River channel region lithofacies model construction method and device under few-well condition

Abstract

The invention discloses a method and a device for constructing a lithofacies model in a riverway region under the condition of few wells. The method comprises the following steps: determining a plane layout map corresponding to a target layer in a target area, determining a target river channel area corresponding to the plane layout map, and obtaining multiple groups of sampling data corresponding to the target river channel area; determining a main variable range value corresponding to the target river channel region based on the multiple groups of sampling data corresponding to the target river channel region and the variation function, and determining a secondary variable range value corresponding to the target river channel region based on the main variable range value and the size information of the central beach in the target river channel region; and determining a layer structure model and a variable range direction corresponding to the target region, and determining a riverway region lithofacies model corresponding to the target layer based on the layer structure model, the main range value, the secondary range value and the variable range direction. According to the method, the accurate construction of the lithofacies model of the river region under the condition of few wells can be realized, and the construction, operability and flexibility of the lithofacies model of the river region are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for constructing a lithofacies model of a river channel region under conditions of few wells. Background Art

[0002] During oilfield development in riverbed areas, the number of wells, well spacing, and planar distribution often result in sparse and uneven data points, making lithofacies model construction difficult.

[0003] Currently, the traditional method for constructing lithofacies models for river channel regions is to perform sampling calculations based on the longitudinal wave impedance obtained through deterministic inversion. However, this traditional approach to constructing lithofacies models for river channel regions lacks flexibility and operability, often neglecting the channel orientation within the region, and failing to accurately construct lithofacies models within the complex geological context of the river channel region. Summary of the Invention

[0004] The present invention provides a method and device for constructing a lithofacies model of a river channel area under conditions of few wells, so as to realize the accurate construction of the lithofacies model of the river channel area under conditions of few wells, greatly improving the construction, operability and flexibility of the lithofacies model of the river channel area, thereby ensuring the accurate construction of the lithofacies model of the river channel area under the complex geological background of the river channel area.

[0005] According to one aspect of the present invention, a method for constructing a lithofacies model of a river channel region under conditions of few wells is provided, the method comprising:

[0006] Determine a plane distribution diagram corresponding to a target layer in a target area, determine a target river channel area corresponding to the plane distribution diagram, and obtain multiple sets of sampling data corresponding to the target river channel area;

[0007] Determine the primary range value corresponding to the target river channel area based on multiple groups of sampling data and variograms corresponding to the target river channel area, and determine the secondary range value corresponding to the target river channel area based on the primary range value and size information of the inner shoal in the target river channel area;

[0008] Determine the layer structure model and variable range direction corresponding to the target area, and determine the river area lithofacies model corresponding to the target layer based on the layer structure model, the major range value, the minor range value and the variable range direction.

[0009] According to another aspect of the present invention, a device for constructing a lithofacies model of a river channel region under conditions of few wells is provided, the device comprising:

[0010] A sampling module is used to determine a plane distribution diagram corresponding to a target layer in a target area, determine a target river channel area corresponding to the plane distribution diagram, and obtain multiple sets of sampling data corresponding to the target river channel area;

[0011] a range value determination module, configured to determine a major range value corresponding to the target river channel area based on multiple sets of sampling data and a variogram corresponding to the target river channel area, and to determine a minor range value corresponding to the target river channel area based on the major range value and size information of the inner bank of the target river channel area;

[0012] The lithologic model determination module is used to determine the layer structure model and variable range direction corresponding to the target area, and determine the river area lithologic model corresponding to the target layer based on the layer structure model, the main range value, the secondary range value and the variable range direction.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for constructing a river channel region lithofacies model under a few-well condition as described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for constructing a river channel regional lithofacies model under the condition of few wells as described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention determines the plane distribution map corresponding to the target layer in the target area, and determines the target river channel area corresponding to the plane distribution map, and obtains multiple sets of sampling data corresponding to the target river channel area, providing a data basis for subsequent variogram analysis and lithofacies modeling. Based on the multiple sets of sampling data and variogram corresponding to the target river channel area, the main range value corresponding to the target river channel area is determined, and based on the main range value and the size information of the inner bank of the target river channel area, the secondary range value corresponding to the target river channel area is determined, which can accurately describe the anisotropic characteristics of river channel sediments. The layer structure model and variable range direction corresponding to the target area are determined, and based on the layer structure model, the main range value, the secondary range value and the variable range direction, the lithofacies model of the river channel area corresponding to the target layer is determined, thereby improving the efficiency and flexibility of lithofacies model construction. By sampling data and center bar size, the range parameters can be made more reliable, and the linear characteristics of river channel sediments can be quantified according to the major and minor range values and directions, so as to realize the accurate construction of the lithofacies model of the river channel area under the condition of few wells. This greatly improves the construction, operability and flexibility of the lithofacies model of the river channel area, thereby ensuring the accurate construction of the lithofacies model of the river channel area under the complex geological background of the river channel area.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of a method for constructing a lithofacies model of a river channel region under conditions of few wells provided in accordance with the first embodiment of the present invention;

[0022] Figure 2 An example diagram of a main range value fitting result according to the first embodiment of the present invention;

[0023] Figure 3 An example diagram of determining a final main range value according to embodiment 1 of the present invention;

[0024] Figure 4 An example diagram of a variable range direction of a river channel region according to the first embodiment of the present invention;

[0025] Figure 5This is a flow chart of a method for constructing a lithofacies model of a river channel region under conditions of few wells provided in accordance with the second embodiment of the present invention;

[0026] Figure 6 This is an example diagram of a plane attribute graph involved in the second embodiment of the present invention;

[0027] Figure 7 This is an example diagram of a planar layout diagram according to the second embodiment of the present invention;

[0028] Figure 8 This is an example diagram of a gridded planar layout of a river channel region according to the second embodiment of the present invention;

[0029] Figure 9 is a schematic diagram of a seismic attribute sampling result involved in the second embodiment of the present invention;

[0030] Figure 10 This is an example diagram of a set of sampling data along the stream line of a river channel according to the second embodiment of the present invention;

[0031] Figure 11 2 is a schematic structural diagram of a device for constructing a lithofacies model of a river channel region under conditions of few wells according to a third embodiment of the present invention;

[0032] Figure 12 It is a structural schematic diagram of an electronic device for implementing the method for constructing a river channel regional lithofacies model under the condition of few wells according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," "target," and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0035] Example 1

[0036] Figure 1 A flowchart of a method for constructing a lithofacies model of a river channel region under conditions of few wells is provided for the first embodiment of the present invention. This embodiment is applicable to the case where a lithofacies model of a river channel region is constructed under conditions of few wells. The method can be executed by a device for constructing a lithofacies model of a river channel region under conditions of few wells. The device for constructing a lithofacies model of a river channel region under conditions of few wells can be implemented in the form of hardware and / or software. The device for constructing a lithofacies model of a river channel region under conditions of few wells can be configured in an electronic device. Figure 1 As shown, the method includes:

[0037] S110 , determining a plane distribution diagram corresponding to a target layer in a target area, determining a target river channel area corresponding to the plane distribution diagram, and obtaining multiple sets of sampling data corresponding to the target river channel area.

[0038] Among them, the target area may refer to an area containing a river channel and its adjacent floodplain. The target layer may refer to a specific stratigraphic unit that needs to be studied in the target area, which may correspond to a sedimentary body formed in a certain geological history period (such as a river channel sand body, a delta front phase), and has unique lithology, physical properties or oil and gas properties. The plane distribution map may refer to a contour map or probability distribution map that reflects the horizontal distribution characteristics of the target layer in the target area (such as seismic attributes such as thickness, lithology ratio, and physical parameters), which is a projection of a three-dimensional geological body on a two-dimensional plane. The target river channel area may refer to a sub-area further delineated from the target layer, which has clear river channel sedimentary characteristics (such as continuous sand bodies, developed heart bars or point bars), and is the core object of subsequent lithofacies modeling or reservoir evaluation. The sampling data may refer to the horizontal distribution characteristics of the target river channel area extracted from the plane distribution map.

[0039] Specifically, under conditions with few wells, a planar distribution map of the target layer (e.g., river channel sedimentary layer) is constructed using logging and seismic data from various well points (exploratory wells) within the target area, combined with well-seismic calibration operations. This is known as a planar distribution map of the target layer within the target area. Logging data can refer to a continuous depth-parameter curve obtained by directly measuring formation physical parameters (e.g., electrical, acoustic, and radioactive properties) using downhole instruments, reflecting vertical changes in the formation around the wellbore. Seismic data can refer to a three-dimensional time-space domain data volume formed by processing the physical signals of artificially excited seismic waves propagating, reflecting, and refracting in the formation. Based on the horizontal distribution characteristics corresponding to the planar distribution map, the river channel boundary is demarcated, and the target river channel area corresponding to the planar distribution map is determined. Data sampling is then performed along the river flow direction in the target river channel area in the planar distribution map to obtain multiple sets of sampling data corresponding to the target river channel area. This improves data acquisition accuracy and provides basic input for subsequent variogram analysis and lithofacies modeling.

[0040] S120. Based on multiple groups of sampling data and variation functions corresponding to the target river channel area, determine the main range value corresponding to the target river channel area, and based on the main range value and the size information of the inner shoal of the target river channel area, determine the secondary range value corresponding to the target river channel area.

[0041] Among them, the variogram can refer to a core tool in geostatistics to describe the variability of spatial variables, which is used to quantify the spatial correlation of seismic attributes (such as lithology, porosity, etc.). The main range value can refer to the maximum distance at which the correlation of spatial variables disappears in the main direction (such as the direction of the river channel). The heart bar can refer to a sandy shoal located in the middle of the riverbed in a river sedimentary system, formed by the action of complex circulation. Size information can refer to specific parameters that describe the geometric characteristics of a geological body, including length, width, thickness, etc. The secondary range value can refer to the maximum distance at which the correlation of spatial variables disappears in the secondary direction (such as the direction perpendicular to the river channel), which is usually smaller than the main range value.

[0042] Specifically, the variogram is calculated based on multiple sets of sampled data corresponding to the target channel area to determine the primary range value corresponding to the target channel area. This primary range value is then combined with the dimensional information of the inner bank within the target channel area (such as the major and minor axes) to obtain the secondary range value corresponding to the target channel area, that is, the range value perpendicular to the main channel direction. The secondary range value can compensate for the isotropy assumption of a single primary range and more accurately describe the anisotropic characteristics of river channel sediments.

[0043] Exemplarily, "determining the main range value corresponding to the target river channel area based on multiple groups of sampling data and variogram corresponding to the target river channel area" in S120 may include: adjusting the step size of each group of sampling data, determining a preset number of groups of step-adjusted sampling data corresponding to each group of sampling data, and determining the coefficient of variation corresponding to each group of step-adjusted sampling data based on the variogram formula; determining the main range value to be selected corresponding to each group of sampling data based on the coefficient of variation and a preset fitting model, and performing data fitting on the main range value to be selected to determine the main range value corresponding to the target river channel area.

[0044] Among them, the preset number of groups may refer to the preset number of groups of new sampling data obtained after multiple step adjustments are made to a certain group of sampling data. Step-adjusted sampling data may refer to the new sampling data obtained after step adjustment is made to a certain group of sampling data. The coefficient of variation may refer to an indicator for quantifying the randomness of microscopic scale data in variogram analysis. The preset fitting model may refer to a mathematical function used in geological statistics to describe the spatial variability of regionalized variables (such as lithology, porosity, permeability, etc.), the core function of which is to quantify the correlation (range) and variability of data at different spatial scales by fitting the experimental variogram curve. For example, the preset fitting model may be a spherical theoretical model. The main range value to be selected may refer to the main range value extracted from the same group of sampling data through the preset fitting model.

[0045] Specifically, step size adjustment can use the interval between adjacent data in the sampled data (e.g., the distance between adjacent sampling points) as the initial step size. This initial step size can be adjusted by integer multiples, and sampling data extraction is performed on this set of sampled data at intervals of the adjusted step size, obtaining a set of step-size-adjusted sampled data corresponding to the adjusted step size. Based on different step size intervals, a preset number of step-size-adjusted sampled data sets corresponding to this set of sampled data are obtained. Each set of step-size-adjusted sampled data is input into the variogram formula to obtain the coefficient of variation corresponding to each set of step-size-adjusted sampled data. The multiple coefficients of variation corresponding to each set of sampled data are fitted using a preset fitting model to obtain the candidate main range value corresponding to each set of sampled data. The mode of the multiple candidate main range values is determined as the main range value corresponding to the target river channel area. Alternatively, the mode of the integer closest to each candidate main range value can be determined as the main range value corresponding to the target river channel area. Through step size adjustment, the variability of the target layer at different spatial scales can be quantified, providing a basis for determining the main range value, ensuring that the main range value conforms to the sedimentary law.

[0046] For example, the calculation formula for the coefficient of variation corresponding to each set of step-size adjusted sampling data can be as follows:

[0047]

[0048] Among them, x iis the coordinate of the kth observation point; Z(x i ),Z(x i +h) are x i and x i +h is the observation value at two points; h is the distance between the two observation points; N(h) is the number of data pairs with a distance of h; y * (h) is the value of the coefficient of variation.

[0049] According to the step size of each group, the sampling data is adjusted. According to the above calculation formula, the coefficient of variation γ*(0), γ*(1), γ*(2)... corresponding to the group can be obtained in turn by substituting different step sizes (intervals between adjacent sampling points). Then, the spherical theoretical model is used for fitting to obtain the main range characteristic value of the group (such as Figure 2 shown).

[0050] Calculate the main range eigenvalue of each group in the I direction, and count the I eigenvalues. The mode can be taken as the final main range value (such as Figure 3 shown).

[0051] S130. Determine the layer structure model and variable range direction corresponding to the target area, and determine the river channel area lithofacies model corresponding to the target layer based on the layer structure model, major range value, minor range value and variable range direction.

[0052] The layer structure model can refer to a model that reflects the three-dimensional spatial structure of the target layer. The variable range direction can refer to the direction of the range at different locations within the target area. The river channel region lithofacies model can refer to a three-dimensional geological model that describes the distribution of different lithofacies types within the target layer.

[0053] Specifically, well logging and seismic data from various well points within the target area can be used to construct a stratum structure model corresponding to the target layer. The variable range direction can be defined based on the flow direction or river channel orientation corresponding to the target river channel area. Based on the stratum structure model, major and minor range values, and variable range direction, a sequential indicator stochastic simulation method can be used to generate lithofacies distributions (e.g., river channels and floodplains) and establish a lithofacies model for the river channel area corresponding to the target layer, greatly improving the efficiency and flexibility of lithofacies model construction.

[0054] Exemplarily, S130 may include: determining the layer structure model corresponding to the target area, and filling the layer structure model with data based on the lithofacies curves of each well point, determining the spatial distribution information of the lithofacies within the target layer of the target area, and determining the variable range direction corresponding to each position in the target river channel area based on a preset reference direction; determining the lithofacies model of the river channel area corresponding to the target layer based on the layer structure model, major range value, minor range value, spatial distribution information and variable range direction after data filling.

[0055] A lithofacies curve can refer to a continuous record describing the vertical variation of lithofacies types (e.g., sandstone, mudstone, siltstone, etc.) with depth within a single well. Spatial distribution information can refer to the overall distribution ratio of each lithofacies type within the target layer and the lithofacies distribution ratio at each vertical position. A preset reference direction can refer to a direction with a preset angle of zero degrees.

[0056] Specifically, a stratum structure model corresponding to the target area can be constructed using 3D visualization modeling software (such as Petrel). Alternatively, a 3D structural model (stratum structure model) of the study area can be generated through interpolation, using well logging data as control points and the top surface structure traced by geophysical analysis as the interwell trend. The vertical grid size is set by comprehensively considering the thickness of interlayers and sand bodies. The lithofacies curves of each well point are then populated into the stratum structure model, and the populated stratum structure model is analyzed to obtain spatial distribution information of the lithofacies within the target layer of the target area. Based on a preset reference direction, the deviation angle of the river channel flow at each location within the target channel area relative to the preset reference direction is determined as the variable range direction corresponding to each location within the target channel area. Based on the populated stratum structure model, major range values, minor range values, and variable range directions, using this spatial distribution information as a spatial constraint, a pre-set modeling method (such as sequential indicator stochastic simulation) is used to construct a lithofacies model of the channel area corresponding to the target layer, adapting it to the geological background of the channel and making the model more consistent with actual sedimentary patterns.

[0057] Exemplarily, data is filled into the layer structure model based on the lithofacies curves of each well point to determine the spatial distribution information of the lithofacies within the target layer of the target area, including: filling the lithofacies curves of each well point into the layer structure model based on the corresponding position of each well point in the layer structure model, and analyzing the spatial distribution pattern of each lithofacies in the layer structure model after data filling to determine the horizontal distribution information and vertical distribution information of the lithofacies within the target layer.

[0058] The horizontal distribution information may refer to the distribution ratio of each lithofacies within the entire target layer, and the vertical distribution information may refer to the distribution ratio of each position (depth) of the target layer in the vertical direction.

[0059] Specifically, the lithofacies curve (e.g., sandstone, mudstone, siltstone, etc.) of each well is matched to the corresponding horizon (depth) based on the intersection of the drilling trajectory corresponding to each well point and the layer interface of the layer structure model. For example, if the depth segment of layer 1 at the well point is "sandstone-mudstone interbedded," the lithofacies curve of this segment is filled into the corresponding grid in the model, and the layer structure model can be divided into a three-dimensional grid (e.g., 50m×50m×1m). The spatial distribution trends and patterns of each microfacies (lithofacies) within the target layer are analyzed, and the horizontal and vertical distribution information of the lithofacies within the target layer is calculated, thus providing a data foundation for the subsequent construction of the lithofacies model.

[0060] Exemplarily, based on a preset reference direction, the variable range direction corresponding to each position in the target river channel area is determined, including: based on the preset reference direction, determining the deviation angle of the river flow direction corresponding to each position in the target river channel area and the preset reference direction in the clockwise direction, and determining the deviation angle as the variable range direction corresponding to each position in the target river channel area.

[0061] Specifically, the preset reference direction can be an angle in a mathematical coordinate system (such as 0° = due north, 90° = due east). Different river channel parts have different streamline directions. Taking due north as 0°, the angle (angle value, not arc value) of the streamline direction of the river channel at each position deviating from due north is calculated, and the calculated deflection angle is directly used as the variable range direction of the position, which is used for the variation function direction in subsequent lithofacies modeling, so that the spatial correlation direction of the lithofacies (such as river channel sand body) can be consistent with the actual river channel extension direction. Figure 4 As shown, different variable range directions can also be displayed differently by the size of the deflection angle to generate a river variable range direction map, thereby facilitating the intuitive display of the variable range direction of the target river area.

[0062] In this embodiment, by determining the plane distribution map corresponding to the target layer in the target area and determining the target river channel area corresponding to the plane distribution map, multiple sets of sampling data corresponding to the target river channel area are obtained, providing a data basis for subsequent variogram analysis and lithofacies modeling. Based on the multiple sets of sampling data and variogram corresponding to the target river channel area, the main range value corresponding to the target river channel area is determined, and based on the main range value and the size information of the inner bank of the target river channel area, the secondary range value corresponding to the target river channel area is determined, which can accurately describe the anisotropic characteristics of the river channel sediments. The layer structure model and variable range direction corresponding to the target area are determined, and based on the layer structure model, the main range value, the secondary range value and the variable range direction, the lithofacies model of the river channel area corresponding to the target layer is determined, thereby improving the efficiency and flexibility of lithofacies model construction. By sampling data and center bar size, the range parameters can be made more reliable, and the linear characteristics of river channel sediments can be quantified according to the major and minor range values and directions, so as to realize the accurate construction of the lithofacies model of the river channel area under the condition of few wells. This greatly improves the construction, operability and flexibility of the lithofacies model of the river channel area, thereby ensuring the accurate construction of the lithofacies model of the river channel area under the complex geological background of the river channel area.

[0063] Example 2

[0064] Figure 5This is a flowchart of a method for constructing a lithofacies model for a river channel region under conditions with few wells, provided in Example 2 of the present invention. Based on the above examples, this example optimizes the steps of "determining a planar distribution map corresponding to a target layer within a target region, determining a target river channel region corresponding to the planar distribution map, and obtaining multiple sets of sampling data corresponding to the target river channel region." Explanations of terms that are identical or corresponding to those in the above examples are omitted here.

[0065] See also Figure 5 Another method for constructing a lithofacies model of a river channel region under the condition of few wells provided in this embodiment specifically includes the following steps:

[0066] S210. Determine the plane distribution diagram corresponding to the target layer in the target area based on the thickness of the sand body encountered at each well point and the top and bottom phase information of the well point, wherein the target layer is a stratum formed in the river area in the target area during a preset historical period.

[0067] The thickness of the sand body encountered at a well point can refer to the cumulative vertical thickness of the sandstone layer within the target layer at a specific well point, as determined through well logging interpretation or core analysis. The top and bottom phase information of the well point can refer to the top and bottom depths and sequence interface types of the target layer at the well point, and is used to determine the spatial position of the stratum and its relationship to sedimentary evolution.

[0068] Specifically, sandstone sections are identified from well logging curves, and the cumulative thickness of the target layer (river channel sedimentary stratum) at each well point is calculated. Seismic interpretation is used to determine the top and bottom phase information (such as peaks, troughs, or zero phase) of the target layer at the well point for horizon calibration. Planar attributes of the target layer are then extracted, and a planar distribution map corresponding to the target layer is constructed by statistically analyzing the attribute values at each well point. This top and bottom phase information ensures accurate horizon calibration and avoids distortion of the distribution map caused by intersecting layers.

[0069] Exemplarily, S210 may include: determining the layer structural surface corresponding to the target layer in the target area based on the top and bottom phase information of the well points corresponding to the target layer of each well point in the target area, and extracting the plane attributes of the layer structural surface to determine the plane attribute information corresponding to the target layer; determining the target plane attributes corresponding to the target layer based on the well point drilling sand body thickness and plane attribute information corresponding to the target layer of each well point in the target area, and generating a plane distribution map corresponding to the target layer based on the target plane attributes.

[0070] Among them, the layer structural surface can refer to the continuous distribution interface of the same geological layer in the target area in three-dimensional space, reflecting the top or bottom surface morphology of the layer (such as the top surface of the river sand body and the bottom surface of the coal seam). Plane attribute information can refer to the seismic attribute information extracted from the layer structural surface, which is used to describe the structural morphology, sedimentary characteristics or physical property distribution pattern. For example, the plane attribute information can be a variety of attributes such as minimum amplitude, maximum amplitude, total sum of troughs, root mean square, and maximum. The target attribute information can refer to the seismic attribute with the strongest correlation with the thickness of the sand body encountered by the well point in the plane attribute information.

[0071] Specifically, the top and bottom corresponding peaks, troughs or zero phases of the target layer sand body can be determined based on the well seismic calibration, that is, the top and bottom phase information of the well point, and the layer structural surface corresponding to the target layer in the target area can be constructed. The plane attributes of the layer structural surface are extracted to obtain the plane attribute information corresponding to the target layer, including the minimum amplitude, maximum amplitude, sum of the total troughs, root mean square, maximum and other attributes. Combining the thickness of the sand body encountered by the well point with the plane attribute information, the seismic attribute with the strongest correlation with the thickness of the sand body encountered by the well point in the plane attribute information is determined as the target plane attribute corresponding to the target layer. The thickness of the sand body can be indicated by the size of the attribute value of the target plane attribute, and the plane distribution morphology of the target layer can be further portrayed, that is, the plane distribution map corresponding to the target layer, thereby improving the image accuracy.

[0072] Exemplarily, based on the well point drilling sand body thickness and plane attribute information corresponding to the target layer of each well point in the target area, the target plane attribute corresponding to the target layer is determined, including: performing correlation analysis on the well point drilling sand body thickness and plane attribute information corresponding to the target layer of each well point in the target area based on a preset correlation algorithm, and determining the plane attribute with the strongest correlation with the well point drilling sand body thickness in the plane attribute information as the target plane attribute corresponding to the target layer.

[0073] Specifically, according to a preset correlation algorithm (such as the Pearson correlation coefficient), a correlation analysis can be performed on the thickness of the sand body encountered by the well point and the plane attribute information corresponding to the target layer of each well point in the target area, and the correlation coefficient between each plane attribute and the thickness of the sand body encountered by the well point can be calculated. The plane attribute with the strongest correlation with the thickness of the sand body encountered by the well point in the plane attribute information is determined, and the plane attribute with the strongest correlation with the thickness of the sand body encountered by the well point in the plane attribute information is determined as the target plane attribute corresponding to the target layer, thereby improving the accuracy of the subsequent plane layout map.

[0074] Exemplarily, based on the target plane attributes, a plane layout map corresponding to the target layer is generated, including: displaying the target plane attributes corresponding to each position in the target layer according to the numerical differences, and generating a plane attribute map corresponding to the target layer; based on the plane attribute map, determining the river area within the target area, and generating a plane layout map corresponding to the target layer.

[0075] The plane attribute map may refer to a visualization image generated by projecting the target plane attributes of each position of the target layer onto a two-dimensional plane.

[0076] Specifically, the target plane attributes corresponding to each position in the target layer can be displayed differently according to the numerical difference, so as to intuitively reflect the attribute changes of each position in the target layer (for example, the larger the attribute value, the darker the color), and generate the plane attribute map corresponding to the target layer. For example, the target plane attribute can be the minimum amplitude, such as Figure 6 As shown in , it is the plane attribute diagram when the target plane attribute is the minimum amplitude. According to the plane attribute diagram, Figure 7 As shown in Figure 2, the size of the attribute value can indicate the thickness of the sand body, thereby depicting the channel boundary and generating a planar distribution map corresponding to the target layer.

[0077] S220, determining the target river channel area corresponding to the planar layout diagram, and sampling the target river channel area based on a preset sampling interval along the river channel flow direction and a preset number of samples perpendicular to the river channel flow direction to obtain multiple groups of sampling data corresponding to the target river channel area.

[0078] The preset sampling interval may refer to the preset distance between adjacent sampling points. The preset number of samples may refer to the preset number of sampling points arranged along a sampling line perpendicular to the direction of the river.

[0079] Specifically, a preset sampling interval and number of samples can be set based on the channel width (e.g., 500m) and length to ensure that longitudinal variations in the channel are captured. Three to five survey lines (the number of lines equals the preset number of samples) are arranged perpendicular to the channel (e.g., a channel width of 500m). Samples are taken at fixed intervals (e.g., 100m) along each line to quantify lateral sedimentation differences. Data collection can generate multiple sets of sampling data corresponding to the target channel area, improving the comprehensiveness of the sampling data.

[0080] For example, Figure 8 As shown in Figure 1, the planar layout can be gridded, and the stream line direction (stream direction) of the river channel is determined as the I direction, and the direction perpendicular to the stream line of the river channel is the J direction. The I and J directions are always orthogonal. The I direction will show different directions as the river channel position changes, but it always represents the main water flow direction of the river section. An orthogonal grid is generated according to the I and J directions. The grid size in the I direction is set to 50m (preset sampling interval). In the J direction, the preset number of samples is used, and each sampling node obtains an attribute data point, so that data sampling is evenly performed in the I and J directions, and the seismic attribute sampling results (sampling data) are obtained. The sampling results are shown in Figure 1. Figure 9As shown, the discretization of earthquake attributes is realized. It should be noted that the sampling data obtained according to the preset sampling interval and the preset number of samples need to retain the changes in earthquake attributes to avoid the discretization results being blurred and unable to reflect earthquake attribute information due to the grid being too large, and to avoid the grid being too small, which leads to a surge in data volume and excessive computation. According to the grid number, the grids with the same I number are extracted and arranged from small to large according to the J number to obtain a group of data representing the direction of the flow line along the river channel (such as Figure 10 Each group of data was used for subsequent analysis.

[0081] S230. Based on multiple sets of sampling data and variation functions corresponding to the target river channel area, determine the main range value corresponding to the target river channel area, and based on the main range value and the size information of the inner shoal of the target river channel area, determine the secondary range value corresponding to the target river channel area.

[0082] S240. Determine the layer structure model and variable range direction corresponding to the target area, and determine the river channel area lithofacies model corresponding to the target layer based on the layer structure model, major range value, minor range value and variable range direction.

[0083] The technical solution of this embodiment is adopted. The present invention determines the plane distribution diagram corresponding to the target layer in the target area based on the thickness of the sand body encountered by the well point drilling at each well point and the top and bottom phase information of the well point. It realizes the accurate construction of the plane distribution morphology of the target layer, and provides a basis for subsequent data sampling. Determine the target river channel area corresponding to the plane distribution diagram, and sample the target river channel area based on the preset sampling interval along the river channel flow direction and the preset number of samples perpendicular to the river channel flow direction, obtain multiple groups of sampling data corresponding to the target river channel area, and realize multi-dimensional data collection of the river channel area. Through the plane distribution diagram, it is possible to achieve accurate characterization of the river channel area, and further combine the river channel flow direction to perform multi-dimensional data collection on the river channel area, which can provide comprehensive input for subsequent lithofacies modeling and reservoir evaluation, and can be extended to other sedimentary systems, with strong scalability and operability.

[0084] Example 3

[0085] Figure 11 This is a schematic diagram of the structure of a device for constructing a river channel regional lithofacies model under conditions of few wells provided in the third embodiment of the present invention. Figure 11 As shown, the device includes: a sampling module 310, a range value determination module 320 and a lithofacies model determination module 330;

[0086] The sampling module is used to determine the plane distribution diagram corresponding to the target layer in the target area, determine the target river area corresponding to the plane distribution diagram, and obtain multiple sets of sampling data corresponding to the target river area;

[0087] a range value determination module, configured to determine a major range value corresponding to the target river channel area based on multiple sets of sampling data and a variogram corresponding to the target river channel area, and to determine a minor range value corresponding to the target river channel area based on the major range value and size information of the inner bank of the target river channel area;

[0088] The lithologic model determination module is used to determine the layer structure model and variable range direction corresponding to the target area, and determine the river area lithologic model corresponding to the target layer based on the layer structure model, the main range value, the secondary range value and the variable range direction.

[0089] In this embodiment, by determining the plane distribution map corresponding to the target layer in the target area and determining the target river channel area corresponding to the plane distribution map, multiple sets of sampling data corresponding to the target river channel area are obtained to provide a data basis for subsequent variogram analysis and lithofacies modeling. Based on the multiple sets of sampling data and variogram corresponding to the target river channel area, the main range value corresponding to the target river channel area is determined, and based on the main range value and the size information of the inner bank of the target river channel area, the secondary range value corresponding to the target river channel area is determined, which can accurately describe the anisotropic characteristics of river channel sediments. The layer structure model and variable range direction corresponding to the target area are determined, and based on the layer structure model, the main range value, the secondary range value and the variable range direction, the lithofacies model of the river channel area corresponding to the target layer is determined, thereby improving the efficiency and flexibility of lithofacies model construction. By sampling data and center bar size, the range parameters can be made more reliable, and the linear characteristics of river channel sediments can be quantified according to the major and minor range values and directions, so as to realize the accurate construction of the lithofacies model of the river channel area under the condition of few wells. This greatly improves the construction, operability and flexibility of the lithofacies model of the river channel area, thereby ensuring the accurate construction of the lithofacies model of the river channel area under the complex geological background of the river channel area.

[0090] Optionally, the sampling module 310 includes:

[0091] a distribution diagram determining unit, configured to determine a planar distribution diagram corresponding to a target layer in the target area based on the thickness of the sand body encountered by the well point at each well point and the top and bottom phase information of the well point, wherein the target layer is a stratum formed in a river channel area in the target area during a preset historical period;

[0092] The sampling unit is used to determine the target river channel area corresponding to the planar layout diagram, and sample the target river channel area based on a preset sampling interval along the river channel flow direction and a preset number of samples perpendicular to the river channel flow direction to obtain multiple groups of sampling data corresponding to the target river channel area.

[0093] Optionally, the layout pattern determining unit includes:

[0094] An attribute information determination subunit is used to determine the layer structure surface corresponding to the target layer in the target area based on the top and bottom phase information of the well points corresponding to the target layer of each well point in the target area, and perform plane attribute extraction on the layer structure surface to determine the plane attribute information corresponding to the target layer;

[0095] The distribution diagram determination subunit is used to determine the target plane attributes corresponding to the target layer based on the thickness of the sand body encountered by the well points corresponding to the target layer of each well point in the target area and the plane attribute information, and generate the plane distribution diagram corresponding to the target layer based on the target plane attributes.

[0096] Optionally, the distribution diagram determination subunit is specifically used to: perform correlation analysis on the thickness of the sand body encountered by the well point corresponding to the target layer of each well point in the target area and the plane attribute information based on a preset correlation algorithm, and determine the plane attribute with the strongest correlation with the thickness of the sand body encountered by the well point in the plane attribute information as the target plane attribute corresponding to the target layer.

[0097] Optionally, the distribution map determination subunit is specifically used to: differentiate the target plane attributes corresponding to each position in the target layer according to the numerical difference, and generate a plane attribute map corresponding to the target layer; based on the plane attribute map, determine the river area within the target area, and generate a plane distribution map corresponding to the target layer.

[0098] Optionally, the range value determination module 320 is specifically used to: adjust the step size of each group of sampling data, determine a preset number of groups of step-adjusted sampling data corresponding to each group of sampling data, and determine the coefficient of variation corresponding to each group of step-adjusted sampling data based on the variation function formula; determine the candidate main range value corresponding to each group of sampling data based on the coefficient of variation and a preset fitting model, and perform data fitting on the candidate main range value to determine the main range value corresponding to the target river area.

[0099] Optionally, the lithofacies model determination module 330 includes:

[0100] a range direction determination unit, configured to determine a layer structure model corresponding to the target area, fill the layer structure model with data based on the lithofacies curves of each well point, determine the spatial distribution information of the lithofacies within the target layer of the target area, and determine the variable range direction corresponding to each position in the target river channel area based on a preset reference direction;

[0101] The lithofacies model construction unit is used to determine the lithofacies model of the river channel area corresponding to the target layer based on the layer structure model, the major range value, the minor range value, the spatial distribution information and the variable range direction after data filling.

[0102] Optionally, the range direction determination unit is specifically used to: based on the corresponding position of each well point in the layer structure model, fill the lithologic curve of each well point into the layer structure model, and analyze the spatial distribution law of each lithologic phase in the layer structure model after data filling, and determine the horizontal distribution information and vertical distribution information of the lithologic phase in the target layer.

[0103] Optionally, the range direction determination unit is specifically used to: determine the deviation angle of the river flow direction corresponding to each position in the target river channel area and the preset reference direction in the clockwise direction based on a preset reference direction, and determine the deviation angle as the variable range direction corresponding to each position in the target river channel area.

[0104] The above-mentioned device can execute the method for constructing a lithofacies model of a river channel region under conditions of few wells provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method for constructing a lithofacies model of a river channel region under conditions of few wells.

[0105] Example 4

[0106] Figure 12 1 is a schematic diagram of the structure of an electronic device for implementing the method for constructing a river channel regional lithofacies model under the condition of few wells according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0107] like Figure 12 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12 and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0109] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the method for constructing a lithofacies model for a river channel region under conditions with few wells.

[0110] In some embodiments, the method for constructing a lithofacies model of a river channel region under conditions with few wells can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for constructing a lithofacies model of a river channel region under conditions with few wells described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for constructing a lithofacies model of a river channel region under conditions with few wells by any other appropriate means (e.g., by means of firmware).

[0111] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the ROM 12. When the computer program is executed by the processor 11, the above-mentioned functions defined in the method of the embodiment of the present invention are performed.

[0112] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0114] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0115] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0116] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0117] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0118] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0119] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for constructing a lithofacies model of a river channel region under conditions of few wells, characterized in that: include: Determine a plane distribution diagram corresponding to a target layer in a target area, determine a target river channel area corresponding to the plane distribution diagram, and obtain multiple sets of sampling data corresponding to the target river channel area; Determine the primary range value corresponding to the target river channel area based on multiple groups of sampling data and variograms corresponding to the target river channel area, and determine the secondary range value corresponding to the target river channel area based on the primary range value and size information of the inner shoal in the target river channel area; Determine the layer structure model and variable range direction corresponding to the target area, and determine the river area lithofacies model corresponding to the target layer based on the layer structure model, the major range value, the minor range value and the variable range direction.

2. The method according to claim 1, characterized in that The determining of a plane distribution diagram corresponding to a target layer in a target area, determining a target river channel area corresponding to the plane distribution diagram, and obtaining multiple sets of sampling data corresponding to the target river channel area include: Determine a planar distribution diagram corresponding to the target layer in the target area based on the thickness of the sand body encountered by the well point at each well point and the top and bottom phase information of the well point, wherein the target layer is a stratum formed in the river area of the target area during a preset historical period; Determine the target river channel area corresponding to the planar layout diagram, and sample the target river channel area based on a preset sampling interval along the river channel flow direction and a preset number of samples perpendicular to the river channel flow direction to obtain multiple groups of sampling data corresponding to the target river channel area.

3. The method according to claim 2, characterized in that The step of determining a planar distribution diagram corresponding to the target layer in the target area based on the well point drilling sand body thickness and the well point top and bottom phase information of the target layer at each well point in the target area comprises: Based on the top and bottom phase information of the target layers corresponding to the well points of each well point in the target area, the layer structure surface corresponding to the target layer in the target area is determined, and the plane attribute information corresponding to the target layer is determined by extracting the plane attribute of the layer structure surface; Based on the sand body thickness encountered by the well point corresponding to the target layer of each well point in the target area and the plane attribute information, the target plane attribute corresponding to the target layer is determined, and based on the target plane attribute, a plane distribution map corresponding to the target layer is generated.

4. The method according to claim 3, characterized in that The determining of the target plane attribute corresponding to the target layer based on the well point drilled sand body thickness corresponding to the target layer of each well point in the target area and the plane attribute information includes: Based on a preset correlation algorithm, a correlation analysis is performed on the thickness of the sand body encountered by the well point corresponding to the target layer of each well point in the target area and the plane attribute information, and the plane attribute with the strongest correlation with the thickness of the sand body encountered by the well point in the plane attribute information is determined as the target plane attribute corresponding to the target layer.

5. The method according to claim 3, characterized in that The generating, based on the target plane attributes, a plane layout diagram corresponding to the target layer includes: Differentiating and displaying the target plane attributes corresponding to various positions in the target layer according to numerical differences, and generating a plane attribute map corresponding to the target layer; Based on the plane attribute map, the river channel area within the target area is determined, and a plane distribution map corresponding to the target layer is generated.

6. The method according to claim 1, characterized in that The determining of the main range value corresponding to the target river channel area based on the multiple groups of sampling data and the variogram corresponding to the target river channel area includes: Performing step adjustment on each set of sampling data, determining a preset number of sets of step-adjusted sampling data corresponding to each set of sampling data, and determining a coefficient of variation corresponding to each set of step-adjusted sampling data based on a variogram formula; Based on the coefficient of variation and the preset fitting model, the candidate main range value corresponding to each set of sampling data is determined, and data fitting is performed on the candidate main range value to determine the main range value corresponding to the target river area.

7. The method according to claim 1, characterized in that The determining of the layer structure model and the variable range direction corresponding to the target area, and determining the river channel area lithofacies model corresponding to the target layer based on the layer structure model, the major range value, the minor range value and the variable range direction, includes: Determine the layer structure model corresponding to the target area, and fill the layer structure model with data based on the lithofacies curves of each well point, determine the spatial distribution information of the lithofacies within the target layer of the target area, and determine the variable range direction corresponding to each position in the target river channel area based on a preset reference direction; Based on the layer structure model after data filling, the major range value, the minor range value, the spatial distribution information and the variable range direction, the river channel area lithofacies model corresponding to the target layer is determined.

8. The method according to claim 7, characterized in that The layer structure model is filled with data based on the lithofacies curves of each well point to determine the spatial distribution information of the lithofacies in the target layer of the target area, including: Based on the corresponding position of each well point in the layer structure model, the lithofacies curve of each well point is filled into the layer structure model, and the spatial distribution law of each lithofacies in the layer structure model after data filling is analyzed to determine the horizontal and vertical distribution information of the lithofacies in the target layer.

9. The method according to claim 7, characterized in that The step of determining the variable range direction corresponding to each position in the target river channel area based on the preset reference direction includes: Based on the preset reference direction, the deviation angle of the river flow direction corresponding to each position in the target river channel area and the preset reference direction in the clockwise direction is determined, and the deviation angle is determined as the variable range direction corresponding to each position in the target river channel area.

10. A device for constructing a lithofacies model of a river channel region under conditions of few wells, characterized in that: include: A sampling module is used to determine a plane distribution diagram corresponding to a target layer in a target area, determine a target river channel area corresponding to the plane distribution diagram, and obtain multiple sets of sampling data corresponding to the target river channel area; a range value determination module, configured to determine a major range value corresponding to the target river channel area based on multiple sets of sampling data and a variogram corresponding to the target river channel area, and to determine a minor range value corresponding to the target river channel area based on the major range value and size information of the inner bank of the target river channel area; The lithologic model determination module is used to determine the layer structure model and variable range direction corresponding to the target area, and determine the river area lithologic model corresponding to the target layer based on the layer structure model, the main range value, the secondary range value and the variable range direction.

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