A method and device for constructing a lithofacies model of a river channel region under a few-well condition

Abstract

This invention discloses a method and apparatus for constructing lithofacies models of river channels under conditions with few wells. The method includes: determining the planar distribution map corresponding to the target layer within the target area, and determining the target river channel area corresponding to the planar distribution map; acquiring multiple sets of sampling data corresponding to the target river channel area; determining the principal range value corresponding to the target river channel area based on the multiple sets of sampling data and the variogram function; determining the secondary range value corresponding to the target river channel area based on the principal range value and the size information of the incenter bars in the target river channel area; determining the stratigraphic model and variable range direction corresponding to the target area; and determining the lithofacies model of the river channel area corresponding to the target layer based on the stratigraphic model, principal range value, secondary range value, and variable range direction. This invention enables accurate construction of lithofacies models of river channels under conditions with few wells, greatly improving the construction, operability, and flexibility of lithofacies models of river channels.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and apparatus for constructing a lithofacies model of a river channel area under conditions of few wells. Background Technology

[0002] In the development of oilfields in riverine areas, the scarcity and uneven distribution of data points due to the number of wells, well spacing, and planar distribution often make it difficult to construct lithofacies models.

[0003] Currently, the traditional method for constructing lithofacies models of river channels typically involves sampling calculations based on P-wave impedance obtained through deterministic inversion, thereby constructing the lithofacies model. However, this traditional method suffers from poor flexibility and operability, often neglecting the river channel orientation and failing to guarantee accurate construction of lithofacies models in the complex geological context of river channels. Summary of the Invention

[0004] This invention provides a method and apparatus for constructing lithofacies models of river channels under conditions with few wells, so as to achieve accurate construction of lithofacies models of river channels under conditions with few wells, greatly improving the construction, operability and flexibility of lithofacies models of river channels, thereby ensuring accurate construction of lithofacies models of river channels under complex geological backgrounds.

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

[0006] Determine the planar distribution map corresponding to the target layer within the target area, and determine the target river area corresponding to the planar distribution map, and obtain multiple sets of sampling data corresponding to the target river area;

[0007] Based on multiple sets of sampling data and variation functions 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 bar of the target river channel area, the secondary range value corresponding to the target river channel area is determined.

[0008] The stratigraphic model and variable range direction corresponding to the target area are determined, and based on the stratigraphic 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.

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

[0010] The sampling module is used to determine the planar distribution map corresponding to the target layer within the target area, and to determine the target river area corresponding to the planar distribution map, and to obtain multiple sets of sampling data corresponding to the target river area;

[0011] The range value determination module is used to determine the main range value corresponding to the target river channel area based on multiple sets of sampling data and the variation function corresponding to the target river channel area, and to determine the secondary range value corresponding to the target river channel area based on the main range value and the size information of the inner bar of the target river channel area.

[0012] The lithofacies model determination module is used to determine the stratigraphic model and variable range direction corresponding to the target area, and to determine the lithofacies model of the river channel area corresponding to the target layer based on the stratigraphic 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, the electronic device 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 that can be executed by the at least one processor, which enables the at least one processor to perform the method for constructing a lithofacies model of a river channel under conditions with few wells 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, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for constructing a lithofacies model of a river channel area under conditions with few wells as described in any embodiment of the present invention.

[0018] The technical solution of this invention determines the planar distribution map corresponding to the target layer within the target area, and identifies the target channel region corresponding to the planar distribution map. Multiple sets of sampling data corresponding to the target channel region are obtained, providing a data foundation for subsequent variogram analysis and lithofacies modeling. Based on the multiple sets of sampling data and variogram corresponding to the target channel region, the principal range value corresponding to the target channel region is determined. Based on the principal range value and the size information of the incenter of the target channel region, the secondary range value corresponding to the target channel region is determined, which can accurately describe the anisotropic characteristics of channel sedimentation. The stratigraphic model and variable range direction corresponding to the target region are determined. Based on the stratigraphic model, the principal range value, the secondary range value, and the variable range direction, the lithofacies model of the channel region corresponding to the target layer is determined, improving the efficiency and flexibility of lithofacies model construction. By using sampled data and bar size, the range parameters can be made more reliable. Then, based on the primary and secondary range values ​​and directions, the linear characteristics of channel sedimentation can be quantified, enabling the accurate construction of lithofacies models of channel areas under conditions with few wells. This greatly improves the construction, operability, and flexibility of lithofacies models of channel areas, thereby ensuring the accurate construction of lithofacies models of channel areas under complex geological backgrounds.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for constructing a lithofacies model of a river channel area under conditions of few wells, according to Embodiment 1 of the present invention;

[0022] Figure 2 An example diagram of a main range value fitting result according to Embodiment 1 of the present invention;

[0023] Figure 3 An example diagram illustrating the determination of the final main range value according to Embodiment 1 of the present invention;

[0024] Figure 4 An example diagram of a variable-range direction in a river area according to Embodiment 1 of the present invention;

[0025] Figure 5This is a flowchart of a method for constructing a lithofacies model of a river channel area under conditions with few wells, according to Embodiment 2 of the present invention;

[0026] Figure 6 This is an example diagram of a planar property diagram according to Embodiment 2 of the present invention;

[0027] Figure 7 This is an example diagram of a planar layout according to Embodiment 2 of the present invention;

[0028] Figure 8 This is an example diagram of a gridded planar distribution map of a river area according to Embodiment 2 of the present invention;

[0029] Figure 9 This is a schematic diagram of a seismic attribute sampling result according to Embodiment 2 of the present invention;

[0030] Figure 10 This is an example diagram of a set of sampling data along the flow line of a river, according to Embodiment 2 of the present invention;

[0031] Figure 11 This is a schematic diagram of a device for constructing a lithofacies model of a river channel area under conditions with few wells, according to Embodiment 3 of the present invention.

[0032] Figure 12 This is a schematic diagram of the structure of an electronic device for implementing the method of constructing a lithofacies model of a river channel area under the condition of few wells, as described in this embodiment of the invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] Example 1

[0036] Figure 1 This invention provides a flowchart of a method for constructing a lithofacies model of a river channel area under conditions of few wells, as described in Embodiment 1 of the present invention. This embodiment is applicable to the construction of lithofacies models of river channels under conditions of few wells. The method can be executed by a lithofacies model construction device for river channels under conditions of few wells, which can be implemented in hardware and / or software. This lithofacies model construction device for river channels under conditions of few wells can be configured in an electronic device. Figure 1 As shown, the method includes:

[0037] S110. Determine the planar distribution map corresponding to the target layer within the target area, and determine the target river area corresponding to the planar distribution map, and obtain multiple sets of sampling data corresponding to the target river area.

[0038] The target area can refer to the region encompassing the river channel and its adjacent floodplain. The target layer can refer to a specific stratigraphic unit within the target area that requires focused study; it may correspond to a sedimentary body formed during a particular geological period (such as channel sand bodies or delta front facies) and possess unique lithology, physical properties, or hydrocarbon potential. The planar distribution map can refer to a contour map or probability distribution map reflecting the horizontal distribution characteristics of the target layer within the target area (such as thickness, lithological proportions, seismic attributes like physical parameters), representing a projection of a three-dimensional geological body onto a two-dimensional plane. The target channel region can refer to a sub-region further delineated from the target layer that possesses clear channel sedimentary characteristics (such as continuous sand bodies, developed core bars, or point bars), serving as the core object for subsequent lithofacies modeling or reservoir evaluation. Sampling data can refer to the horizontal distribution characteristics of the target channel region extracted from the planar distribution map.

[0039] Specifically, under conditions of limited wells, well logging data and seismic data from various wells (exploration wells) within the target area are used, combined with well-seismic calibration operations, to create a planar distribution map of the target layer (such as a channel sedimentary layer). This map represents the planar distribution of the target layer within the target area. Well logging data refers to the depth-parameter continuous curve obtained by directly measuring formation physical parameters (such as electrical, acoustic, and radioactive properties) using downhole instruments, reflecting the vertical changes in the formation around the wellbore. Seismic data refers to the three-dimensional time-space domain data volume formed after processing the physical signals of artificially generated seismic waves and recording their propagation, reflection, and refraction within the formation. Based on the horizontal distribution characteristics corresponding to the planar distribution map, the channel boundary is calibrated, the target channel area corresponding to the planar distribution map is determined, and data sampling is performed along the river flow direction in the target channel area of ​​the planar distribution map to obtain multiple sets of sampling data corresponding to the target channel area. This improves the accuracy of data acquisition and provides a basic input for subsequent variogram analysis and lithofacies modeling.

[0040] S120. Based on multiple sets of sampled 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 bar of the target river channel area, determine the secondary range value corresponding to the target river channel area.

[0041] In geostatistics, the variogram is a core tool for describing the variability of spatial variables, used to quantify the spatial correlation of seismic attributes (such as lithology and porosity). The principal range value refers to the maximum distance at which the correlation of spatial variables disappears in the principal direction (e.g., river channel direction). A mid-channel bar refers to a sandy shoal in the middle of a riverbed in a fluvial sedimentary system, formed by complex circulation. Size information refers to specific parameters describing the geometric characteristics of a geological body, including length, width, and thickness. The secondary range value refers to the maximum distance at which the correlation of spatial variables disappears in a secondary direction (e.g., perpendicular to the river channel), and is usually smaller than the principal range value.

[0042] Specifically, the variogram function is calculated based on multiple sets of sampling data corresponding to the target channel area to determine the principal range value for that area. This principal range value is then combined with the dimensional information of the in-shoal within the target channel area (such as the major and minor axes) to obtain the secondary range value, which is the range value perpendicular to the main channel direction. The secondary range value can compensate for the isotropic assumption of a single principal range, thus more accurately describing the anisotropic characteristics of channel sedimentation.

[0043] For example, the step size adjustment of each set of sampling data and the variogram corresponding to the target river area in S120 can include: adjusting the step size of each set of sampling data to determine the preset number of step size adjustment sampling data corresponding to each set of sampling data, and determining the coefficient of variation corresponding to each set of step size adjustment sampling data based on the variogram formula; determining the candidate main range value corresponding to each set of sampling data based on the coefficient of variation and the preset fitting model, and performing data fitting on the candidate main range value to determine the main range value corresponding to the target river area.

[0044] The preset number of groups refers to the number of new sampling data sets obtained after multiple step size adjustments to a certain set of sampling data. Step-size adjusted sampling data refers to the new sampling data obtained after adjusting the step size of a certain set of sampling data. The coefficient of variation is an indicator used in variogram analysis to quantify the microscale randomness of data. The preset fitting model is a mathematical function in geostatistics used to describe the spatial variability of regional variables (such as lithology, porosity, permeability, etc.). Its core function 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 could be a spherical theory model. The candidate principal range value refers to the principal range value extracted from the same set of sampling data using the preset fitting model.

[0045] Specifically, step size adjustment can use the interval between adjacent data points in the sampling data (such as the distance between adjacent sampling points) as the initial step size. The initial step size can be adjusted to an integer multiple, and sampling data is extracted from the set of sampling data at intervals with the adjusted step size. This yields a set of step-size adjusted sampling data corresponding to the adjusted step size. Based on different step size intervals, a preset number of step-size adjusted sampling data sets corresponding to this set of sampling data are obtained. Each set of step-size adjusted sampling data is input into the variogram formula to obtain the coefficient of variation for each set. Multiple coefficients of variation for each set of sampling data are fitted using a preset fitting model to obtain candidate principal range values ​​for each set of sampling data. The mode among the candidate principal range values ​​is determined as the principal range value corresponding to the target channel area. Alternatively, the mode of the nearest integer among each candidate principal range value can be determined as the principal range value corresponding to the target 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 principal range value and ensuring that the principal range value conforms to sedimentary patterns.

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

[0047]

[0048] Where, x iZ(x) represents the coordinates of the k-th observation point. i ),Z(x i +h) are respectively x i and x i The observation values ​​at points +h; h is the distance between the two observation points; N(h) is the number of data pairs separated by a distance h; y * (h) represents the value of the coefficient of variation.

[0049] The sampling data is adjusted according to the step size of each group. Following the above calculation formula, substituting the different step sizes (interval between adjacent sampling points) yields the corresponding coefficients of variation γ*(0), γ*(1), γ*(2)... for that group. Then, the spherical theoretical model is used for fitting to obtain the characteristic values ​​of the main range of that group (e.g., ...). Figure 2 (As shown).

[0050] Calculate the main range characteristic value for each group in direction I, and statistically analyze these I characteristic values. The mode can be taken as the final main range value (e.g., ...). Figure 3 (As shown).

[0051] S130. Determine the stratigraphic model and variable range direction corresponding to the target area, and based on the stratigraphic model, main range value, secondary range value and variable range direction, determine the lithofacies model of the river channel area corresponding to the target layer.

[0052] Among these, 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 range variation at different locations within the target area. The river channel area 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 data and seismic data from various well points within the target area can be used to construct a stratigraphic model corresponding to the target layer, and a variable range direction can be defined based on the flow direction or river course orientation corresponding to the target river region. Based on the stratigraphic model, primary range values, secondary range values, and variable range direction, a sequential indicator stochastic simulation method can be used to generate lithofacies distributions (such as river channels and floodplains), establishing a lithofacies model of the river region corresponding to the target layer, greatly improving the efficiency and flexibility of lithofacies model construction.

[0054] For example, S130 may include: determining the stratigraphic model corresponding to the target area, filling the stratigraphic model with data based on the lithofacies curves of each well point, determining the spatial distribution information of lithofacies within the target layer of the target area, and determining the variable range direction corresponding to each location within the target channel area based on a preset reference direction; and determining the lithofacies model of the channel area corresponding to the target layer based on the stratigraphic model after data filling, the main range value, the secondary range value, the spatial distribution information, and the variable range direction.

[0055] Here, the lithofacies curve can refer to a continuous record describing the vertical variation of lithofacies types (such as sandstone, mudstone, siltstone, etc.) with depth in a single well. Spatial distribution information can refer to the overall distribution ratio of each lithofacies type within the target layer and the distribution ratio of lithofacies at various locations vertically. The preset reference direction can refer to a direction with a pre-set angle of zero degrees.

[0056] Specifically, a stratigraphic model corresponding to the target area can be built using 3D visualization modeling software (such as Petrel). Alternatively, well logging data can be used as control points, with the top surface structure traced by fine geophysical tracking as the inter-well trend. A 3D structural model (stratigraphic model) of the study area can be generated through interpolation, taking into account the thickness of interlayers and sand bodies, and setting the vertical grid size. The lithofacies curves of each well point are then filled into the stratigraphic model, and the filled model is analyzed to obtain the 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 flow direction 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 filled stratigraphic model, the main range value, the secondary range value, and the variable range direction, the spatial distribution information is used as a spatial constraint. Using a preset modeling method (such as sequential indicator stochastic simulation), a lithofacies model of the channel area corresponding to the target layer is established, adapting to the geological background of the channel and making the model more consistent with actual sedimentary patterns.

[0057] For example, data filling is performed on the stratigraphic model based on the lithofacies curves of each well point to determine the spatial distribution information of lithofacies within the target layer of the target area. This includes: filling the lithofacies curves of each well point into the stratigraphic model based on the corresponding positions of each well point in the stratigraphic model, and analyzing the spatial distribution pattern of each lithofacies in the stratigraphic model after data filling to determine the horizontal and vertical distribution information of lithofacies within the target layer.

[0058] Horizontal distribution information refers to the proportion of each lithofacies within the target layer as a whole. Vertical distribution information refers to the proportion of the target layer at various locations (depths) in the vertical direction.

[0059] Specifically, based on the intersection of the drilling trajectory corresponding to each well point and the layer interface of the stratigraphic model, the lithofacies curves (such as sandstone, mudstone, siltstone, etc.) of each well are matched to the corresponding layer (depth). For example, if the depth segment of the well point at layer 1 is "interbedded sandstone-mudstone", then the lithofacies curve of that segment is filled into the corresponding grid in the model, and the stratigraphic model can be divided into a three-dimensional grid (such as 50m×50m×1m). The trends and patterns of the spatial distribution 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] For example, determining the variable range direction corresponding to each location within the target river channel area based on a preset reference direction includes: determining the deviation angle between the river flow direction and the preset reference direction at each location within the target river channel area in a clockwise direction based on the preset reference direction, and determining the deviation angle as the variable range direction corresponding to each location within the target river channel area.

[0061] Specifically, the preset reference direction can be an angle in a mathematical coordinate system (e.g., 0° = due north, 90° = due east). Different sections of the river channel have different streamline directions. Taking due north as 0°, the angle (angle value, not radian value) of the streamline direction deviating from due north at each location is calculated. The calculated deviation angle is directly used as the variable range direction at that location, which is then used for the variogram direction in subsequent lithofacies modeling. This ensures that the spatial correlation direction of lithofacies (e.g., channel sand bodies) is consistent with the actual river channel extension direction. For example... Figure 4 As shown, different variable range directions can also be displayed differently based on the size of the deflection angle, generating a variable range direction map of the river channel, which makes it easier to intuitively display the variable range direction of the target river channel area.

[0062] In this embodiment, by determining the planar distribution map corresponding to the target layer within the target area and the target channel region corresponding to the planar distribution map, multiple sets of sampling data corresponding to the target channel region are obtained, providing a data foundation for subsequent variogram analysis and lithofacies modeling. Based on the multiple sets of sampling data and variogram corresponding to the target channel region, the principal range value corresponding to the target channel region is determined. Based on the principal range value and the size information of the incenter bar in the target channel region, the secondary range value corresponding to the target channel region is determined, which can accurately describe the anisotropic characteristics of channel sedimentation. The stratigraphic model and variable range direction corresponding to the target region are determined. Based on the stratigraphic model, principal range value, secondary range value, and variable range direction, the lithofacies model of the channel region corresponding to the target layer is determined, improving the efficiency and flexibility of lithofacies model construction. By using sampled data and bar size, the range parameters can be made more reliable. Then, based on the primary and secondary range values ​​and directions, the linear characteristics of channel sedimentation can be quantified, enabling the accurate construction of lithofacies models of channel areas under conditions with few wells. This greatly improves the construction, operability, and flexibility of lithofacies models of channel areas, thereby ensuring the accurate construction of lithofacies models of channel areas under complex geological backgrounds.

[0063] Example 2

[0064] Figure 5This is a flowchart of a method for constructing a lithofacies model of a river channel area under conditions with few wells, provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment optimizes the step of "determining the planar distribution map corresponding to the target layer within the target area, determining the target river channel area corresponding to the planar distribution map, and obtaining multiple sets of sampling data corresponding to the target river channel area". Explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0065] See Figure 5 The alternative method for constructing a lithofacies model of a river channel area under conditions of few wells provided in this embodiment specifically includes the following steps:

[0066] S210. Based on the thickness of sand bodies encountered at each well point and the phase information of the top and bottom of the well points in the target layer within the target area, determine the planar distribution map corresponding to the target layer within the target area. The target layer is the stratum formed in the river area within 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 formation, determined through well logging interpretation or core analysis at a specific well point. The top and bottom phase information of the well point can refer to the top and bottom depths of the target formation and the type of sequence boundary at the well point, used to determine the spatial location of the formation and its sedimentary evolution relationship.

[0068] Specifically, sandstone sections are identified from well logging curves, and the cumulative thickness of the sand body in the target layer (channel sedimentary strata) at each well point is calculated. Through seismic interpretation, the top and bottom phase information (such as crests, troughs, or zero phase) of the target layer at each well point is determined for stratigraphic calibration. Planar attribute extraction of the target layer is then performed, and by statistically analyzing the attribute values ​​at each well point, a planar distribution map corresponding to the target layer is drawn. The top and bottom phase information ensures accurate stratigraphic calibration and avoids distortion of the distribution map caused by stratigraphic crossing time.

[0069] For example, S210 may include: determining the layer structure surface corresponding to the target layer in the target area based on the top and bottom phase information of the target layer corresponding to each well point in the target area, extracting the planar attributes of the layer structure surface, and determining the planar attribute information corresponding to the target layer; determining the target planar attribute corresponding to the target layer based on the thickness of the sand body encountered by each well point in the target area and the planar attribute information, and generating a planar distribution map corresponding to the target layer based on the target planar attribute.

[0070] Among these, the stratigraphic plane refers to the continuous distribution interface of the same geological stratum in three-dimensional space within the target area, reflecting the top or bottom morphology of that stratum (such as the top surface of a channel sand body or the bottom surface of a coal seam). Planar attribute information refers to seismic attribute information extracted from the stratigraphic plane, used to describe structural morphology, sedimentary characteristics, or physical property distribution patterns. For example, planar attribute information can include multiple attributes such as minimum amplitude, maximum amplitude, sum of total troughs, root mean square, and maximum. Target attribute information refers to the seismic attribute among the planar attribute information that has the strongest correlation with the thickness of the sand body encountered at the well point.

[0071] Specifically, based on well-seismic calibration, the peaks, troughs, or zero phases corresponding to the top and bottom of the target layer sand body can be identified, i.e., the phase information of the well point's top and bottom, thus constructing the stratigraphic surface corresponding to the target layer within the target area. Planar attributes are extracted from the stratigraphic surface to obtain the planar attribute information corresponding to the target layer, including minimum amplitude, maximum amplitude, sum of total troughs, root mean square, and other attributes. Combining the thickness of the sand body encountered at the well point with the planar attribute information, the seismic attribute with the strongest correlation to the thickness of the sand body encountered at the well point is determined as the target planar attribute corresponding to the target layer. The magnitude of the target planar attribute value can indicate the thickness of the sand body and further characterize the planar distribution morphology of the target layer, i.e., the planar distribution map corresponding to the target layer, thereby improving image accuracy.

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

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

[0074] For example, generating a planar distribution map corresponding to the target layer based on the target planar attributes includes: displaying the target planar attributes corresponding to each location within the target layer in a differentiated manner according to numerical differences, and generating a planar attribute map corresponding to the target layer; and determining the river area within the target area based on the planar attribute map, and generating a planar distribution map corresponding to the target layer.

[0075] Among them, a planar attribute map can refer to a visual image generated by projecting the target planar attributes at various locations of the target layer onto a two-dimensional plane.

[0076] Specifically, the target plane attributes at different locations within the target layer can be displayed differently based on their numerical values. This allows for a more intuitive reflection of attribute changes at each location within the target layer (e.g., the larger the attribute value, the darker the color), generating a plane attribute map corresponding to the target layer. For example, the target plane attribute could be the minimum amplitude, such as... Figure 6 As shown, this is the planar property map when the target plane property is at its minimum amplitude. Based on the planar property map, as... Figure 7 As shown, the magnitude of the attribute value can indicate the thickness of the sand body, thereby characterizing the channel boundary and generating a planar distribution map corresponding to the target layer.

[0077] S220. Determine the target river area corresponding to the planar layout map, and sample the target river area based on the preset sampling interval along the river flow direction and the preset number of samples perpendicular to the river flow direction to obtain multiple sets of sampling data corresponding to the target river area.

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

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

[0080] For example, such as Figure 8 As shown, the planar distribution map can be gridded. The direction of the river channel streamline (river flow direction) is defined as direction I, and the direction perpendicular to the river channel streamline is defined as direction J. The I and J directions are always orthogonal. The direction I will show different directions depending on the location of the river channel, but it always represents the main flow direction of that section of the river. 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), and the J direction is sampled according to the preset number of samples. Each sampling node obtains one attribute data point, achieving uniform data sampling in the I and J directions, and obtaining seismic attribute sampling results (sampled data). The sampling results are as follows. Figure 9As shown, seismic attribute discretization is achieved. It should be noted that the sampling data obtained based on the preset sampling interval and preset number of samples needs to retain the changes in seismic attributes. This avoids the discretization results becoming blurred and failing to reflect seismic attribute information due to excessively large grid settings, while also avoiding excessively small grid settings that lead to a surge in data volume and excessive computational load. Grids with the same I number are extracted according to their grid number, and then arranged according to their J number in ascending order to obtain a set of data representing the direction along the river channel flow (e.g., ...). Figure 10 As shown in the figure, each set of data is used for subsequent analysis.

[0081] S230. Based on multiple sets of sampled 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 bar of the target river channel area, determine the secondary range value corresponding to the target river channel area.

[0082] S240. Determine the stratigraphic model and variable range direction corresponding to the target area, and based on the stratigraphic model, main range value, secondary range value and variable range direction, determine the lithofacies model of the river channel area corresponding to the target layer.

[0083] The technical solution of this embodiment is successful. This invention determines the planar distribution map of the target layer within the target area based on the thickness of sand bodies encountered at each well point and the top and bottom phase information of the well points. This achieves accurate construction of the planar distribution morphology of the target layer, providing a foundation for subsequent data sampling. The target channel region corresponding to the planar distribution map is determined, and sampling is performed on the target channel region based on a preset sampling interval along the channel flow direction and a preset number of samples perpendicular to the channel flow direction, obtaining multiple sets of sampling data corresponding to the target channel region, thus achieving multi-dimensional data acquisition of the channel region. Through the planar distribution map, the channel region can be accurately depicted. Further multi-dimensional data acquisition of the channel region in conjunction with the channel flow direction can provide comprehensive input for subsequent lithofacies modeling and reservoir evaluation. It can be extended to other sedimentary systems, exhibiting strong scalability and operability.

[0084] Example 3

[0085] Figure 11 This is a schematic diagram of a device for constructing a lithofacies model of a river channel area under conditions with few wells, provided in Embodiment 3 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 planar distribution map corresponding to the target layer within the target area, and to determine the target river area corresponding to the planar distribution map, and to obtain multiple sets of sampling data corresponding to the target river area.

[0087] The range value determination module is used to determine the main range value corresponding to the target river channel area based on multiple sets of sampling data and the variation function corresponding to the target river channel area, and to determine the secondary range value corresponding to the target river channel area based on the main range value and the size information of the inner bar of the target river channel area.

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

[0089] In this embodiment, by determining the planar distribution map corresponding to the target layer within the target area and identifying the target channel region corresponding to the planar distribution map, multiple sets of sampling data corresponding to the target channel region are obtained, providing a data foundation for subsequent variogram analysis and lithofacies modeling. Based on the multiple sets of sampling data and variogram corresponding to the target channel region, the principal range value corresponding to the target channel region is determined, and based on the principal range value and the size information of the incenter of the target channel region, the secondary range value corresponding to the target channel region is determined, which can accurately describe the anisotropic characteristics of channel sedimentation. The stratigraphic model and variable range direction corresponding to the target region are determined, and based on the stratigraphic model, the principal range value, the secondary range value, and the variable range direction, the lithofacies model of the channel region corresponding to the target layer is determined, improving the efficiency and flexibility of lithofacies model construction. By using sampled data and bar size, the range parameters can be made more reliable. Then, based on the primary and secondary range values ​​and directions, the linear characteristics of channel sedimentation can be quantified, enabling the accurate construction of lithofacies models of channel areas under conditions with few wells. This greatly improves the construction, operability, and flexibility of lithofacies models of channel areas, thereby ensuring the accurate construction of lithofacies models of channel areas under complex geological backgrounds.

[0090] Optionally, the sampling module 310 includes:

[0091] The distribution map determination unit is used to determine the planar distribution map corresponding to the target layer in the target area based on the thickness of the sand body encountered at each well point and the phase information of the top and bottom of the well points. The target layer is the stratum formed in the river area of ​​the target area during a preset historical period.

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

[0093] Optionally, the layout determination unit includes:

[0094] The 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 target layer corresponding to each well point in the target area, and to extract the planar attributes of the layer structure surface to determine the planar attribute information corresponding to the target layer.

[0095] The deployment map 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 each well point in the target area and the plane attribute information, and to generate the plane deployment map corresponding to the target layer based on the target plane attributes.

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

[0097] Optionally, the layout map determining sub-unit is specifically used for: displaying the target plane attributes corresponding to each position within the target layer in a differentiated manner according to numerical differences, generating a plane attribute map corresponding to the target layer; and determining the river area within the target area based on the plane attribute map, generating a planar layout map corresponding to the target layer.

[0098] Optionally, the range value determination module 320 is specifically used for: adjusting the step size of each group of sampled data, determining the preset number of step-size adjusted sampled data corresponding to each group of sampled data, and determining the coefficient of variation corresponding to each group of step-size adjusted sampled data based on the variogram formula; determining the candidate main range value corresponding to each group of sampled data based on the coefficient of variation and the preset fitting model, and performing 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] The variable range direction determination unit is used to determine the stratigraphic model corresponding to the target area, fill the stratigraphic model with data based on the lithofacies curves of each well point, determine the spatial distribution information of lithofacies within the target layer of the target area, and determine the variable range direction corresponding to each position within the target channel area based on a preset reference direction.

[0101] The lithofacies model construction unit is used to determine the lithofacies model of the river region corresponding to the target layer based on the data-filled layer structure model, the main range value, the secondary range value, the spatial distribution information, and the variable range direction.

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

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

[0104] The above-described device can execute the method for constructing lithofacies models of river channels under conditions with few wells provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method for constructing lithofacies models of river channels under conditions with few wells.

[0105] Example 4

[0106] Figure 12 This is a schematic diagram of the electronic device used to implement the method for constructing a lithofacies model of a river channel area under conditions with few wells, as described in this embodiment of the invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

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

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

[0109] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose 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 performs the various methods and processes described above, such as the method for constructing lithofacies models of river channels under conditions with few wells.

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

[0111] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0112] Various embodiments of the systems and techniques described above 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), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] Computer programs used to implement 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 executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

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

[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 provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0116] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0117] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0118] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0119] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for constructing a lithofacies model of a river channel region under a few-well condition, characterized in that, include: Based on the top and bottom phase information of the target layer corresponding to each well point in the target area, the layer structure surface corresponding to the target layer in the target area is determined, and the planar attribute is extracted from the layer structure surface to determine the planar attribute information corresponding to the target layer. Based on a preset correlation algorithm, correlation analysis is performed on the thickness of the sand body encountered by each well point in the target area and the planar attribute information. The planar attribute with the strongest correlation to the thickness of the sand body encountered by the well point is determined as the target planar attribute corresponding to the target layer. Based on the target planar attribute, a planar distribution map corresponding to the target layer is generated. The target layer is the stratum formed in the river area of ​​the target area during a preset historical period. The target river region corresponding to the planar distribution map is determined, and the target river region is sampled based on a preset sampling interval along the river flow direction and a preset number of samples perpendicular to the river flow direction to obtain multiple sets of sampling data corresponding to the target river region. The sampling data refers to the horizontal distribution characteristics of the target river region extracted from the planar distribution map. Based on multiple sets of sampling data and variation functions 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 bar of the target river channel area, the secondary range value corresponding to the target river channel area is determined. The stratigraphic model and variable range direction corresponding to the target area are determined, and based on the stratigraphic 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, wherein the variable range direction is the deflection angle of the river flow direction at each location in the target river channel area relative to a preset reference direction.

2. The method according to claim 1, characterized in that, The step of generating a planar distribution map corresponding to the target layer based on the target plane attributes includes: The target plane attributes corresponding to each position within the target layer are displayed differently according to their numerical differences, thereby generating a plane attribute map corresponding to the target layer. Based on the planar attribute map, the river areas within the target area are determined, and a planar distribution map corresponding to the target layer is generated.

3. The method according to claim 1, characterized in that, The step of determining the principal range value corresponding to the target river channel region based on multiple sets of sampled data and the variogram function includes: The step size is adjusted for each group of sampled data to determine the preset number of groups of sampled data with the step size adjustment for each group of sampled data, and the coefficient of variation corresponding to each group of sampled data with the step size adjustment is determined based on the formula of the variogram. Based on the coefficient of variation and the preset fitting model, the candidate main range value corresponding to each group of sampled data is determined, and the candidate main range value is fitted to determine the main range value corresponding to the target river area.

4. The method according to claim 1, characterized in that, The step of determining the stratigraphic model and variable range direction corresponding to the target area, and determining the lithofacies model of the channel area corresponding to the target layer based on the stratigraphic model, the main range value, the secondary range value, and the variable range direction, includes: The stratigraphic model corresponding to the target area is determined, and the stratigraphic model is filled with data based on the lithofacies curves of each well point. The spatial distribution information of lithofacies within the target layer of the target area is determined, and the variable range direction corresponding to each position in the target channel area is determined based on the preset reference direction. Based on the data-filled layer structure model, the main range value, the secondary range value, the spatial distribution information, and the variable range direction, the lithofacies model of the river region corresponding to the target layer is determined.

5. The method according to claim 4, characterized in that, The process of filling the stratigraphic model with data based on the lithofacies curves at each well point, and determining the spatial distribution information of lithofacies within the target layer of the target area, includes: Based on the corresponding positions of each well point in the stratigraphic model, the lithofacies curves of each well point are filled into the stratigraphic model, and the spatial distribution of each lithofacies in the stratigraphic model after data filling is analyzed to determine the horizontal and vertical distribution information of the lithofacies within the target layer.

6. The method according to claim 4, characterized in that, The step of determining the variable range direction corresponding to each location within the target river channel area based on a preset reference direction includes: Based on a preset reference direction, the deviation angle between the river flow direction at each location within the target river area and the preset reference direction in a clockwise direction is determined, and the deviation angle is determined as the variable range direction at each location within the target river area.

7. A device for constructing a lithofacies model of a river channel area under conditions of few wells, characterized in that, include: The attribute information determination module 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 target layer corresponding to each well point in the target area, and to extract the planar attributes of the layer structure surface to determine the planar attribute information corresponding to the target layer. The distribution map determination module is used to perform correlation analysis on the thickness of the sand body encountered by each well point in the target area and the planar attribute information based on a preset correlation algorithm, and to determine the planar attribute with the strongest correlation with the thickness of the sand body encountered by the well point as the target planar attribute of the target layer, and to generate a planar distribution map of the target layer based on the target planar attribute, wherein the target layer is the stratum formed in the river area of ​​the target area during a preset historical period; The sampling module is used to determine the target river area corresponding to the planar layout map, and to sample the target river area based on a preset sampling interval along the river flow direction and a preset number of samples perpendicular to the river flow direction to obtain multiple sets of sampling data corresponding to the target river area. The sampling data refers to the horizontal distribution characteristics of the target river area extracted from the planar layout map. The range value determination module is used to determine the main range value corresponding to the target river channel area based on multiple sets of sampling data and the variation function corresponding to the target river channel area, and to determine the secondary range value corresponding to the target river channel area based on the main range value and the size information of the inner bar of the target river channel area. The lithofacies model determination module is used to determine the stratigraphic model and variable range direction corresponding to the target area, and based on the stratigraphic model, the main range value, the secondary range value and the variable range direction, determine the lithofacies model of the river area corresponding to the target layer, wherein the variable range direction is the deflection angle of the river flow direction at each location in the target river area relative to the preset reference direction.

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