planar graph based on 0 and 1, and in the first planar graph, attribute data with a binarized value of 0 and attribute data with a binarized value of 1 can be distinguished by two different colors.

[0058] Step 204: The electronic device performs meshing processing on the first planar image to obtain the second planar image.

[0059] The electronic device can establish a Cartesian coordinate system in the first planar view, and divide the first planar view into multiple grids in the Cartesian coordinate system to obtain the second planar view. The length and width of the grids can be set and changed as needed, without specific limitations.

[0060] Step 205: The electronic device determines multiple target grids in the second plan view. The target grids are grids that simultaneously include the first value and the second value.

[0061] The electronic device can scan multiple grids in the second planar image according to a certain scanning direction, and determine the grids that simultaneously include the first and second values ​​from the multiple grids to obtain multiple target grids. The finer the grid division, and the smaller the length and width of the grids, the more advantageous it is to determine the target grids.

[0062] Step 206: The electronic device determines multiple isopleths corresponding to preset boundary values ​​in the second planar graph based on multiple target grids; wherein the preset boundary values ​​are greater than the first value and less than the second value.

[0063] Since the first value represents attribute data that is not within the effective threshold range and the second value represents attribute data that is within the effective threshold range, a preset boundary value is determined between the first value and the second value. Then, multiple isopleths corresponding to the preset boundary value are determined in the second planar diagram, and the reservoir and non-reservoir are distinguished by multiple isopleths.

[0064] The preset boundary values ​​can be set and changed as needed. For example, the preset boundary values ​​can be 0.3, 0.5 or 0.8, without any specific limitation.

[0065] Step 207: The electronic device determines the first attribute region corresponding to the seismic attribute in the second plan view based on multiple contour points.

[0066] Multiple isopleths include multiple first isopleths and multiple second isopleths. The electronic device can first determine a second attribute region in the second planar diagram based on the multiple first isopleths. Then, based on the multiple second isopleths, a third attribute region is determined within the second attribute region, and a fifth attribute region is determined within the fourth attribute region; the third and fifth attribute regions are then combined to form the first attribute region. The second attribute region is a non-closed region, the third and fifth attribute regions are both closed regions, and the fourth attribute region is the attribute region in the second planar diagram excluding the second attribute region.

[0067] Step 208: The electronic device determines at least one target seismic attribute among multiple seismic attributes.

[0068] At least one target seismic attribute is one or more of a variety of seismic attributes.

[0069] For example, in step 201, if the electronic device acquires attribute data corresponding to three earthquake attributes, then in this step, the electronic device can use one of the three earthquake attributes as at least one target earthquake attribute, or it can use two of the three earthquake attributes as at least one target earthquake attribute, or it can use all three earthquake attributes as at least one target earthquake attribute, without making any specific limitations.

[0070] Step 209: The electronic device determines the reservoir boundary based on the first attribute region corresponding to at least one target seismic attribute.

[0071] If the number of at least one target seismic attribute is 1, the electronic device directly determines the boundary of the first attribute region corresponding to this 1 target seismic attribute as the reservoir boundary.

[0072] If the number of at least one target seismic attribute is greater than 1, the electronic device can fuse the first attribute regions corresponding to at least one target seismic attribute and determine the boundary of the fused attribute region as the reservoir boundary.

[0073] This application provides a method for determining reservoir boundaries. The method first acquires attribute data corresponding to multiple seismic attributes, then binarizes the attribute data for each seismic attribute to obtain binarized data. A planar map is then drawn based on the binarized data, and multiple contour points corresponding to preset boundary values ​​are determined on the planar map. The attribute regions of the seismic attributes are determined based on these contour points. Finally, the reservoir boundary is determined based on the attribute regions corresponding to at least one target seismic attribute. For each seismic attribute, since the preset boundary value lies between the first and second values ​​of the binarized data, the attribute region determined by the contour points corresponding to the preset boundary value is the reservoir region possessing that seismic attribute, thus determining the boundary of reservoir regions simultaneously possessing at least one seismic attribute. Therefore, this method can automatically determine reservoir boundaries without manual boundary picking, thereby improving the efficiency of reservoir boundary determination.

[0074] Figure 3 This is a flowchart of a reservoir boundary determination method provided in an embodiment of this application, executed by an electronic device. See also... Figure 3 The method includes:

[0075] Step 301: The electronic device acquires attribute data corresponding to various seismic attributes within the target work area.

[0076] In this step, after the electronic equipment obtains the detailed interpretation results of the horizon and faults in the structural interpretation process based on the seismic data, it can extract attribute data corresponding to various seismic attributes from the seismic data along the horizon based on the detailed interpretation results, or it can extract attribute data corresponding to various seismic attributes through inversion.

[0077] In one possible implementation, electronic devices extract attribute data corresponding to multiple seismic attributes along the stratigraphic plane.

[0078] In this implementation, electronic equipment identifies stratigraphic erosion surfaces and acquires stratigraphic data characterizing these erosion surfaces based on seismic data. The selected stratigraphic layers must be stably developed throughout the region and exhibit clear unconformity. Then, attribute data corresponding to various seismic attributes are extracted from the seismic data along these stratigraphic layers.

[0079] In another possible implementation, electronic devices extract attribute data corresponding to various seismic attributes through inversion.

[0080] In this implementation, after the electronic device retrieves parameters such as wave impedance, velocity ratio, and elastic body from the earthquake, it can convert the retrieved volume into an attribute volume corresponding to the earthquake attribute based on the relationship between these parameters and the earthquake attribute, and extract the attribute data corresponding to the earthquake attribute from the attribute volume.

[0081] For example, if the seismic attribute is porosity, during well logging cross-plotting, a relationship is fitted between wave impedance and porosity. Using this relationship, the inverted volume is transformed into a volume of porosity. Then, the attribute data corresponding to porosity is extracted from the volume of porosity.

[0082] Of course, electronic devices can also extract attribute data corresponding to multiple seismic attributes from seismic data through other methods, without specific limitations. The number of seismic attributes can be set and changed as needed, without specific limitations. For example, seismic attributes can be amplitude, phase, frequency, porosity, clay content, etc., or other types of seismic attributes, without specific limitations.

[0083] In this embodiment, the electronic device uses attribute data corresponding to multiple seismic attributes as the basis for reservoir boundary tracking, which can improve the accuracy of determining reservoir boundaries.

[0084] It should be noted that after the electronic device acquires the attribute data corresponding to multiple seismic attributes, it can directly execute step 302, or it can first filter the attribute data corresponding to multiple seismic attributes and then execute step 302. There is no specific limitation on this.

[0085] When performing filtering, electronic devices can use Gaussian filtering, bilateral filtering, anisotropic filtering, or other filtering algorithms to filter attribute data corresponding to various seismic attributes.

[0086] Gaussian filtering, a linear smoothing filter, is suitable for eliminating Gaussian noise and is widely used in image processing noise reduction. This algorithm primarily uses a normal distribution to calculate the transformation of each pixel in the image. The normal distribution equation for Gaussian filtering in two-dimensional space is: Where σ is the standard deviation of the normal distribution.

[0087] In two-dimensional space, the contour lines of the surface generated by this equation are concentric circles that follow a normal distribution starting from the center. A convolution matrix composed of pixels with non-zero values ​​is used to transform the original image. The value of each pixel is a weighted average of the values ​​of its surrounding pixels. The original pixel has the largest Gaussian distribution value, so it has the largest weight. The weight of neighboring pixels decreases as they become farther away from the original pixel. This blurring process can effectively remove random noise.

[0088] Bilateral filtering not only removes random noise but also preserves edge features well. It considers not only the influence of position on the center pixel but also the similarity between pixels in the convolution kernel and the center pixel. Two different weight tables are generated based on the positional influence and the similarity between pixel values. The difference between the spatial domain and the value domain is also taken into account. These two weights are considered when calculating the center pixel, thus achieving bilateral low-pass filtering.

[0089] The equation for bilateral filtering is: Where f(·) is the noisy image, h(·) is the restored image, c(·) and 5(·) are functions that measure geometric distance and restoration similarity, respectively, and k is a standardization coefficient.

[0090] Anisotropic filtering is an image processing technique based on partial differential equations. It uses 3D structural tensors to control the smoothing intensity, which can enhance the continuity of features while suppressing incoherent noise, and at the same time avoid smoothing the boundaries.

[0091] Attribute data often contains a large amount of random noise, which can interfere with the subsequent determination of reservoir boundaries, resulting in jagged and burr-like phenomena in the results. In the embodiments of this application, filtering not only improves the quality of low signal-to-noise ratio data and reduces random noise, but also significantly improves the accuracy and efficiency of reservoir boundary determination, making the reservoir boundaries regular and orderly, and more consistent with the actual boundaries.

[0092] In this embodiment, after filtering the attribute data corresponding to multiple seismic attributes, the electronic device can transfer the attribute data corresponding to multiple seismic attributes into a raster data container and store it in the memory of the electronic device in raster data format. When determining the reservoir boundary later, the attribute data corresponding to multiple seismic attributes can be obtained from the raster data container.

[0093] Step 302: For each earthquake attribute, the electronic device performs binarization processing on the attribute data corresponding to the earthquake attribute to obtain binarized data.

[0094] Electronic devices can perform binarization processing on attribute data through the following steps (1) to (2) to obtain binarized data.

[0095] (1) The electronic device draws a third plane map based on the attribute data corresponding to the earthquake attribute, and distinguishes the attribute data of different ranges in the third plane map by multiple markers.

[0096] Among them, multiple labels are used to characterize the distribution characteristics of the attribute data corresponding to the earthquake attribute.

[0097] The electronic device retrieves the attribute data corresponding to the earthquake attribute from memory, displays the attribute data in the form of a two-dimensional planar graph, and uses different markers to represent attribute data of different ranges.

[0098] For example, electronic devices use different colors to represent different ranges of attribute data; red represents attribute data in the first range, and yellow represents attribute data in the second range. By using different color combinations to represent attribute data in different ranges, the distribution characteristics of the attribute data are clearly displayed in the third-plane plot. See [link to relevant documentation]. Figure 4 .

[0099] Electronic devices can also represent attribute data of different ranges in other ways, such as lines of different thicknesses, lines of different thicknesses plus different colors, without being specifically limited to these.

[0100] (2) The electronic device performs binarization processing on the attribute data based on the distribution characteristics of the attribute data in the third plane diagram to obtain binarized data.

[0101] The binarized data includes a first value and a second value, and the first value is less than the second value.

[0102] The electronic device determines the effective threshold range based on the distribution characteristics of the attribute data in the third plane diagram. For each attribute data, if the attribute data is within the effective threshold range, the attribute data is modified to the second value; if the attribute data is not within the effective threshold range, the attribute data is modified to the first value.

[0103] The electronic device can display a settings interface. In one possible implementation, the settings interface displays a first input box and a second input box. Based on the distribution characteristics of attribute data in a third planar graph, the user can input the lower limit of the effective threshold range in the first input box and the upper limit of the effective threshold range in the second input box. Correspondingly, the electronic device acquires the lower and upper limit values ​​input by the user and combines them to form the effective threshold range.

[0104] In another possible implementation, the settings interface displays a first dropdown option and a second dropdown option. In response to a trigger operation that detects the first dropdown option being triggered, the electronic device displays multiple values, and the selected value among these values ​​is determined as the lower limit of the valid threshold range. In response to a trigger operation that detects the second dropdown option being triggered, the electronic device displays multiple values, and the selected value among these values ​​is determined as the upper limit of the valid threshold range. The lower limit and the upper limit together constitute the valid threshold range.

[0105] Electronic devices may also determine the effective threshold range in other ways, without specific limitations.

[0106] In the embodiments of this application, the first value and the second value can be set and changed as needed, and there is no specific limitation. The following is an example of the first value being 0 and the second value being 1.

[0107] For example, the electronic device acquires some attribute data as shown in Table 1. The effective threshold range determined by the electronic device is 0.5-1.0. The electronic device assigns the attribute data in Table 1 that is in the range of 0.5-1.0 to 1 and assigns the attribute data that is not in the range of 0.5-1.0 to 0, thus obtaining the binarized data shown in Table 2.

[0108] Table 1

[0109] 1.0205 2.3682 1.7584 1.0816 1.0662 2.7549 2.7267 2.5827 0.9067 0.9776 0.6823 2.6011 2.9578 1.6088 0.5432 0.8589 0.6951 1.6903 1.1767 1.0497 0.6729 0.7017 0.9609 1.8309 1.8380 1.0756 1.9804 1.4286 1.7619 1.0945 1.5718 2.6121 1.5907 2.5221 1.5109 2.6777

[0110] Table 2

[0111] 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0

[0112] Step 303: The electronic device draws the first planar map corresponding to the seismic attributes based on the binarized data.

[0113] The electronic device performs a binarization transformation on the third planar map based on the first and second numerical values ​​to obtain the first planar map. The electronic device can display a confirm and cancel option. If the user confirms that the converted planar map clearly reflects the oil and gas distribution characteristics, the confirm option can be triggered. Correspondingly, in response to the triggering operation that detects the confirm option being activated, the electronic device identifies the converted planar map as the first planar map.

[0114] If the user believes that the converted planar map cannot clearly reflect the oil and gas distribution characteristics, the cancellation option is triggered. Correspondingly, in response to the detection of the cancellation option being triggered, the electronic device returns to the settings interface for the effective threshold range, where the effective threshold range is redefined. Based on the redefined effective threshold range, the attribute data is re-binarized to obtain new binarized data. Based on this new binarized data, the third planar map is converted again until the converted planar map can clearly reflect the oil and gas distribution characteristics. This converted planar map is then designated as the first planar map. See also... Figure 5 , Figure 5 To perform a calculation based on the binarized data 0 and 1. Figure 4 The first planar diagram is obtained by transforming the third planar diagram shown.

[0115] Step 304: The electronic device performs meshing processing on the first plan view to obtain the second plan view.

[0116] Electronic devices can divide the first planar view into multiple grids to obtain the second planar view.

[0117] Step 305: The electronic device determines multiple target grids in the second plan view.

[0118] The target grid is a grid that includes both the first and second values.

[0119] After binarization, the attribute data consists of only two values: the first value and the second value. Therefore, the grid in the second planar image can be divided into three types: those containing only the first value, those containing only the second value, and those containing both the first and second values. Based on this, the electronic device can scan the grid in the second planar image according to a certain scanning direction, and determine the grid that contains both the first and second values ​​as the target grid, thus obtaining multiple target grids.

[0120] Step 306: The electronic device determines multiple isopleth points corresponding to preset boundary values ​​in the second planar diagram based on multiple target grids.

[0121] The preset boundary value is greater than the first value and less than the second value, that is, the preset boundary value is a value between the first value and the second value.

[0122] In this step, the electronic device can determine multiple isopleth points corresponding to the preset boundary value through the following steps (1) to (4).

[0123] (1) For each target grid, the electronic device determines the target edge in the target grid.

[0124] The target mesh has four edges. The electronic device determines the edge containing the first and second values ​​as the target edge. Specifically, the binarized data corresponding to the first position point on the target edge is the first value, and the binarized data corresponding to the second position point on the target edge is the second value.

[0125] The first position point can be an endpoint on the target edge or any point between the two endpoints; the second position point can be an endpoint on the target edge or any point between the two endpoints, without specific limitations.

[0126] (2) The electronic device determines the first coordinate and the second coordinate.

[0127] The electronic device determines the planar coordinates of the first position point on the second plane to obtain the first coordinates; it then determines the planar coordinates of the second position point on the second plane to obtain the second coordinates.

[0128] (3) The electronic device performs linear interpolation based on the first coordinate, the second coordinate, the first value, the second value and the preset boundary value to determine the coordinates of the isopleth point corresponding to the preset boundary value.

[0129] For any edge, if the edge is parallel to the x-axis, then the y-coordinates of all points on the edge are equal. If the edge is parallel to the y-axis, then the x-coordinates of all points on the edge are equal.

[0130] For example, if the target edge is parallel to the x-axis, the x-coordinate of the first position point is x1, and the corresponding binarized data is z1. The x-coordinate of the second position point is x2, and the corresponding binarized data is z2. The y-coordinates of these two position points are equal and can be ignored.

[0131] If there exists an isopleth point corresponding to a preset boundary value on this edge, for example, if the preset boundary value is z, then (z1-z)(z2-z) < 0 must hold. According to the principle of linear interpolation, we have: Here, x3 is the x-coordinate of the isopleth point corresponding to the preset boundary value. Therefore, x3 can be calculated based on x1, x2, z1, z2, and z. Since the isopleth point corresponding to the preset boundary value is on the target edge, the y-coordinate of the isopleth point corresponding to the preset boundary value is equal to the y-coordinate of the first position point and the second position point.

[0132] If the first value is 0 and the second value is 1, the preset boundary value can be any value between 0 and 1. For example, the preset boundary value is 0.3, 0.5 or 0.8, and there is no specific limitation on this.

[0133] Similarly, if the target edge is parallel to the y-axis, the coordinates of the isopleth points corresponding to the preset boundary values ​​can also be calculated using the above method.

[0134] (4) The electronic device determines the isopleth point corresponding to the preset boundary value on the target edge based on the coordinates of the isopleth point corresponding to the preset boundary value.

[0135] For each target grid, the electronic device can determine the isopleth points corresponding to the preset boundary values ​​through the above steps (1) to (4), thereby obtaining multiple isopleth points.

[0136] Step 307: The electronic device determines the second attribute region in the second planar diagram based on multiple first contour points.

[0137] The second attribute region is a non-closed region.

[0138] In this step, the electronic device can determine the second attribute region through the following steps (1) to (6).

[0139] (1) The electronic device determines the starting contour point from multiple first contour points based on the first scanning direction.

[0140] The electronic device scans multiple first contour points based on a first scanning direction, and determines the first contour point that is scanned first among the multiple first contour points as the starting contour point.

[0141] The first scanning direction can be set and changed as needed. For example, the first scanning direction can be from left to right or from bottom to top; or it can be from right to left or from top to bottom. There is no specific limitation on this. For example, if the first scanning direction is from left to right or from bottom to top, the electronic device scans multiple first contour points from left to right and from bottom to top, and determines the leftmost and bottommost first contour point among the multiple first contour points as the starting contour point.

[0142] (2) The electronic device determines the first target grid and the second target grid where the starting contour point is located.

[0143] In this case, the row number of the first target grid is not equal to the row number of the second target grid, or the column number of the first target grid is not equal to the column number of the second target grid.

[0144] The first target grid and the second target grid are two adjacent grids. The starting isopleth point is on the edge of both the first target grid and the second target grid, meaning that the first target grid and the second target grid share a target edge.

[0145] If the first and second target grids are adjacent horizontally, their row numbers are equal, but their column numbers are unequal. If the first and second target grids are adjacent vertically, their column numbers are equal, but their row numbers are unequal. Therefore, it can be seen that the row and column numbers of the two grids traversed by an isoplethora point are always one equal and the other necessarily unequal. Thus, the connection direction of the first isoplethora line can be determined based on this.

[0146] (3) If the column number of the first target grid is less than the column number of the second target grid, the electronic device determines the next first contour point on the top, right or bottom edge of the second target grid and connects the starting contour point and the next first contour point.

[0147] In this embodiment, if the first scanning direction is from left to right and from bottom to top, and the row numbers of the first target grid and the second target grid are equal but the column numbers are different, then the column number of the first target grid is less than the column number of the second target grid, meaning the first target grid is to the left of the second target grid. In this case, the electronic device first determines the next first contour point on the top edge of the second target grid. If there is a first contour point on the top edge of the second target grid, the electronic device connects the starting contour point and the first contour point. If there is no first contour point on the top edge of the second target grid, the electronic device determines the next first contour point on the right edge of the second target grid. If there is a first contour point on the right edge of the second target grid, the electronic device connects the starting contour point and the first contour point. If there is no first contour point on the right edge of the second target grid, the electronic device determines the next first contour point on the bottom edge of the second target grid, and then connects the starting contour point and the first contour point on the bottom edge.

[0148] If there is no first contour point on the bottom edge of the second target grid, the electronic device determines the next target grid according to the first scanning direction, and determines the next first contour point based on the positional relationship between the next target grid and the second target grid.

[0149] (4) If the row number of the first target grid is less than the row number of the second target grid, the electronic device determines the next first contour point on the left, top or right side of the second target grid and connects the starting contour point and the next first contour point.

[0150] If the first scanning direction is from left to right and from bottom to top, and the column numbers of the first target grid and the second target grid are equal but the row numbers are different, then the row number of the first target grid is less than the row number of the second target grid, meaning the first target grid is below the second target grid. In this case, the electronic device first determines the next first contour point on the left side of the second target grid. If there is a first contour point on the left side of the second target grid, the electronic device connects the starting contour point and that first contour point. If there is no first contour point on the left side of the second target grid, the electronic device determines the next first contour point on the top side of the second target grid. If there is a first contour point on the top side of the second target grid, the electronic device connects the starting contour point and that first contour point. If there is no first contour point on the top side of the second target grid, the electronic device determines the first contour point on the right side of the second target grid, and then connects the starting contour point and the first contour point on the right side.

[0151] If there is no first contour point on the right side of the second target grid, the electronic device determines the next target grid according to the first scanning direction, and determines the next first contour point based on the positional relationship between the next target grid and the second target grid.

[0152] (5) The electronic device takes the next first contour point as the starting contour point and executes the steps of determining the first target grid and the second target grid where the starting contour point is located until the stopping condition is met.

[0153] The stopping condition can be that the row number of the second target grid is the smallest or the largest, or the column number of the second target grid is the smallest or the largest, that is, the second target grid is the edge grid in the second planar graph.

[0154] (6) The electronic device defines the region formed by multiple first contour lines after connecting multiple first contour points as the second attribute region.

[0155] Whenever the second target grid that is tracked is an edge grid in the second planar graph, the first contour line in this direction is tracked. Then, starting from the boundary of the second planar graph again, the starting contour point is determined. Tracking is carried out based on the starting contour point to determine the next first contour line. Finally, the non-closed area composed of multiple first contour lines is determined as the second attribute region.

[0156] See Figure 6 The grid labeled I represents the first target grid, the grid labeled II represents the second target grid, A1 represents the previous first contour point of the starting contour point, and A2 represents the starting contour point. Figure 6 The document lists four contour line orientations. If the row number of the first target grid is less than the row number of the second target grid, it indicates that the contour lines run from bottom to top, meaning... Figure 6 The situation is illustrated in (a). In this case, the next first contour point should be determined on the left, top, or right edge of the second target grid. If the first contour point is found on the left edge, the next target grid is the grid adjacent to the left of the second target grid, and the row number of the next target grid is equal to the row number of the second target grid, while the column number of the next target grid is less than the column number of the second target grid. Therefore, the next first contour point is both the exit point of the second target grid and the entrance point of the next grid.

[0157] If the first isopleth point is found on the top edge, the next target grid is the grid adjacent to the top of the second target grid, and the column number of the next target grid is equal to the column number of the second target grid, while the row number of the next target grid is greater than the row number of the second target grid. If the first isopleth point is found on the right edge, the next target grid is the grid adjacent to the right of the second target grid, and the row number of the next target grid is equal to the row number of the second target grid, while the column number of the next target grid is greater than the column number of the second target grid.

[0158] If the column number of the first target grid is less than the column number of the second target grid, it means that the contour lines run from left to right. Figure 6 The situation is shown in (b). In this case, the next first contour point should be determined on the top, right, or bottom edge of the second target grid.

[0159] If the row number of the first target grid is greater than the row number of the second target grid, it means that the contour lines are drawn from top to bottom. Figure 6 The situation is shown in (c). In this case, the next first contour point should be determined on the left, bottom, or right edge of the second target grid.

[0160] If the column number of the first target grid is greater than the column number of the second target grid, it means that the contour lines run from right to left. Figure 6The situation is shown in (d). In this case, the next first contour point should be determined on the top, left, or bottom edge of the second target grid. Figure 6 The process of determining the next first isopleth point in (b), (c) and (d) is the same as in (a), and will not be repeated here.

[0161] Step 308: The electronic device determines a third attribute region within a second attribute region and a fifth attribute region within a fourth attribute region based on multiple second isopleth points.

[0162] The third and fifth attribute regions are both closed regions, while the fourth attribute region is the attribute region in the second planar diagram other than the second attribute region.

[0163] The process by which an electronic device determines a third attribute region within a second attribute region is described below. This process can be achieved through the following steps (1) to (3):

[0164] (1) The electronic device connects the second contour points on multiple third target grids in the second attribute region according to the second scanning direction to obtain the second contour line.

[0165] After the electronic device tracks the first contour line starting from the boundary, it tracks within the grid to determine the third attribute region.

[0166] The number of second attribute regions obtained in step 307 can be one or more. When there are multiple second attribute regions, for each second attribute region, the electronic device determines any second contour point within the second attribute region, then determines the two adjacent third target grids containing that second contour point. Based on the positional relationship between these two adjacent third target grids and the second scanning direction, the next second contour point is determined, and this second contour point is connected to the next second contour point. The electronic device tracks the second contour points in the second scanning direction using this method to obtain the second contour line.

[0167] The process of connecting the electronic device to the second contour points on multiple third target grids is the same as the process of connecting the starting contour point and the first contour point in step 307 above, and will not be repeated here.

[0168] (2) The electronic device connects the second contour points on multiple fourth target grids in the second attribute region according to the third scanning direction to obtain the third contour line.

[0169] When no second contour point exists in the second scanning direction, the electronic device, based on the last second contour point on the second contour line, tracks along the third scanning direction, connecting the second contour points on multiple fourth target grids in the second attribute region to obtain a third contour line. The third scanning direction is opposite to the second scanning direction, and the terminating contour point of the third contour line is the starting contour point of the second contour line.

[0170] For example, if the second scanning direction is from left to right, then the third scanning direction is from right to left. That is, the electronic device first tracks from left to right, and then tracks from right to left, starting from a second contour point and finally returning to the second contour point to form a closed loop, thus obtaining a complete third contour line.

[0171] The process of connecting the electronic device to the second contour points on multiple fourth target grids is the same as the process of connecting the starting contour point and the first contour point in step 307 above, and will not be repeated here.

[0172] The above explanation uses the example of connecting the second contour points while simultaneously tracing the third contour line. In this embodiment, instead of initially connecting the traced second contour points, the traced second contour points can be marked or their coordinates stored in a temporary array. When the last traced second contour point is the same as the initial second contour point, the traced second contour points can be connected based on the marked or stored coordinates to obtain the third contour line.

[0173] In the embodiments of this application, each tracked contour point can be marked to avoid repeated tracking. In addition, it can also be known whether the current contour line has been tracked to the starting position and whether the contour line is a closed contour line to prevent endless repeated tracking.

[0174] (3) The electronic device determines the region formed by the third contour lines as the third attribute region.

[0175] A third contour line forms a closed third attribute region. If the electronic device tracks a third contour line through the above steps (1) to (2), the number of third attribute regions is 1. If the electronic device tracks multiple third contour lines through the above steps (1) to (2), the number of third attribute regions is multiple.

[0176] In this embodiment, a closed region, namely a fifth attribute region, may also exist within the fourth attribute region in the second planar view, in addition to the second attribute region. The electronic device then determines the fifth attribute region within the fourth attribute region. The number of fifth attribute regions can be one or more, without specific limitation. The process by which the electronic device determines the fifth attribute region within the fourth attribute region is similar to the process of determining the third attribute region, and will not be elaborated further here.

[0177] In this embodiment of the application, the electronic device, in the binarized planar data, for a given preset boundary value, uses contour tracing technology to first perform linear interpolation on the grid edge to obtain contour points, and then connects each contour point according to a certain discrimination orientation to segment the attribute region.

[0178] Step 309: The electronic device combines the third attribute region and the fifth attribute region into the first attribute region.

[0179] There are multiple first attribute regions.

[0180] In this embodiment, after determining the first attribute region, the electronic device can smooth the boundary of the first attribute region to make it more closely conform to the trend surface. The smoothing process can be performed using inverse distance smoothing, least squares surface fitting, surface (thin plate) spline function interpolation, or other algorithms; no specific limitation is made in this regard.

[0181] In addition, after smoothing the first attribute region, the electronic device can determine the area of ​​each first attribute region, delete multiple first attribute regions with areas smaller than a preset area, and display the remaining first attribute regions and their corresponding areas on the second planar diagram.

[0182] See Figure 7 , Figure 7 The image shows multiple first attribute regions, from Figure 7 As can be seen, some of these first attribute regions have relatively small areas. These smaller first attribute regions are of little research value. Therefore, the electronic device can delete the smaller first attribute regions and display the larger first attribute regions on the second planar view. (See also...) Figure 8 , Figure 8 This refers to the remaining first attribute region after deleting the smaller first attribute region.

[0183] Step 310: The electronic device determines at least one target seismic attribute among multiple seismic attributes.

[0184] The number of at least one target seismic attribute can be less than or equal to the number of multiple seismic attributes. When the number of at least one target seismic attribute is equal to the number of multiple seismic attributes, the electronic device treats all seismic attributes as at least one target seismic attribute.

[0185] Step 311: The electronic device determines the reservoir boundary based on the first attribute region corresponding to at least one target seismic attribute.

[0186] When the number of at least one target seismic attribute is 1, the electronic device takes the boundary of the first attribute region corresponding to the target seismic attribute as the reservoir boundary.

[0187] When the number of at least one target seismic attribute is greater than 1, the electronic device fuses the first attribute regions corresponding to at least one target seismic attribute based on the second plan view corresponding to each target seismic attribute; based on the fusion result, the intersecting region between the first attribute regions of at least one target seismic attribute is determined, and the boundary of the intersecting region is determined as the reservoir boundary.

[0188] In this implementation, the electronic device can draw a blank fourth planar map, which has the same area as the second planar map. Then, the first attribute region of at least one target seismic attribute is projected onto the fourth planar map. The first attribute regions of at least one target seismic attribute will intersect, and the electronic device determines the boundary of the intersecting region as the reservoir boundary. Therefore, the reservoir boundary determined in this implementation is the boundary corresponding to a reservoir that simultaneously possesses at least one target seismic attribute.

[0189] It should be noted that after the electronic device determines the intersecting region, it can either directly define the boundary of the intersecting region as the reservoir boundary, or it can smooth the boundary of the intersecting region and define the smoothed boundary of the intersecting region as the reservoir boundary. The method by which the electronic device smooths the boundary of the intersecting region is the same as the method for smoothing the boundary of the first attribute region, and will not be elaborated here.

[0190] In this embodiment of the application, the required attribute type can be selected according to production needs, the boundary data corresponding to these attribute types can be overlaid and displayed, and the intersecting parts can be statistically extracted to obtain the required reservoir boundary.

[0191] The method provided in this application does not require complex digital image analysis or sophisticated algorithm processing. It can automatically identify complex boundaries by cleverly utilizing the features of planar data. This solves the technical bottleneck of accuracy and efficiency in obtaining complex boundary contours. It is far superior to traditional manual operation methods in both efficiency and accuracy, and plays a very important supporting role in reservoir prediction.

[0192] In recent years, some in the industry have proposed methods for automatic boundary tracking, but these algorithms are based on computer graphics and are very difficult to implement. For example, in two-dimensional digital image recognition, algorithms such as polygon triangulation and interpolation are used to extract boundaries, or a binary image is transformed into a boundary representation, and the topological structure between each boundary is extracted, using complex boundary tracking strategies. However, these methods are complex, inefficient, and cannot meet the accuracy requirements of exploration and production.

[0193] Compared to similar automatic tracing techniques that require complex graphics algorithms, the method provided in this application has a significant advantage in efficiency. Furthermore, by basing its approach on planar data, it ensures the rationality of boundary tracing, achieving a balance between accuracy and efficiency. This method fully integrates with practical applications of reservoir prediction, quickly and accurately locating oil and gas reservoir boundaries. This is of great significance for improving the efficiency of subsequent reservoir development analysis, comprehensive oilfield geological analysis, and well location verification. The method provided in this application is a general boundary tracing method, applicable not only to oil and gas reservoir prediction but also to the automatic tracing of various boundaries, showing considerable application prospects in reservoir prediction, comprehensive oilfield geological analysis, and well location verification.

[0194] To more clearly illustrate the process of determining reservoir boundaries, the following section combines... Figure 9 The above process will be explained. For example... Figure 9 As shown, the electronic device extracts attribute data corresponding to various seismic attributes from seismic data along the stratigraphic plane or through inversion, based on the detailed interpretation results of the stratigraphic plane and faults. For each seismic attribute, the electronic device displays the attribute data corresponding to that seismic attribute using a two-dimensional planar graph, analyzes the distribution characteristics of the attribute data and determines the effective threshold range, and then performs binarization processing on the attribute data according to the effective threshold range. Attribute data that meets the conditions is assigned a value of 1, and attribute data that does not meet the conditions is assigned a value of 0, resulting in binarized data. Then, based on preset boundary values, it traces to obtain closed contour lines, which are then smoothed. The area of ​​the attribute region formed by the closed contour lines is counted, and attribute regions whose area does not meet the requirements are discarded. Finally, as needed, attribute regions of various target seismic attributes are selected for intersection, and the boundary of the intersection region is smoothed. The boundary of the smoothed intersection region is determined as the reservoir boundary.

[0195] This application provides a method for determining reservoir boundaries. The method first acquires attribute data corresponding to multiple seismic attributes, then binarizes the attribute data for each seismic attribute to obtain binarized data. A planar map is then drawn based on the binarized data, and multiple contour points corresponding to preset boundary values ​​are determined on the planar map. The attribute regions of the seismic attributes are determined based on these contour points. Finally, the reservoir boundary is determined based on the attribute regions corresponding to at least one target seismic attribute. For each seismic attribute, since the preset boundary value lies between the first and second values ​​of the binarized data, the attribute region determined by the contour points corresponding to the preset boundary value is the reservoir region possessing that seismic attribute, thus determining the boundary of reservoir regions simultaneously possessing at least one seismic attribute. Therefore, this method can automatically determine reservoir boundaries without manual boundary picking, thereby improving the efficiency of reservoir boundary determination.

[0196] Figure 10 This is a schematic diagram of a reservoir boundary determination device provided in an embodiment of this application. See also... Figure 10 The device includes:

[0197] Module 1001 is used to acquire attribute data corresponding to various seismic attributes within the target work area;

[0198] The first processing module 1002 is used to perform binarization processing on the attribute data corresponding to each earthquake attribute to obtain binarized data; wherein, the binarized data includes a first value and a second value, and the first value is less than the second value;

[0199] The drawing module 1003 is used to draw the first planar map corresponding to the seismic attributes based on the binarized data;

[0200] The second processing module 1004 is used to perform gridding processing on the first plan view to obtain the second plan view;

[0201] The first determining module 1005 is used to determine multiple target grids in the second planar diagram, wherein the target grids are grids that simultaneously include the first value and the second value;

[0202] The second determining module 1006 is used to determine multiple isopleths corresponding to preset boundary values ​​in a second planar diagram based on multiple target grids; wherein the preset boundary values ​​are greater than a first value and less than a second value.

[0203] The third determination module 1007 is used to determine the first attribute region corresponding to the seismic attribute in the second planar map based on multiple isopleth points.

[0204] The fourth determination module 1008 is used to determine at least one target seismic attribute among multiple seismic attributes;

[0205] The fifth determining module 1009 is used to determine the reservoir boundary based on the first attribute region corresponding to at least one target seismic attribute.

[0206] In one possible implementation, the first processing module 1002 is used to draw a third planar map based on the attribute data corresponding to the earthquake attributes. In the third planar map, attribute data of different ranges are distinguished by multiple markers. The multiple markers are used to characterize the distribution characteristics of the attribute data. Based on the distribution characteristics of the attribute data in the third planar map, the attribute data is binarized to obtain binarized data.

[0207] In another possible implementation, there are multiple attribute data; the first processing module 1002 is used to determine the effective threshold range based on the distribution characteristics of the attribute data in the third plane graph; for each attribute data, if the attribute data is within the effective threshold range, the attribute data is modified to a second value; if the attribute data is not within the effective threshold range, the attribute data is modified to a first value.

[0208] In another possible implementation, the second determining module 1006 is used to determine a target edge in each target grid; wherein the binarized data corresponding to the first position point on the target edge is a first value, and the binarized data corresponding to the second position point on the target edge is a second value; determine a first coordinate and a second coordinate; wherein the first coordinate is the planar coordinate of the first position point on the second planar diagram, and the second coordinate is the planar coordinate of the second position point on the second planar diagram; perform linear interpolation based on the first coordinate, the second coordinate, the first value, the second value, and a preset boundary value to determine the coordinates of the isopleth points corresponding to the preset boundary values; and determine the isopleth points corresponding to the preset boundary values ​​on the target edge based on the coordinates of the isopleth points corresponding to the preset boundary values.

[0209] In another possible implementation, the multiple isopoints include multiple first isopoints and multiple second isopoints;

[0210] The third determining module 1007 is used to determine a second attribute region in a second planar diagram based on multiple first contour points; wherein the second attribute region is a non-closed region; to determine a third attribute region in the second attribute region and a fifth attribute region in the fourth attribute region based on multiple second contour points; wherein the third attribute region and the fifth attribute region are both closed regions; the fourth attribute region is an attribute region in the second planar diagram other than the second attribute region; and to combine the third attribute region and the fifth attribute region into a first attribute region.

[0211] In another possible implementation, the third determining module 1007 is used to determine a starting contour point from multiple first contour points based on a first scanning direction; determine a first target grid and a second target grid where the starting contour point is located; wherein the row number of the first target grid is not equal to the row number of the second target grid, or the column number of the first target grid is not equal to the column number of the second target grid; if the column number of the first target grid is less than the column number of the second target grid, then a next first contour point is determined on the top, right, or bottom edge of the second target grid, and the starting contour point and the next first contour point are connected; if the row number of the first target grid is less than the row number of the second target grid, then a next first contour point is determined on the left, top, or right edge of the second target grid, and the starting contour point and the next first contour point are connected; the next first contour point is used as the starting contour point, and the steps of determining the first target grid and the second target grid where the starting contour point is located are executed until the stopping condition is met; the region composed of multiple first contour lines after connecting multiple first contour points is determined as the second attribute region.

[0212] In another possible implementation, the third determining module 1007 is used to connect the second contour points on multiple third target grids in the second attribute region according to the second scanning direction to obtain a second contour line; and to connect the second contour points on multiple fourth target grids in the second attribute region according to the third scanning direction to obtain a third contour line; wherein the third scanning direction is opposite to the second scanning direction, and the terminating contour point of the third contour line is the starting contour point of the second contour line; and the region formed by the third contour lines is determined as the third attribute region.

[0213] In another possible implementation, the number of first attribute regions is multiple; the apparatus also includes:

[0214] The sixth determining module is used to determine the area of ​​multiple first attribute regions;

[0215] The display module is used to delete multiple first attribute regions whose area is smaller than a preset area, and display the remaining first attribute regions and their corresponding areas on the second plan view.

[0216] In another possible implementation, the fifth determining module 1009 is used to, if there are multiple targets for seismic attributes, fuse the first attribute regions corresponding to the at least one target seismic attribute based on the second planar map corresponding to each target seismic attribute; based on the fusion result, determine the intersecting regions between the first attribute regions of the at least one target seismic attribute, and determine the boundary of the intersecting region as the reservoir boundary.

[0217] This application provides a reservoir boundary determination device. The device first acquires attribute data corresponding to multiple seismic attributes, then binarizes the attribute data for each seismic attribute to obtain binarized data. A planar map is then drawn based on the binarized data, and multiple contour points corresponding to preset boundary values ​​are determined on the planar map. The attribute regions of the seismic attributes are determined based on these contour points. Finally, the reservoir boundary is determined based on the attribute regions corresponding to at least one target seismic attribute. For each seismic attribute, since the preset boundary value lies between the first and second values ​​of the binarized data, the attribute region determined by the contour points corresponding to the preset boundary value is the reservoir region possessing that seismic attribute, thus determining the boundary of reservoir regions simultaneously possessing at least one seismic attribute. Therefore, this device can automatically determine reservoir boundaries without manual boundary picking, thereby improving the efficiency of reservoir boundary determination.

[0218] It should be noted that the reservoir boundary determination device provided in the above embodiments is only illustrated by the division of the above functional modules when determining the reservoir boundary. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the electronic device can be divided into different functional modules to complete all or part of the functions described above. In addition, the reservoir boundary determination device and the reservoir boundary determination method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0219] refer to Figure 11 , Figure 11 A structural block diagram of a terminal 1100 provided in an exemplary embodiment of this application is shown. The terminal 1100 may be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 1100 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0220] Typically, terminal 1100 includes a processor 1101 and a memory 1102.

[0221] Processor 1101 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1101 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1101 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1101 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 1101 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0222] The memory 1102 may include one or more computer-readable storage media, which may be non-transitory. The memory 1102 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1102 is used to store at least one piece of program code, which is executed by the processor 1101 to implement the operations performed in the reservoir boundary determination method provided in the method embodiments of this application.

[0223] In some embodiments, the terminal 1100 may also optionally include a peripheral device interface 1103 and at least one peripheral device. The processor 1101, memory 1102, and peripheral device interface 1103 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1103 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 1104, a display screen 1105, a camera assembly 1106, an audio circuit 1107, and a power supply 1108.

[0224] Peripheral device interface 1103 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1101 and memory 1102. In some embodiments, processor 1101, memory 1102 and peripheral device interface 1103 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1101, memory 1102 and peripheral device interface 1103 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0225] The radio frequency (RF) circuit 1104 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1104 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1104 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 1104 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1104 can communicate with other terminals via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1104 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0226] Display screen 1105 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1105 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1101 for processing. In this case, display screen 1105 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 1105, disposed on the front panel of terminal 1100; in other embodiments, there may be at least two display screens, disposed on different surfaces of terminal 1100 or in a folded design; in still other embodiments, display screen 1105 may be a flexible display screen, disposed on a curved or folded surface of terminal 1100. Furthermore, display screen 1105 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 1105 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0227] The camera assembly 1106 is used to acquire images or videos. Optionally, the camera assembly 1106 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1106 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0228] The audio circuit 1107 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1101 for processing, or input to the radio frequency circuit 1104 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the terminal 1100. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1101 or the radio frequency circuit 1104 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1107 may also include a headphone jack.

[0229] Power supply 1108 is used to power the various components in terminal 1100. Power supply 1108 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1108 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0230] In some embodiments, the terminal 1100 further includes one or more sensors 1109. The one or more sensors 1109 include, but are not limited to: an acceleration sensor 1110, a gyroscope sensor 1111, a pressure sensor 1112, an optical sensor 1113, and a proximity sensor 1114.

[0231] Accelerometer 1110 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established with terminal 1100. For example, accelerometer 1110 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 1101 can control display screen 1105 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 1110. Accelerometer 1110 can also be used for collecting motion data from games or users.

[0232] The gyroscope sensor 1111 can detect the orientation and rotation angle of the terminal 1100. The gyroscope sensor 1111 can work in conjunction with the accelerometer sensor 1110 to collect 3D motion data from the user on the terminal 1100. Based on the data collected by the gyroscope sensor 1111, the processor 1101 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0233] The pressure sensor 1112 can be disposed on the side bezel of the terminal 1100 and / or on the lower layer of the display screen 1105. When the pressure sensor 1112 is disposed on the side bezel of the terminal 1100, it can detect the user's grip signal on the terminal 1100, and the processor 1101 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 1112. When the pressure sensor 1112 is disposed on the lower layer of the display screen 1105, the processor 1101 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 1105. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0234] Optical sensor 1113 is used to collect ambient light intensity. In one embodiment, processor 1101 can control the display brightness of display screen 1105 based on the ambient light intensity collected by optical sensor 1113. Specifically, when the ambient light intensity is high, the display brightness of display screen 1105 is increased; when the ambient light intensity is low, the display brightness of display screen 1105 is decreased. In another embodiment, processor 1101 can also dynamically adjust the shooting parameters of camera assembly 1106 based on the ambient light intensity collected by optical sensor 1113.

[0235] The proximity sensor 1114, also known as a distance sensor, is typically located on the front panel of the terminal 1100. The proximity sensor 1114 is used to detect the distance between the user and the front of the terminal 1100. In one embodiment, when the proximity sensor 1114 detects that the distance between the user and the front of the terminal 1100 is gradually decreasing, the processor 1101 controls the display screen 1105 to switch from a screen-on state to a screen-off state; when the proximity sensor 1114 detects that the distance between the user and the front of the terminal 1100 is gradually increasing, the processor 1101 controls the display screen 1105 to switch from a screen-off state to a screen-on state.

[0236] Those skilled in the art will understand that Figure 11 The structure shown does not constitute a limitation on terminal 1100 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0237] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one piece of program code that is loaded and executed by a processor to implement the reservoir boundary determination method in the above embodiments.

[0238] In an exemplary embodiment, a computer program product is also provided, which stores at least one piece of program code, which is loaded and executed by a processor to implement the reservoir boundary determination method in the above embodiments.

[0239] 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.

[0240] The above description is only for the purpose of enabling those skilled in the art to understand the technical solution of this application, and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A reservoir boundary determination method characterized by, The method comprises: acquiring attribute data corresponding to a plurality of seismic attributes in a target work area; for each seismic attribute, performing binaryzation processing on the attribute data corresponding to the seismic attribute to obtain binaryzation data; wherein the binaryzation data comprises a first value and a second value, and the first value is smaller than the second value; based on the binaryzation data, drawing a first plan view corresponding to the seismic attribute; performing grid processing on the first plan view to obtain a second plan view; determining a plurality of target grids in the second plan view, wherein the target grid is a grid that simultaneously comprises the first value and the second value; for each target grid, determining a target edge in the target grid; wherein the binaryzation data corresponding to a first position point on the target edge is the first value, and the binaryzation data corresponding to a second position point on the target edge is the second value; determining a first coordinate and a second coordinate; wherein the first coordinate is the planar coordinate of the first position point on the second plan view, and the second coordinate is the planar coordinate of the second position point on the second plan view; based on the first coordinate, the second coordinate, the first value, the second value, and a preset boundary value, performing linear interpolation to determine the isopoint coordinate corresponding to the preset boundary value; wherein the preset boundary value is greater than the first value and less than the second value; based on the isopoint coordinate corresponding to the preset boundary value, determining the isopoint corresponding to the preset boundary value on the target edge to obtain a plurality of isopoints corresponding to the plurality of target grids; based on the plurality of isopoints, determining a first attribute region corresponding to the seismic attribute in the second plan view; determining at least one target seismic attribute in the plurality of seismic attributes; if the number of the at least one target seismic attribute is a plurality, based on the second plan view corresponding to each target seismic attribute, fusing the first attribute regions corresponding to the at least one target seismic attribute; based on the fusion result, determining an intersection region between the first attribute regions of the at least one target seismic attribute, and determining the boundary of the intersection region as a reservoir boundary.

2. The method of claim 1, wherein, The binaryzation processing on the attribute data corresponding to the seismic attribute to obtain binaryzation data comprises: based on the attribute data corresponding to the seismic attribute, drawing a third plan view, and distinguishing attribute data of different ranges in the third plan view through a plurality of markers; wherein the plurality of markers are used to represent the distribution characteristics of the attribute data; based on the distribution characteristics of the attribute data in the third plan view, performing binaryzation processing on the attribute data to obtain the binaryzation data.

3. The method of claim 2, wherein, The number of the attribute data is a plurality; and the binaryzation processing on the attribute data based on the distribution characteristics of the attribute data in the third plan view to obtain the binaryzation data comprises: based on the distribution characteristics of the attribute data in the third plan view, determining an effective threshold range; for each attribute data, if the attribute data is within the effective threshold range, modifying the attribute data to the second value; If the attribute data is not within the valid threshold range, the attribute data is modified to the first value.

4. The method of claim 1, wherein, The multiple isosurfaces include multiple first isosurfaces and multiple second isosurfaces. The determining, based on the multiple isosurfaces, the first attribute region corresponding to the seismic attribute in the second plan view includes: determining, based on the multiple first isosurfaces, a second attribute region in the second plan view; wherein the second attribute region is a non-closed region; determining, based on the multiple second isosurfaces, a third attribute region in the second attribute region and a fifth attribute region in a fourth attribute region; wherein the third attribute region and the fifth attribute region are both closed regions; and the fourth attribute region is an attribute region in the second plan view other than the second attribute region; the third attribute region and the fifth attribute region constitute the first attribute region.

5. The method of claim 4, wherein, The determining, based on the multiple first isosurfaces, a second attribute region in the second plan view includes: determining, based on a first scanning direction, a starting isosurface from the multiple first isosurfaces; determining a first target grid and a second target grid in which the starting isosurface is located; wherein the row number of the first target grid is not equal to the row number of the second target grid, or the column number of the first target grid is not equal to the column number of the second target grid; if the column number of the first target grid is less than the column number of the second target grid, determining a next first isosurface on the top edge, the right side edge or the bottom edge of the second target grid, and connecting the starting isosurface and the next first isosurface; if the row number of the first target grid is less than the row number of the second target grid, determining a next first isosurface on the left side edge, the top edge or the right side edge of the second target grid, and connecting the starting isosurface and the next first isosurface; taking the next first isosurface as a starting isosurface, performing the step of determining the first target grid and the second target grid in which the starting isosurface is located until a stop condition is met; the region formed by the multiple first isosurfaces after being connected is determined as the second attribute region.

6. The method of claim 4, wherein, The process of determining, based on the multiple second isosurfaces, a third attribute region in the second attribute region includes: connecting, according to a second scanning direction, second isosurfaces on multiple third target grids in the second attribute region to obtain a second isosurface; connecting, according to a third scanning direction, second isosurfaces on multiple fourth target grids in the second attribute region based on the second isosurface to obtain a third isosurface; wherein the third scanning direction is opposite to the second scanning direction, and the termination isosurface of the third isosurface is the starting isosurface of the second isosurface; the region formed by the third isosurface is determined as the third attribute region.

7. The method of claim 4, wherein, The number of the first attribute regions is multiple; the method further includes: determining the areas of the multiple first attribute regions; Delete the first attribute region with an area less than a preset area in the plurality of first attribute regions, and display the remaining first attribute regions and their corresponding areas on the second plan view.

8. A reservoir boundary determination apparatus characterized by comprising: The device comprises: An acquisition module is configured to acquire attribute data corresponding to a plurality of seismic attributes in a target work area; A first processing module is configured to, for each seismic attribute, perform binaryzation processing on the attribute data corresponding to the seismic attribute to obtain binaryzation data; wherein the binaryzation data comprises a first value and a second value, and the first value is less than the second value; A drawing module is configured to draw a first plan view corresponding to the seismic attribute based on the binaryzation data; A second processing module is configured to perform grid processing on the first plan view to obtain a second plan view; A first determining module is configured to determine a plurality of target grids in the second plan view, wherein the target grid is a grid that simultaneously includes the first value and the second value; A second determining module is configured to, for each target grid, determine a target edge in the target grid; wherein the binaryzation data corresponding to a first position point on the target edge is the first value, and the binaryzation data corresponding to a second position point on the target edge is the second value; determine a first coordinate and a second coordinate; wherein the first coordinate is the planar coordinate of the first position point on the second plan view, and the second coordinate is the planar coordinate of the second position point on the second plan view; perform linear interpolation based on the first coordinate, the second coordinate, the first value, the second value, and a preset boundary value to determine the isopoint coordinate corresponding to the preset boundary value; wherein the preset boundary value is greater than the first value and less than the second value; determine the isopoint corresponding to the preset boundary value on the target edge based on the isopoint coordinate corresponding to the preset boundary value to obtain a plurality of isopoints corresponding to the plurality of target grids; A third determining module is configured to determine a first attribute region corresponding to the seismic attribute in the second plan view based on the plurality of isopoints; A fourth determining module is configured to determine at least one target seismic attribute from the plurality of seismic attributes; A fifth determining module is configured to, if the number of the at least one target seismic attribute is a plurality, fuse the first attribute regions corresponding to the at least one target seismic attribute based on the second plan view corresponding to each target seismic attribute; and determine an intersection region between the first attribute regions of the at least one target seismic attribute based on the fusion result, and determine the boundary of the intersection region as a reservoir boundary.

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