Method, device and storage medium for predicting reservoir space distribution
Patent Information
- Application Number
- CN202210752184.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-06-28
- Publication Date
- 2026-10-20
- Estimated Expiration
- 2042-06-28
AI Technical Summary
然而，目前还未有基于地震属性数据和多尺度分类的油藏数据来预测储层空间展布的方法
[0041]本申请实施例提供的储层空间展布的预测方法，通过获取待研究区域内每口井的油藏数据和位置信息以及每个储层单元的位置信息，可以计算得到每个储层单元的油藏数据；为了便于分析储层单元中油藏数据与地震数据的关系，可以对待研究的储层单元的油藏数据进行图形化处理后，得到其对应的每种油藏参数的空间展布示意图，通过将目标油藏参数的空间展布示意图与待研究区域内的目标地震属性数据叠合，就可以确定油藏数据与地震属性数据之间的关系；在确定油藏数据与地震属性数据之间的关系的基础上，以目标地震属性数据为约束，采用空间插值算法，可以得到待研究的储层单元的目标油藏参数的空间展布示意图，即得到了储层空间展布。该方法可以通过地震属性数据和油藏数据，以预测储层空间展布。
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Figure CN117348068B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of comprehensive seismic interpretation technology, and in particular to a method, apparatus and storage medium for predicting reservoir spatial distribution. Background Technology
[0002] With the continuous advancement of seismic exploration technology, its application in oilfield development is becoming increasingly profound. For geophysical researchers, exploring how to establish the relationship between seismic attribute data and multi-scale reservoir data of different types can achieve the goal of accurately predicting reservoirs using seismic attribute data. However, there is currently no method for predicting reservoir spatial distribution based on seismic attribute data and multi-scale classified reservoir data. Summary of the Invention
[0003] In view of this, this application provides a method, apparatus and storage medium for predicting reservoir space distribution, which can predict reservoir space distribution through seismic attribute data and reservoir data.
[0004] Specifically, the following technical solutions are included:
[0005] On one hand, embodiments of this application provide a method for predicting reservoir space distribution, the method comprising:
[0006] Acquire reservoir data and location information for each well in the study area, as well as location information for each reservoir unit. For any well, the reservoir data includes multiple stratigraphic data, and each stratigraphic data corresponds to multiple reservoir parameters, including porosity, permeability, thickness, oil saturation, and well logging interpretation conclusions.
[0007] Based on the reservoir data of each well and the location information of each reservoir unit, the reservoir data of each reservoir unit is obtained;
[0008] For any reservoir unit, based on the location information of each well, the reservoir data of the reservoir unit is graphically processed to obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit.
[0009] The spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit is overlaid with the target seismic attribute data of the area to be studied to determine the relationship between the reservoir data and the seismic attribute data, wherein the target reservoir parameter is one of the multiple reservoir parameters.
[0010] Based on the relationship between the reservoir data and the seismic attribute data, and using the target seismic attribute data as a constraint, a spatial interpolation algorithm is employed to obtain the spatial distribution of the target reservoir parameters of the reservoir unit.
[0011] In some embodiments, the location information of the reservoir unit includes top depth and bottom depth, and the logging interpretation conclusion includes reservoir and non-reservoir, wherein the reservoir includes oil layer, water layer, poor oil layer, oil-water co-layer, and gas layer, and the non-reservoir includes dry layer, tight layer and mudstone layer;
[0012] The process of obtaining reservoir data for each reservoir unit based on the reservoir data of each well and the location information of each reservoir unit includes:
[0013] For any reservoir unit in any well, the thickness of the reservoir unit is obtained based on the top and bottom depths of the reservoir unit.
[0014] Based on the thickness of the reservoir unit and the reservoir data of the well, the porosity, permeability and oil saturation of the reservoir unit are obtained;
[0015] The reservoir thickness of the reservoir unit is obtained based on the thickness of the reservoir unit and the layer data contained in the reservoir unit.
[0016] In some embodiments, obtaining the porosity, permeability, and oil saturation of the reservoir unit based on its thickness and reservoir data includes:
[0017] Based on the thickness of the reservoir unit and the layer data contained in the reservoir unit, the porosity, permeability and oil saturation of each reservoir in the reservoir unit are obtained.
[0018] Based on the porosity, permeability, and oil saturation of each reservoir in the reservoir unit, the porosity, permeability, and oil saturation of the reservoir unit are obtained using a weighted average method.
[0019] In some embodiments, obtaining the reservoir thickness of the reservoir unit based on the thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit includes:
[0020] Based on the thickness of the reservoir unit and the layer data contained in the reservoir unit, the thickness of each reservoir in the reservoir unit is obtained.
[0021] The reservoir thickness of the reservoir unit is obtained by summing the thicknesses of each reservoir in the reservoir unit.
[0022] In some embodiments, for any reservoir unit, based on the location information of each well, the process of graphically processing the reservoir data of the reservoir unit to obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit includes:
[0023] Normalize each reservoir parameter of the reservoir unit to obtain the normalized reservoir parameters of the reservoir unit;
[0024] By substituting the normalized reservoir parameters of the reservoir unit and the location information of each well into the plotting and display function, a spatial distribution diagram of each reservoir parameter of the reservoir unit is obtained.
[0025] In some embodiments, before overlaying the spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit with the target seismic attribute data of the area to be studied, the method further includes:
[0026] Obtain target seismic attribute data within the area to be studied, wherein the target seismic attribute data within the area to be studied is target seismic attribute data at the same depth as the reservoir unit.
[0027] In some embodiments, obtaining the target seismic attribute data within the area to be studied includes:
[0028] In response to the reservoir unit having a thickness greater than or equal to 1 / 4 of the seismic wave wavelength, a time window is determined according to the thickness of the reservoir unit, and target seismic attribute data of the area under study are extracted.
[0029] In response to the fact that the thickness of the reservoir unit is less than 1 / 4 of the seismic wave wavelength, a time window is determined according to 1 / 4 of the seismic wave wavelength, and the target seismic attribute data of the area to be studied are extracted.
[0030] In some embodiments, overlaying the spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit with the target seismic attribute data of the area to be studied to determine the relationship between the reservoir data and the seismic attribute data includes:
[0031] The spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit is overlaid with the target seismic attribute data of the area to be studied to obtain the overlaid diagram.
[0032] Based on the superimposed schematic diagram, target seismic attribute data of the well point location of each well are extracted;
[0033] Based on the target seismic attribute data and the corresponding values of target reservoir parameters at the location of each well, a functional relationship between the target seismic attribute data and the target reservoir parameters is obtained by fitting.
[0034] On the other hand, embodiments of this application provide a reservoir space distribution prediction device, the device comprising:
[0035] The first acquisition module is used to acquire reservoir data and location information of each well in the area to be studied, as well as location information of each reservoir unit. For any well, the reservoir data includes multiple layer data, and each layer data corresponds to multiple reservoir parameters, including porosity, permeability, thickness, oil saturation, and well logging interpretation conclusions.
[0036] The processing module is used to obtain reservoir data for each reservoir unit based on the reservoir data of each well and the location information of each reservoir unit;
[0037] The graphical module is used to graphically process the reservoir data of any reservoir unit based on the location information of each well, and obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit.
[0038] The relationship determination module is used to overlay the spatial distribution diagram of the target reservoir parameter corresponding to the reservoir unit with the target seismic attribute data of the area to be studied to determine the relationship between the reservoir data and the seismic attribute data, wherein the target reservoir parameter is one of the multiple reservoir parameters.
[0039] The spatial distribution module is used to obtain a spatial distribution diagram of the target reservoir parameters of the reservoir unit based on the relationship between the reservoir data and the seismic attribute data, with the target seismic attribute data as a constraint, and using a spatial interpolation algorithm.
[0040] In another aspect, embodiments of this application provide a storage medium that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform a reservoir space distribution prediction method as described in any of the preceding aspects.
[0041] The reservoir spatial distribution prediction method provided in this application obtains reservoir data and location information of each well and location information of each reservoir unit within the study area. This allows for the calculation of reservoir data for each reservoir unit. To facilitate analysis of the relationship between reservoir data and seismic data within a reservoir unit, the reservoir data of the studied unit can be graphically processed to obtain a spatial distribution diagram of each reservoir parameter. By overlaying the spatial distribution diagram of the target reservoir parameters with the target seismic attribute data within the study area, the relationship between the reservoir data and the seismic attribute data can be determined. Based on this relationship, and using the target seismic attribute data as a constraint, a spatial interpolation algorithm is employed to obtain a spatial distribution diagram of the target reservoir parameters for the studied reservoir unit, thus yielding the reservoir spatial distribution. This method can predict reservoir spatial distribution using both seismic attribute data and reservoir data. Attached Figure Description
[0042] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0043] Figure 1 A flowchart illustrating a method for predicting reservoir space distribution as provided in this application embodiment;
[0044] Figure 2 A flowchart of another method for predicting reservoir space distribution provided in an embodiment of this application;
[0045] Figure 3 A flowchart illustrating a method for predicting reservoir spatial distribution provided in this application, which obtains reservoir data for each reservoir unit based on reservoir data from each well and location information of each reservoir unit;
[0046] Figure 4 In a method for predicting reservoir spatial distribution provided in this application embodiment, for any reservoir unit, based on the location information of each well, the reservoir data of the reservoir unit is graphically processed to obtain a flowchart of a schematic diagram of the spatial distribution of each reservoir parameter of the reservoir unit.
[0047] Figure 5 A graphical schematic diagram of the porosity of a reservoir unit within a study area provided in this application embodiment;
[0048] Figure 6 A graphical schematic diagram of the reservoir thickness of a reservoir unit in the area to be studied, provided for an embodiment of this application;
[0049] Figure 7 The flowchart of a method for predicting reservoir spatial distribution provided in this application is as follows: a schematic diagram of the spatial distribution of target reservoir parameters corresponding to a reservoir unit is overlaid with target seismic attribute data within the area to be studied to determine the relationship between reservoir data and seismic attribute data.
[0050] Figure 8 A schematic diagram showing the superposition of the root mean square amplitude attribute and porosity of a reservoir unit in the area under study, provided for an embodiment of this application;
[0051] Figure 9 A schematic diagram showing the superposition of the root mean square amplitude attribute of a reservoir unit and the reservoir thickness within the area to be studied, provided for an embodiment of this application;
[0052] Figure 10A graph showing the relationship between reservoir thickness and root mean square amplitude properties of a reservoir unit provided in an embodiment of this application;
[0053] Figure 11 A schematic diagram of the spatial distribution of reservoir thickness in a reservoir unit within a study area provided in this application embodiment;
[0054] Figure 12 This is a schematic diagram of the structure of a reservoir space distribution prediction device provided in an embodiment of this application. Detailed Implementation
[0055] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0056] With the application of seismic data from exploration to development, classifying and graphically representing large amounts of reservoir data, and establishing the relationship between seismic attributes and reservoir data, is an important task in reservoir geophysics research. To predict reservoir spatial distribution based on seismic attribute data and reservoir data, this application provides a method for predicting reservoir spatial distribution.
[0057] Figure 1 A flowchart illustrating a method for predicting reservoir space distribution as provided in an embodiment of this application. See also... Figure 1 This method is applied in electronic devices, and the method includes:
[0058] 101. Obtain reservoir data and location information for each well in the study area, as well as location information for each reservoir unit. For any well, the reservoir data includes multiple stratigraphic data, and each stratigraphic data corresponds to multiple reservoir parameters, including porosity, permeability, thickness, oil saturation, and logging interpretation conclusions.
[0059] 102. Based on the reservoir data of each well and the location information of each reservoir unit, the reservoir data of each reservoir unit is obtained.
[0060] 103. For any reservoir unit, based on the location information of each well, the reservoir data of the reservoir unit is graphically processed to obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit.
[0061] 104. Overlay the spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit with the target seismic attribute data of the area to be studied to determine the relationship between the reservoir data and the seismic attribute data, wherein the target reservoir parameter is one of the multiple reservoir parameters.
[0062] 105. Based on the relationship between reservoir data and seismic attribute data, and with the target seismic attribute data as a constraint, a spatial interpolation algorithm is used to obtain a schematic diagram of the spatial distribution of the target reservoir parameters of the reservoir unit.
[0063] The reservoir spatial distribution prediction method provided in this application obtains reservoir data and location information of each well and location information of each reservoir unit within the study area. This allows for the calculation of reservoir data for each reservoir unit. To facilitate analysis of the relationship between reservoir data and seismic data within a reservoir unit, the reservoir data of the studied unit can be graphically processed to obtain a spatial distribution diagram of each reservoir parameter. By overlaying the spatial distribution diagram of the target reservoir parameters with the target seismic attribute data within the study area, the relationship between the reservoir data and the seismic attribute data can be determined. Based on this relationship, and using the target seismic attribute data as a constraint, a spatial interpolation algorithm is employed to obtain a spatial distribution diagram of the target reservoir parameters for the studied reservoir unit, thus yielding the reservoir spatial distribution. This method can predict reservoir spatial distribution using both seismic attribute data and reservoir data.
[0064] In some embodiments, the location information of a reservoir unit includes top depth and bottom depth, and the logging interpretation conclusion includes reservoir and non-reservoir, wherein the reservoir includes oil layer, water layer, poor oil layer, gas layer and oil and gas co-layer, and the non-reservoir includes dry layer, tight layer and mudstone layer;
[0065] Based on the reservoir data of each well and the location information of each reservoir unit, the reservoir data for each reservoir unit includes:
[0066] For any reservoir unit in any well, the thickness of the reservoir unit is obtained based on the top and bottom depths of the reservoir unit.
[0067] Based on the thickness of the reservoir unit and the reservoir data of the well, the porosity, permeability and oil saturation of the reservoir unit are obtained;
[0068] The reservoir thickness of the reservoir unit is obtained based on the thickness of the reservoir unit and the stratigraphic data contained within the reservoir unit.
[0069] In some embodiments, obtaining the porosity, permeability, and oil saturation of a reservoir unit based on its thickness and reservoir data includes:
[0070] Based on the thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit, the porosity, permeability and oil saturation of each reservoir in the reservoir unit are obtained.
[0071] Based on the porosity, permeability, and oil saturation of each reservoir in a reservoir unit, the porosity, permeability, and oil saturation of the reservoir unit are obtained using a weighted average method.
[0072] In some embodiments, the reservoir thickness of a reservoir unit is obtained based on the thickness of the reservoir unit and the stratigraphic data contained within the reservoir unit, including:
[0073] Based on the thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit, the thickness of each reservoir in the reservoir unit is obtained.
[0074] The reservoir thickness of a reservoir unit is obtained by summing the thicknesses of each reservoir in the unit.
[0075] In some embodiments, for any reservoir unit, based on the location information of each well, the reservoir data of the reservoir unit is graphically processed to obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit, including:
[0076] Normalize each reservoir parameter of the reservoir unit to obtain the normalized reservoir parameters of the reservoir unit;
[0077] By substituting the normalized reservoir parameters of the reservoir unit and the location information of each well into the plotting and display function, a spatial distribution diagram of each reservoir parameter of the reservoir unit is obtained.
[0078] In some embodiments, before overlaying the spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit with the target seismic attribute data of the area to be studied, the method further includes:
[0079] Obtain target seismic attribute data within the study area, wherein the target seismic attribute data within the study area are target seismic attribute data at the same depth as the reservoir unit.
[0080] In some embodiments, acquiring target seismic attribute data within the area to be studied includes:
[0081] In response to the reservoir unit thickness being greater than or equal to 1 / 4 of the seismic wave wavelength, a time window is determined according to the reservoir unit thickness, and target seismic attribute data for the area under study are extracted.
[0082] In response to the fact that the thickness of the reservoir unit is less than 1 / 4 of the seismic wave wavelength, the time window is determined according to 1 / 4 of the seismic wave wavelength, and the target seismic attribute data of the area to be studied are extracted.
[0083] In some embodiments, overlaying a spatial distribution diagram of the target reservoir parameters corresponding to a reservoir unit with target seismic attribute data within the area to be studied to determine the relationship between the reservoir data and the seismic attribute data includes:
[0084] The spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit is overlaid with the target seismic attribute data of the area to be studied to obtain the overlaid diagram.
[0085] Based on the overlaid schematic diagram, target seismic attribute data of the well point location of each well are extracted;
[0086] Based on the target seismic attribute data and the corresponding values of target reservoir parameters at the location of each well, a functional relationship between the target seismic attribute data and the target reservoir parameters is obtained by fitting.
[0087] Figure 2 A flowchart illustrating another method for predicting reservoir space distribution provided in this application embodiment. See also... Figure 2 This method is applied in electronic devices, and the method includes:
[0088] 201. Obtain reservoir data and location information for each well in the area to be studied, as well as location information for each reservoir unit.
[0089] There are multiple wells in the area to be studied. By obtaining logging and well logging data from each well, we can obtain reservoir data and location information for each well.
[0090] A reservoir unit is a reservoir region within a certain depth, and the determination of its location information is the responsibility of the staff.
[0091] For any given well, reservoir data includes data from multiple stratigraphic layers, each with corresponding reservoir parameters, including porosity, permeability, thickness, oil saturation, and logging interpretations. The location information for each reservoir unit includes top and bottom depths. The logging interpretations include both reservoir and non-reservoir data.
[0092] Based on well logging interpretation results, reservoirs can be classified first. These results can then be used to categorize reservoir types. In this application, reservoir types can be divided into reservoirs and non-reservoirs. Specifically, well logging interpretation results indicating oil layers, water layers, poor-oil layers, gas layers, and oil-gas co-existing layers are classified as reservoirs, while dry layers, tight layers, and mudstone layers are classified as non-reservoirs. Alternatively, reservoir types can also be classified as permeable reservoirs and non-permeable reservoirs; no specific limitation is made here.
[0093] The following table uses an area with 23 wells as an example. The reservoir data for these wells is shown in Table 1, the location information of the reservoir units is shown in Table 2, the location information of the wells is shown in Table 3, and the reservoir type classification is shown in Table 4. It should be noted that the following lists are merely illustrative and do not include information on all wells within the area under study.
[0094] Table 1. Reservoir data of wells in the area to be studied.
[0095]
[0096]
[0097] Table 2 Location Information of Reservoir Units
[0098]
[0099]
[0100] Table 3. Well Location Information
[0101] g1 368181.0938 4869974 228.8 g5 366543.1875 4865051 231.8 g6 368214 4866342 224 g8 371445.1875 4878038 220 he1 370926.4063 4861958 224.75 he11 373187 4863209 224.8 he12 373901 4862409 224.8 he18 379718 4865819 215 he18-18 373250.8 4863281 217.15 he19 372924 4864493 226 he20 370966 4864834 246 he20-20 373253.1 4863276 217.15 he21 370214 4866259 221 he22-18 373279.7 4863230 217.1 he23 369428 4869124 244.9 he24 372002 4868239 235 he25 374135.7 4866170 231 he27 375352.9 4864717 235.5 he28 372916.2 4870599 240.5 he3 368286.0938 4857408 232.5 he32 370343.2 4868956 240.5 he8 371299.0938 4865705 244.8 he8-1 372125.333 4868158 217.64
[0102] Table 4 Reservoir Type Classification Table
[0103] g1 614.1 616.1 2 25.5 88.2 0 reservoir g1 626.9 633.8 6.9 16.7 18.1 0 reservoir g1 663.1 667.8 4.7 21.8 45.5 1 reservoir g1 670.4 672.9 2.5 20.3 30.6 1 reservoir g1 683.6 686.8 3.2 14.3 8.6 2 reservoir g1 697.1 699.6 2.5 13.6 9.1 2 reservoir g1 720.6 724.3 3.7 8.1 2.9 0 reservoir g1 725.6 728.5 2.9 15.3 12.5 0 reservoir g1 815.6 818.6 3 20.3 104.5 0 reservoir g1 820.3 822.4 2.1 15.5 9.7 0 reservoir g1 840.4 843.4 3 21.6 36.1 0 reservoir g1 845.1 847.8 2.7 20.1 36.7 0 reservoir g1 913.5 915.1 1.6 20.3 27.7 0 reservoir g1 917.3 920.5 3.2 12.8 13.1 0 non-reservoir g1 973.1 977.1 4 16.2 19.1 0 non-reservoir g1 993 999.3 6.3 11.5 4.3 0 reservoir g1 1015.9 1018.4 2.5 15 19.7 0 reservoir g1 1050.1 1053.6 3.5 14.9 9.5 0 reservoir g1 1480.8 1487 6.2 9.8 1.9 0 non-reservoir g1 1491 1493.5 2.5 12.3 7.7 0 non-reservoir g1 1582.8 1587.5 4.7 11.5 2.7 0 non-reservoir g1 1613.1 1660.4 47.3 9.8 2 0 non-reservoir g1 1663.4 1734.9 71.5 8 0.8 0 non-reservoir g1 1735.5 1739.9 4.4 11 3 80 reservoir g1 1740.8 1742.5 1.7 7.4 0.4 80 reservoir g1 1745.1 1749.9 4.8 3.2 0.3 0 non-reservoir g1 1750.6 1751.6 1 4 0.2 0 non-reservoir g1 1753.6 1754.6 1 5.7 0.1 0 non-reservoir g1 1755 1757.1 2.1 2.8 0.1 76 reservoir ... ... ... ... ... ... … ...
[0104] 202. Based on the reservoir data of each well and the location information of each reservoir unit, the reservoir data of each reservoir unit is obtained.
[0105] By using reservoir data from each well and location information for each reservoir unit, the reservoir data for each reservoir unit can be determined, preparing data for subsequent integration with seismic attribute data structures.
[0106] See Figure 3 This step specifically includes:
[0107] In 2021, for any reservoir unit in any well, the thickness of the reservoir unit is obtained based on the top and bottom depths of the reservoir unit.
[0108] Understandably, the thickness of a reservoir unit can be obtained by subtracting the top depth from the bottom depth of the reservoir unit.
[0109] In 2022, based on the thickness of the reservoir unit and the reservoir data of the well, the porosity, permeability and oil saturation of the reservoir unit were obtained.
[0110] Specifically, based on the thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit, the porosity, permeability and oil saturation of each reservoir in the reservoir unit are obtained; based on the porosity, permeability and oil saturation of each reservoir in the reservoir unit, the porosity, permeability and oil saturation of the reservoir unit are obtained by weighted average method.
[0111] Since a reservoir unit can include multiple reservoirs, the porosity, permeability, and oil saturation of a reservoir unit cannot be based on the data of a single reservoir. Instead, it is more accurate to calculate the porosity, permeability, and oil saturation of the multiple reservoirs contained in the reservoir unit. Therefore, the weighted average method is used here to calculate the porosity, permeability, and oil saturation of the reservoir unit.
[0112] The formula for calculating the weighted average method is as follows:
[0113]
[0114] In the formula: This represents the average value, x. i This represents the reservoir attribute value, h. i This indicates the thickness of the reservoir.
[0115] In 2023, the reservoir thickness of the reservoir unit was obtained based on the thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit.
[0116] Specifically, based on the thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit, the thickness of each reservoir in the reservoir unit is obtained; the thicknesses of each reservoir in the reservoir unit are summed to obtain the reservoir thickness of the reservoir unit.
[0117] In this application, reservoir units have multiple different levels. The reservoir unit levels in the area under study cover large-scale oil group levels, sandstone groups, and small-scale sublayers or single sand bodies.
[0118] For example, the reservoir data for reservoir units jfIV-II-1 and jfIV-II-1 can be shown in Table 5 below.
[0119] Table 5 Reservoir data for reservoir units jfIV-II-1 and jfIV-II-1
[0120]
[0121]
[0122] 203. For any reservoir unit, based on the location information of each well, the reservoir data of the reservoir unit is graphically processed to obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit.
[0123] By graphically processing the reservoir data of the reservoir unit, operators can more intuitively determine the specific value of the reservoir data corresponding to the location of each well.
[0124] See Figure 4 This step specifically includes:
[0125] In 2031, the reservoir parameters of each reservoir unit were normalized to obtain the normalized reservoir parameters of the reservoir unit.
[0126] Based on the location information of each well and reservoir data, the reservoir parameters of each reservoir unit are normalized to obtain the normalized reservoir parameters.
[0127] Taking porosity as an example, the normalized calculation formula can be:
[0128]
[0129] In the formula, z is the normalized porosity value, POR is the porosity value of the current reservoir unit, and POR max The maximum porosity value in this well is taken as POR. min The minimum porosity value in the well is set to `maxheight`, which is half the maximum height of the drawing canvas to be displayed.
[0130] In 2032, the normalized reservoir parameters of the reservoir unit and the location information of each well are substituted into the plotting and display function to obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit.
[0131] By substituting the normalized reservoir parameters of the reservoir unit and the location information of each well into the plotting display function of the existing software, a spatial distribution diagram of each reservoir parameter of the reservoir unit can be directly obtained.
[0132] In one possible implementation, the `patches` module object from the `maltplotlib` plotting library in Python is used to draw a histogram corresponding to the attribute values of each well location on the planar plot, visually displaying the magnitude of reservoir parameters. The function `patches.Rectangle(xy,width,height,angle=0.0,linewidth=1,edgecolor='r',facecolor='r')` is called to draw multiple rectangles, where: `xy` is the horizontal and vertical coordinates (x,y) of each well's planar location, `width` is the width of each rectangle, `height` is the normalized attribute value `z`, `angle` is the angle of the drawn rectangle, `linewidth` is the rectangle's line width, `edgecolor` is the rectangle's line color, and `facecolor` is the rectangle's fill color. Building upon this, the `Axes.text(x,y+height,s,ha='center',size=12)` function from the maltplotlib plotting library is called to directly display the original attribute values above the rectangular plot. This allows for a more intuitive display of attribute values through the length of the bars and the size of the numbers. In this function, `x` is the x-coordinate of the current well, `y+height` is the y-coordinate of the current well plus the normalized attribute value `height`, `s` is the original attribute value, `ha='center'` is the centering parameter, and `size` is the font size for displaying the attribute values. Similarly, this allows for a very intuitive visualization of the bar charts corresponding to each attribute value for each well, along with the attribute value sizes and their planar distribution on the canvas.
[0133] Figure 5 This is a graphical schematic diagram of the porosity of a reservoir unit within a study area, provided as an embodiment of this application. See also... Figure 5 The distribution of porosity in the jfIV-II-2 reservoir unit can be clearly identified.
[0134] Figure 6 This is a graphical schematic diagram illustrating the reservoir thickness of a reservoir unit within a study area, provided as an embodiment of this application. See also... Figure 6 The distribution of reservoir thickness in the jfIV-II-2 reservoir unit can be clearly identified.
[0135] 204. Obtain target seismic attribute data within the area to be studied.
[0136] By acquiring target seismic attribute data within the area to be studied, data preparation is made for combining reservoir data with seismic attribute data to predict reservoir spatial distribution.
[0137] Among them, the target seismic attribute data in the area to be studied are the target seismic attribute data at the same depth as the reservoir unit.
[0138] There are two possible ways to obtain target seismic attribute data for the area under study:
[0139] In one possible implementation, in response to the reservoir element thickness being greater than or equal to 1 / 4 of the seismic wave wavelength, a time window is determined according to the reservoir element thickness, and target seismic attribute data for the area under study are extracted.
[0140] In another possible implementation, in response to the reservoir element thickness being less than 1 / 4 of the seismic wave wavelength, a time window is determined according to 1 / 4 of the seismic wave wavelength, and target seismic attribute data of the area under study are extracted.
[0141] The time window here can be understood as the time corresponding to the wavelength of the seismic wave when the synthetic record is made.
[0142] 205. Overlay the spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit with the target seismic attribute data of the area to be studied to determine the relationship between the reservoir data and the seismic attribute data.
[0143] The target reservoir parameter is one of multiple reservoir parameters.
[0144] See Figure 7 This step includes:
[0145] 2051. The spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit is overlaid with the target seismic attribute data of the area to be studied to obtain the overlaid diagram.
[0146] Figure 8 This is a schematic diagram illustrating the superposition of the root-mean-square amplitude attribute and porosity of a reservoir unit within the study area, provided as an embodiment of this application. See also... Figure 8 The figure illustrates the superposition of the root mean square amplitude attribute and porosity of the jf-II-2 reservoir unit.
[0147] Figure 9 This is a schematic diagram illustrating the superposition of the root-mean-square amplitude attribute of a reservoir unit within the study area and the reservoir thickness, provided as an embodiment of this application. See also... Figure 9 The figure illustrates the superposition of the root mean square amplitude attribute and reservoir thickness of the jfIV-II-2 reservoir unit.
[0148] 2052. Based on the overlaid schematic diagram, the target seismic attribute data of the well point location of each well is extracted.
[0149] To facilitate the study of the relationship between seismic attribute data and reservoir data, further quantitative research can be conducted by extracting the target seismic attribute data of the well point location for each well.
[0150] 2053. Based on the target seismic attribute data and the corresponding values of target reservoir parameters at the location of each well point, a functional relationship between the target seismic attribute data and the target reservoir parameters is fitted.
[0151] After obtaining the target seismic attribute data and the corresponding values of the target reservoir parameters at the location of each well point, the functional relationship between the target seismic attribute data and the target reservoir parameters can be obtained through methods such as curve fitting. This yields the quantitative relationship between the target seismic attribute data and the target reservoir parameters, i.e., the relationship between reservoir data and seismic attribute data.
[0152] Figure 10 This diagram illustrates the relationship between reservoir thickness and root-mean-square amplitude properties of a reservoir unit, as provided in an embodiment of this application. The diagram shows that a functional relationship can be fitted between the reservoir thickness and root-mean-square amplitude properties of this reservoir unit: y = 0.0035x - 6.759.
[0153] 206. Based on the relationship between reservoir data and seismic attribute data, and with the target seismic attribute data as a constraint, a spatial interpolation algorithm is used to obtain a schematic diagram of the spatial distribution of the target reservoir parameters of the reservoir unit.
[0154] Among them, the spatial interpolation algorithm can be the Kriging interpolation algorithm.
[0155] Once the relationship between reservoir data and seismic attribute data is obtained, a spatial distribution map of the target reservoir parameters can be constructed by using spatial interpolation algorithms with the target seismic attribute data as a constraint. This predicts the distribution of reservoir space, allowing operators to intuitively determine the distribution of reservoir space.
[0156] Figure 11 This is a schematic diagram illustrating the spatial distribution of reservoir thickness within a reservoir unit in the study area, provided as an embodiment of this application. See also... Figure 11 The figure illustrates the reservoir prediction for the jfIV-II-2 reservoir unit. The distribution of reservoir thickness within the unit can be observed through the contour-like layout of the figure.
[0157] The reservoir spatial distribution prediction method provided in this application obtains reservoir data and location information of each well and location information of each reservoir unit within the study area. This allows for the calculation of reservoir data for each reservoir unit. To facilitate analysis of the relationship between reservoir data and seismic data within a reservoir unit, the reservoir data of the studied unit can be graphically processed to obtain a spatial distribution diagram of each reservoir parameter. By overlaying the spatial distribution diagram of the target reservoir parameters with the target seismic attribute data within the study area, the relationship between the reservoir data and the seismic attribute data can be determined. Based on this relationship, and using the target seismic attribute data as a constraint, a spatial interpolation algorithm is employed to obtain a spatial distribution diagram of the target reservoir parameters for the studied reservoir unit, thus yielding the reservoir spatial distribution. This method can predict reservoir spatial distribution using both seismic attribute data and reservoir data.
[0158] Figure 12 A schematic diagram of a reservoir space distribution prediction device provided in an embodiment of this application. See also... Figure 12 The device includes:
[0159] The first acquisition module 1201 is used to acquire reservoir data and location information of each well in the area to be studied, as well as location information of each reservoir unit. For any well, the reservoir data includes multiple layer data, and each layer data corresponds to multiple reservoir parameters, including porosity, permeability, thickness, oil saturation and logging interpretation conclusions.
[0160] The processing module 1202 is used to obtain reservoir data for each reservoir unit based on the reservoir data of each well and the location information of each reservoir unit;
[0161] The graphical module 1203 is used to graphically process the reservoir data of any reservoir unit based on the location information of each well, and obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit.
[0162] The relationship determination module 1204 is used to overlay the spatial distribution diagram of the target reservoir parameter corresponding to the reservoir unit with the target seismic attribute data in the area to be studied to determine the relationship between the reservoir data and the seismic attribute data, wherein the target reservoir parameter is one of multiple reservoir parameters.
[0163] The spatial distribution module 1205 is used to obtain a spatial distribution diagram of the target reservoir parameters of the reservoir unit based on the relationship between reservoir data and seismic attribute data, with the target seismic attribute data as a constraint, and using a spatial interpolation algorithm.
[0164] In some embodiments, the location information of the reservoir unit includes top depth and bottom depth, and the reservoir type includes oil layer and water layer. The processing module 1202 includes:
[0165] The first thickness determination unit is used to determine the thickness of any reservoir unit for any well based on the top and bottom depths of the reservoir unit.
[0166] The calculation unit is used to obtain the porosity, permeability and oil saturation of the reservoir unit based on the thickness of the reservoir unit and the reservoir data of the well.
[0167] The second thickness determination unit is used to obtain the reservoir thickness of the reservoir unit based on the thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit.
[0168] In some embodiments, the computing unit includes:
[0169] The first acquisition subunit is used to obtain the porosity, permeability and oil saturation of each reservoir in the reservoir unit based on the thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit.
[0170] The weighted calculation sub-unit is used to obtain the porosity, permeability, and oil saturation of the reservoir unit based on the porosity, permeability, and oil saturation of each reservoir in the reservoir unit using the weighted average method.
[0171] In some embodiments, the second thickness determining unit includes:
[0172] The second acquisition subunit is used to obtain the thickness of each reservoir in the reservoir unit based on the thickness of the reservoir unit and the layer data contained in the reservoir unit.
[0173] The thickness determination sub-unit is used to sum the thicknesses of each reservoir in the reservoir unit to obtain the reservoir thickness of the reservoir unit.
[0174] In some embodiments, the graphical module 1203 includes:
[0175] The homogenization processing unit is used to normalize each reservoir parameter of the reservoir unit to obtain the normalized reservoir parameters of the reservoir unit.
[0176] The display unit is used to input the normalized reservoir parameters of the reservoir unit and the location information of each well into the drawing display function to obtain a spatial distribution diagram of each reservoir parameter of the reservoir unit.
[0177] In some embodiments, prior to the relationship determination module 1204, the apparatus further includes:
[0178] The second acquisition module is used to acquire target seismic attribute data within the area to be studied, wherein the target seismic attribute data within the area to be studied is target seismic attribute data at the same depth as the reservoir unit.
[0179] In some embodiments, the second acquisition module includes:
[0180] The first data extraction unit is used to determine the time window according to the thickness of the reservoir unit in response to the thickness of the reservoir unit being greater than or equal to 1 / 4 of the seismic wave wavelength, and to extract the target seismic attribute data of the area to be studied.
[0181] The second data extraction unit is used to determine the time window according to 1 / 4 of the seismic wave wavelength in response to the fact that the thickness of the reservoir unit is less than 1 / 4 of the seismic wave wavelength, and to extract the target seismic attribute data of the area to be studied.
[0182] In some embodiments, the relationship determination module 1204 includes:
[0183] The superimposed unit is used to superimpose the spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit with the target seismic attribute data of the area to be studied to obtain the superimposed diagram.
[0184] The extraction unit is used to extract the target seismic attribute data of the well point location of each well based on the overlaid schematic diagram;
[0185] The fitting unit is used to fit the target seismic attribute data and the corresponding values of the target reservoir parameters based on the location of the well point of each well to obtain the functional relationship between the target seismic attribute data and the target reservoir parameters.
[0186] It should be noted that the reservoir space distribution prediction device provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the reservoir space distribution prediction device and the reservoir space distribution prediction method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.
[0187] The reservoir spatial distribution prediction device provided in this application can calculate the reservoir data of each reservoir unit by acquiring the reservoir data and location information of each well in the area to be studied, as well as the location information of each reservoir unit. In order to facilitate the analysis of the relationship between reservoir data and seismic data in the reservoir unit, the reservoir data of the reservoir unit to be studied can be graphically processed to obtain a spatial distribution diagram of each reservoir parameter. By overlaying the spatial distribution diagram of the target reservoir parameter with the target seismic attribute data in the area to be studied, the relationship between the reservoir data and the seismic attribute data can be determined. Based on the determination of the relationship between the reservoir data and the seismic attribute data, and with the target seismic attribute data as a constraint, a spatial interpolation algorithm can be used to obtain a spatial distribution diagram of the target reservoir parameter of the reservoir unit to be studied, thus obtaining the reservoir spatial distribution.
[0188] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including program code that can be executed by a processor in an electronic device to perform the reservoir space distribution prediction method in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.
[0189] 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.
[0190] The above are merely optional embodiments of this application and are 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 method for predicting reservoir spatial distribution, characterized in that, The method includes: Acquire reservoir data and location information for each well in the study area, as well as the top and bottom depths of each reservoir unit. For any well, the reservoir data includes multiple stratigraphic data, and each stratigraphic data corresponds to multiple reservoir parameters, including porosity, permeability, thickness, oil saturation, and logging interpretation conclusions. Based on the reservoir data of each well and the top and bottom depths of each reservoir unit, the reservoir data of each reservoir unit is obtained. For any reservoir unit, based on the location information of each well, the reservoir data of the reservoir unit is graphically processed to obtain a preliminary spatial distribution diagram of each reservoir parameter of the reservoir unit. Acquire target seismic attribute data within the area to be studied, wherein the target seismic attribute data within the area to be studied is target seismic attribute data at the same depth as the reservoir unit; The acquisition of target seismic attribute data within the study area includes: in response to the reservoir unit thickness being greater than or equal to 1 / 4 of the seismic wave wavelength, determining a time window according to the reservoir unit thickness and extracting the target seismic attribute data within the study area; in response to the reservoir unit thickness being less than 1 / 4 of the seismic wave wavelength, determining a time window according to 1 / 4 of the seismic wave wavelength and extracting the target seismic attribute data within the study area. The preliminary spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit is overlaid with the target seismic attribute data of the area to be studied to obtain the overlaid diagram, wherein the target reservoir parameter is one of the multiple reservoir parameters; Based on the superimposed schematic diagram, target seismic attribute data of the well point location of each well are extracted; Based on the target seismic attribute data and the corresponding values of target reservoir parameters at the location of each well point, a functional relationship between the target seismic attribute data and the target reservoir parameters is obtained by fitting. Based on the functional relationship between the target seismic attribute data and the target reservoir parameters, and using the target seismic attribute data as a constraint, a spatial interpolation algorithm is employed to obtain the final spatial distribution diagram of the target reservoir parameters of the reservoir unit.
2. The method for predicting reservoir spatial distribution according to claim 1, characterized in that, The logging interpretation results include reservoir and non-reservoir layers, wherein the reservoir layers include oil layers, water layers, poor oil layers, gas layers, and oil and gas co-layers, and the non-reservoir layers include dry layers, tight layers, and mudstone layers; The reservoir data for each reservoir unit, obtained based on the reservoir data of each well and the top and bottom depths of each reservoir unit, includes: For any reservoir unit in any well, the total formation thickness of the reservoir unit is obtained based on the top and bottom depths of the reservoir unit. Based on the total formation thickness of the reservoir unit and the reservoir data of the well, the porosity, permeability and oil saturation of the reservoir unit are obtained. The reservoir thickness of the reservoir unit is obtained based on the total formation thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit.
3. The method for predicting reservoir spatial distribution according to claim 2, characterized in that, The porosity, permeability, and oil saturation of the reservoir unit are obtained based on the total formation thickness of the reservoir unit and the reservoir data of the well, including: Based on the total formation thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit, the porosity, permeability and oil saturation of each reservoir in the reservoir unit are obtained. Based on the porosity, permeability, and oil saturation of each reservoir in the reservoir unit, the porosity, permeability, and oil saturation of the reservoir unit are obtained using a weighted average method.
4. The method for predicting reservoir spatial distribution according to claim 2, characterized in that, The process of obtaining the reservoir thickness of a reservoir unit based on its total formation thickness and the stratigraphic data contained within the reservoir unit includes: Based on the total formation thickness of the reservoir unit and the stratigraphic data contained in the reservoir unit, the thickness of each reservoir in the reservoir unit is obtained. The reservoir thickness of the reservoir unit is obtained by summing the thicknesses of each reservoir in the reservoir unit.
5. The method for predicting reservoir spatial distribution according to claim 1, characterized in that, For any reservoir unit, based on the location information of each well, the reservoir data of the reservoir unit is graphically processed to obtain a preliminary spatial distribution diagram of each reservoir parameter of the reservoir unit, including: Normalize each reservoir parameter of the reservoir unit to obtain the normalized reservoir parameters of the reservoir unit; By substituting the normalized reservoir parameters of the reservoir unit and the location information of each well into the plotting and display function, a preliminary spatial distribution diagram of each reservoir parameter of the reservoir unit is obtained.
6. A device for predicting reservoir spatial distribution, characterized in that, The device includes: The first acquisition module is used to acquire reservoir data and location information of each well in the area to be studied, as well as the top and bottom depths of each reservoir unit. For any well, the reservoir data includes multiple layer data, and each layer data corresponds to multiple reservoir parameters, including porosity, permeability, thickness, oil saturation, and logging interpretation conclusions. The processing module is used to obtain reservoir data for each reservoir unit based on the reservoir data of each well and the top and bottom depths of each reservoir unit. The graphical module is used to graphically process the reservoir data of any reservoir unit based on the location information of each well, and obtain a preliminary spatial distribution diagram of each reservoir parameter of the reservoir unit. The second acquisition module is used to acquire target seismic attribute data within the study area, wherein the target seismic attribute data within the study area is target seismic attribute data at the same depth as the reservoir unit; the acquisition of target seismic attribute data within the study area includes: in response to the thickness of the reservoir unit being greater than or equal to 1 / 4 of the seismic wave wavelength, determining a time window according to the thickness of the reservoir unit, and extracting the target seismic attribute data within the study area; in response to the thickness of the reservoir unit being less than 1 / 4 of the seismic wave wavelength, determining a time window according to 1 / 4 of the seismic wave wavelength, and extracting the target seismic attribute data within the study area. The relationship determination module is used to overlay the preliminary spatial distribution diagram of the target reservoir parameters corresponding to the reservoir unit with the target seismic attribute data of the area to be studied to obtain the overlaid diagram, wherein the target reservoir parameter is one of the multiple reservoir parameters; based on the overlaid diagram, the target seismic attribute data of the well point location of each well is extracted; based on the target seismic attribute data of the well point location of each well and the corresponding values of the target reservoir parameter, the functional relationship between the target seismic attribute data and the target reservoir parameter is fitted. The spatial distribution module is used to obtain the final spatial distribution diagram of the target reservoir parameters of the reservoir unit based on the functional relationship between the target seismic attribute data and the target reservoir parameters, with the target seismic attribute data as a constraint, and using a spatial interpolation algorithm.
7. A storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the reservoir space distribution prediction method as described in any one of claims 1 to 5.