Underground two-dimensional attribute model construction method, device and equipment based on logging curve

Abstract

The invention discloses an underground two-dimensional attribute model construction method, device and equipment based on a logging curve. The method comprises the following steps: determining an interpolation result of each small logging layer according to information of each well location on the basis of a small logging layer division result of a logging curve in a target interval, and obtaining a depth curve of each small logging layer; according to the constraint range of the depth curve of the top logging small layer and the depth curve of the bottom logging small layer in the modeling network, each to-be-estimated grid node is determined; for each to-be-estimated grid node, determining a vertical depth proportionality coefficient of the to-be-estimated grid node according to a depth curve of an upper logging small layer and a lower logging small layer which are closest to the to-be-estimated grid node; according to the vertical depth proportion coefficient, determining a target logging curve value corresponding to the to-be-estimated grid node at the vertical depth proportion; and according to the target logging curve value, determining a target attribute value corresponding to the to-be-estimated grid node, and constructing an underground two-dimensional attribute model. According to the method, the underground two-dimensional attribute model can be constructed quickly and precisely on the basis of the logging curve and the logging small layer.

Description

Technical Field

[0001] The invention relates to a method, a device and equipment for constructing an underground two-dimensional attribute model based on a well logging curve. Background Art

[0002] In oil and gas exploration, it is very important to carry out reservoir evaluation based on sensitive attributes. Seismic data usually covers a wide range and has a large detection depth, and is an important guiding information for oil and gas exploration and development; its resolution is usually low and it is difficult to meet the needs of high-precision reservoir characterization. Well logging has a higher vertical resolution, and it is very important to carry out underground two-dimensional target attribute modeling based on well logging data in the case of dense well networks.

[0003] Two-dimensional geological modeling has been developed for many years. For example, the random stratigraphic modeling method is based on the theory of random functions. It obtains random results by randomly generating traversal paths or randomly generating prediction results at each grid node according to a specific probability distribution, thereby also representing modeling uncertainty. In addition, the deterministic modeling method combines the work area data and prior knowledge to obtain an algorithm-based optimal prediction result, thereby deterministically representing the underground properties. Summary of the invention

[0004] In order to better realize the construction of an underground two-dimensional attribute model, an embodiment of the present invention provides a method, device, equipment and medium for constructing an underground two-dimensional attribute model based on a well logging curve.

[0005] In a first aspect, an embodiment of the present invention provides a method for constructing a two-dimensional underground attribute model based on a well logging curve, the method comprising:

[0006] Based on the logging sub-layer division result of the logging curve in the target layer section, the interpolation result of each logging sub-layer is determined according to the well location information, and the depth curve of each logging sub-layer is obtained;

[0007] According to the constraint range of the depth curves of the top logging layer and the bottom logging layer in the modeling network, each grid node to be estimated is determined;

[0008] For each of the grid nodes to be estimated, determining the vertical depth proportional coefficient of the grid node to be estimated according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated;

[0009] According to the vertical depth ratio coefficient, determining the target logging curve value corresponding to the grid node to be estimated at the vertical depth ratio;

[0010] According to the target logging curve value, the target attribute value corresponding to the grid node to be estimated is determined, and an underground two-dimensional attribute model is constructed.

[0011] In one or some optional implementations of the embodiments of the present invention, the logging sub-layer division result based on the logging curve in the target layer section is determined according to the well location information, and the interpolation result of each logging sub-layer is obtained to obtain the depth curve of each logging sub-layer, including:

[0012] Based on the logging sub-layer division result of the logging curve in the target layer section, the depth value corresponding to each logging sub-layer is extracted at each well in the target layer section;

[0013] Based on the location information of each well in the target layer segment, the coordinates of each well are connected and straightened in a preset order to determine the new coordinates of each well on the straight line;

[0014] For each logging sublayer, interpolation processing is performed according to the depth values ​​corresponding to each well in the logging sublayer and the new coordinates on the straight line, the interpolation result of the logging sublayer is determined, and the depth curve of the logging sublayer is obtained.

[0015] In one or some optional implementations of the embodiments of the present invention, the coordinates of each well are connected and straightened according to a preset order based on the location information of each well in the target layer segment to determine the new coordinates of each well on the straight line, including:

[0016] Based on the location information of each well in the target layer, the coordinates of each well are connected and straightened in a preset order.

[0017] Formula 1, determine the new coordinates of each well on the straight line:

[0018]

[0019] In the formula, is the new coordinate of the rth well on the straight line; [x r ,y r ] is the work area coordinate of the rth well; Preset new coordinates for the first well on the line.

[0020] In one or some optional implementations of the embodiments of the present invention, performing interpolation processing on each logging sublayer according to the depth values ​​corresponding to the logging sublayer of each well and the new coordinates on the straight line to determine the interpolation result of the logging sublayer and obtain the depth curve of the logging sublayer includes:

[0021] For each logging layer, according to the depth values ​​corresponding to the wells in the logging layer and the new coordinates on the straight line, interpolation processing is performed based on the following formula 2 to determine the interpolation result of the logging layer:

[0022]

[0023] Where, L d (g,d i) represents the interpolation result of the logging layer; f(.) represents the preset interpolation algorithm; The new coordinates are The depth value corresponding to the i-th logging layer of the well; The new coordinates are The depth value of the well in the i-th logging layer; g is the current node coordinate, g∈1,...,N x , N x The number of lateral nodes of the mesh for two-dimensional modeling;

[0024] According to the interpolation result of each logging layer, the depth curve of each logging layer is obtained.

[0025] In one or some optional implementations of the embodiments of the present invention, for each of the grid nodes to be estimated, determining the vertical depth proportional coefficient of the grid node to be estimated according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated includes:

[0026] For each of the grid nodes to be estimated, the vertical depth proportional coefficient of the grid node to be estimated is determined based on the following formula 3 according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated:

[0027]

[0028] Where Ratio(x,z) is the vertical depth ratio coefficient corresponding to the depth value of the grid node to be estimated; d (x, z) is the depth value of the grid node to be estimated; is the depth value of the upper logging layer closest to the grid node to be estimated; is the depth value of the nearest lower logging layer of the grid node to be estimated.

[0029] In one or some optional implementations of the embodiment of the present invention, determining the target logging curve value corresponding to the vertical depth ratio of the grid node to be estimated according to the vertical depth ratio coefficient includes:

[0030] For each grid node to be estimated, determining the target attribute value of the grid node to be estimated at the corresponding vertical depth ratio of each well according to the vertical depth ratio coefficient;

[0031] According to the target attribute value at the corresponding vertical depth ratio of each well, the target logging curve value corresponding to the vertical depth ratio of the grid node to be estimated is determined.

[0032] In one or some optional implementations of the embodiments of the present invention, determining the target attribute value corresponding to the grid node to be estimated according to the target logging curve value and constructing the underground two-dimensional attribute model includes:

[0033] For each of the grid nodes to be estimated, the target attribute value corresponding to the grid node to be estimated is determined according to the target logging curve value based on the following formula 4:

[0034]

[0035] In the formula, Grid attr (x,z) is the target attribute value corresponding to the grid node to be estimated; is the target attribute value corresponding to the rth well; S r is the target attribute value corresponding to the grid node to be estimated and the rth well The corresponding distance at the target attribute value of each well; S is the sum of the reciprocals of the corresponding distances between the grid node to be estimated and the target attribute value of each well, that is,

[0036] According to the target attribute values ​​corresponding to all the grid nodes to be estimated, a two-dimensional underground attribute model is constructed.

[0037] In a second aspect, an embodiment of the present invention provides a device for constructing a two-dimensional underground attribute model based on a well logging curve, the device comprising:

[0038] An interpolation module is used to determine the interpolation result of each logging layer based on the logging layer division result of the logging curve in the target layer section and according to the well location information, so as to obtain the depth curve of each logging layer;

[0039] A constraint module, used to determine each grid node to be estimated according to the constraint range of the depth curves of the top logging layer and the bottom logging layer in the modeling network;

[0040] A first determination module is used to determine, for each of the grid nodes to be estimated, a vertical depth proportional coefficient of the grid node to be estimated according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated;

[0041] A second determination module is used to determine the target logging curve value corresponding to the to-be-estimated grid node at the vertical depth ratio according to the vertical depth ratio coefficient;

[0042] The third determination module is used to determine the target attribute value corresponding to the grid node to be estimated according to the target logging curve value, and construct an underground two-dimensional attribute model.

[0043] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a two-dimensional underground attribute model based on well logging curves as described above.

[0044] In a fourth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for constructing a two-dimensional underground attribute model based on logging curves as described above is implemented.

[0045] In a fifth aspect, an embodiment of the present invention provides a computer program product comprising instructions, which, when executed on a computer device, enables the computer device to execute the method for constructing a two-dimensional underground attribute model based on well logging curves as described above.

[0046] In a sixth aspect, an embodiment of the present invention provides a chip, the chip including a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run a computer program or instruction to implement the method for constructing an underground two-dimensional attribute model based on logging curves as described above.

[0047] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0048] The method for constructing an underground two-dimensional attribute model based on logging curves provided in an embodiment of the present invention combines the logging curves of the target attributes and the logging sublayer division results, performs interpolation processing based on the logging sublayer, obtains the depth curve distribution characteristics of the logging sublayer, and then calculates the vertical proportional relationship between the upper and lower layers of each grid node to be estimated in the model space based on the depth curve of the above-mentioned logging sublayer, and interpolates the target attribute values ​​at the corresponding vertical proportional positions of each conditional well, and predicts the target attribute value of each grid node to be estimated in the modeling grid to complete the modeling. This method does not require the constraints of other information such as seismic data, which is conducive to the rapid analysis and evaluation of underground geological conditions. It can quickly and accurately construct underground two-dimensional attribute models based on logging curves and logging layers, and has important production application value. By calculating the vertical proportional relationship between each node to be estimated between specific layers in the model space, and then interpolating the target attribute values ​​at the corresponding vertical proportional positions of each conditional well, it is more in line with the isochronous characteristics of stratigraphic deposition, the predicted attribute changes are stable and the calculation efficiency is higher, providing important guiding information for oil and gas resource exploration, and the algorithm is simple and easy to operate, with high operation efficiency and easy to promote and apply.

[0049] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0050] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0052] Figure 1 A schematic diagram of the steps of a method for constructing a two-dimensional underground attribute model based on well logging curves provided in an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of well location information of three wells in a certain work area provided by an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of the acoustic time difference curves and logging sub-layer division results of three wells in a certain work area provided by an embodiment of the present invention;

[0055] Figure 4 A schematic diagram of the distribution prediction of seven logging sub-layers of three wells in a certain work area in a two-dimensional modeling space provided by an embodiment of the present invention;

[0056] Figure 5 A schematic diagram of acoustic wave time difference properties predicted in a three-dimensional modeling space based on well location distribution, logging curves and logging layer interpolation results of a certain work area provided by an embodiment of the present invention;

[0057] Figure 6 A schematic diagram of comparison between predicted acoustic time difference properties and actual acoustic time difference logging curves extracted from three wells in a certain work area provided by an embodiment of the present invention;

[0058] Figure 7 A schematic diagram of the structure of a device for constructing a two-dimensional underground attribute model based on well logging curves provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0060] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0061] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0062] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0063] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0064] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0065] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0066] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0067] The inventors found that in the prior art, the traditional underground two-dimensional attribute modeling based on well logging mainly has the problems of low accuracy and efficiency, and does not consider the vertical proportional relationship between the grid nodes in the layers, which easily leads to the occurrence of time-delay in the predicted attributes, thereby reducing the prediction accuracy; the constraint effect of the well logging layer in the two-dimensional modeling space is not fully considered, and the modeling accuracy and efficiency are poor; other data constraints such as seismic data are required, which is not conducive to the rapid analysis and evaluation of underground geological conditions, and does not meet the inventor's expectations. Based on this, the inventors have made the present invention after further research and development, providing a method, device and equipment for constructing an underground two-dimensional attribute model based on well logging curves.

[0068] Embodiment 1

[0069] The embodiment of the present invention provides a method for constructing a two-dimensional underground attribute model based on a well logging curve. Figure 1 As shown, the method includes:

[0070] S101: Based on the logging sub-layer division result of the logging curve in the target layer section, the interpolation result of each logging sub-layer is determined according to the well location information, and the depth curve of each logging sub-layer is obtained.

[0071] In the above step S101, the logging sub-layer division result based on the logging curve in the target layer section is determined according to the well location information, and the interpolation result of each logging sub-layer is obtained to obtain the depth curve of each logging sub-layer, which specifically includes:

[0072] Based on the logging sub-layer division result of the logging curve in the target layer section, the depth value corresponding to each logging sub-layer is extracted at each well in the target layer section;

[0073] Based on the location information of each well in the target layer segment, the coordinates of each well are connected and straightened in a preset order to determine the new coordinates of each well on the straight line;

[0074] For each logging sublayer, interpolation processing is performed according to the depth values ​​corresponding to each well in the logging sublayer and the new coordinates on the straight line, the interpolation result of the logging sublayer is determined, and the depth curve of the logging sublayer is obtained.

[0075] In an embodiment of the present invention, for the well logging data collected in the work area, combined with the geological knowledge of the work area and the results of comprehensive data analysis, well logging sub-layer division is carried out in the target layer based on the well logging curve, and the well logging sub-layer division results of each well in the target layer are obtained. Among them, the spacing of the well logging sub-layer division should comprehensively balance the production needs and the modeling grid constraints. If the interval between the well logging sub-layers is too large, the modeling results cannot accurately characterize the property change law in the target layer; if the interval between the well logging sub-layers is too small, it is necessary to increase the vertical node density of the two-dimensional grid, thereby greatly increasing the amount of calculation and reducing the calculation efficiency. The well logging sub-layer is denoted as L(w,d), where w represents the well name and d is the depth of the corresponding well logging sub-layer. For the same well w, multiple sub-layer division results can be expressed as L(w,d i )(i=1,2,...,N), N is the number of logging small layers.

[0076] Based on the above logging sub-layer division results, interpolation and extrapolation processing are carried out to convert the logging sub-layer information L(w r ,d i )(r=1,2,...,R, R is the number of conditional wells, i=1,2,...,N) to construct the corresponding N depth curves. The specific interpolation processing method is as follows:

[0077] For each logging layer L(w,d i )(i=1,2,...,N), first extract the depth value D corresponding to the logging layer at each conditional well point i , as shown in the following formula 5:

[0078]

[0079] Where D i is the set of depth values ​​corresponding to the i-th logging layer at each well; is the depth value corresponding to the i-th logging layer at the R-th well; i=1,...,N.

[0080] After extracting the depth value corresponding to each logging layer at each well in the target layer section, the interpolation result of the logging layer can be constructed by combining the well location information. Among them, the well location information, that is, the work area coordinates of each well, are shown in the following formula 6:

[0081] Coord R ={[x1,y1],[x2,y2],...,[x R ,y R ]}Formula 6;

[0082] Where Coord R is the set of work area coordinates of each well; [x r ,y r](r=1,...,R) is the work area coordinate of the rth well.

[0083] In the embodiment of the present invention, the coordinates of each well are connected and straightened according to a preset order based on the location information of each well in the target layer segment to determine the new coordinates of each well on the straight line, which specifically includes:

[0084] Based on the location information of each well in the target layer, the coordinates of each well are connected and straightened in a preset order.

[0085] Formula 1, determine the new coordinates of each well on the straight line:

[0086]

[0087] In the formula, is the new coordinate of the rth well on the straight line; [x r ,y r ] is the work area coordinate of the rth well; The preset new coordinates of the first well on the straight line; sqrt(.) means taking the square root.

[0088] In the embodiment of the present invention, after the work area coordinates of each well are collected, the coordinates of each well are connected and straightened according to a preset order, and the coordinates of each well on the newly constructed straight line are recalculated. The coordinates of the first well are set as Then the coordinates corresponding to the rth port are As shown in the above formula 1.

[0089] The preset order can be determined according to the plane position relationship of each well, such as Figure 2 As shown, the well location information of three wells W4, W401 and W402 in a certain work area, and the dotted line is the connecting line of the three wells in the plane.

[0090] In the embodiment of the present invention, the interpolation processing is performed on each logging sublayer according to the depth value corresponding to each well in the logging sublayer and the new coordinates on the straight line to determine the interpolation result of the logging sublayer and obtain the depth curve of the logging sublayer, specifically including:

[0091] For each logging layer, according to the depth values ​​corresponding to the wells in the logging layer and the new coordinates on the straight line, interpolation processing is performed based on the following formula 2 to determine the interpolation result of the logging layer:

[0092] L d (g,d i )=f(d i r ,d i r+1 ,g) Formula 2;

[0093] Where, L d(g,d i ) represents the interpolation result of the logging layer; f(.) represents the preset interpolation algorithm; d i r The new coordinates are The depth value corresponding to the well in the i-th logging layer; d i r+1 The new coordinates are The depth value of the well in the i-th logging layer; g is the current node coordinate, g∈1,...,N x , N x The number of lateral nodes of the mesh for two-dimensional modeling;

[0094] According to the interpolation result of each logging layer, the depth curve of each logging layer is obtained.

[0095] In the embodiment of the present invention, for each logging layer, the current node coordinates of the logging layer interpolation curve, i.e., the depth curve, are set to be g(g∈1,...,N x ), where N x It also represents the number of lateral nodes of the two-dimensional modeling grid. The node spacing is set to 1. For each logging layer L(w,d i )(i=1,2,...,N) at the depth value L of the current node g d (g,d i )(i=1,2,...,N), first determine the new coordinates of the wells immediately adjacent to the current node. Then, the interpolation calculation is performed according to the above formula 2. Wherein, f(.) represents a preset interpolation algorithm, and a linear interpolation algorithm can be used.

[0096] Traverse the node g(g∈1,...,N x ) calculates the interpolation depth curve corresponding to a certain logging sublayer, and then traverses each logging sublayer i (i=1,2,...,N) on this basis to obtain the interpolation result of each logging sublayer and the depth curve of each logging sublayer, that is, the distribution information of each logging sublayer in the two-dimensional modeling space is obtained.

[0097] S102: Determine each grid node to be estimated according to the constraint range of the depth curves of the top logging sublayer and the bottom logging sublayer in the modeling network.

[0098] In the embodiment of the present invention, according to the depth curves of each well logging layer, the top and bottom well logging layer lines, i.e., the depth curves corresponding to the well logging layer, are selected as constraints to determine the effective prediction range in the two-dimensional attribute modeling space. Assume that the depth of the top well logging layer, i.e., the depth of the top well logging layer, is The depth of the lowest logging layer, i.e. the bottom logging layer, is For each node in the grid divided into the modeling space,v (x,z) has:

[0099]

[0100] In the formula, Grid v (x,z) is each grid node; z is the vertical coordinate, ranging from [1,N z ],N z The number of vertical nodes of the modeling grid; Grid d (x,z) is the grid node Grid v The depth value corresponding to (x,z); is the depth value of the top logging layer corresponding to the grid node; It is the depth value of the bottom logging layer corresponding to the grid node.

[0101] When Grid v When the value corresponding to (x,z) is 1, it means that the grid node is the grid node to be estimated; when Grid v When the value corresponding to (x,z) is 0, it means that the grid node is out of range and no prediction is made. The following steps are only performed on the grid nodes to be estimated.

[0102] S103: For each of the grid nodes to be estimated, determine the vertical depth proportional coefficient of the grid node to be estimated according to the depth curves of the upper and lower logging layers that are the closest to the grid node to be estimated.

[0103] In the embodiment of the present invention, for each of the grid nodes to be estimated, determining the vertical depth proportional coefficient of the grid node to be estimated according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated specifically includes:

[0104] For each of the grid nodes to be estimated, the vertical depth proportional coefficient of the grid node to be estimated is determined based on the following formula 3 according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated:

[0105]

[0106] Where Ratio(x,z) is the vertical depth ratio coefficient corresponding to the depth value of the grid node to be estimated; d (x, z) is the depth value of the grid node to be estimated; is the depth value of the upper logging layer closest to the grid node to be estimated; is the depth value of the nearest lower logging layer of the grid node to be estimated.

[0107] In the embodiment of the present invention, for each grid node to be evaluated Grid v(x,z) = 1, determine the two adjacent logging layers above and below it, and set the depth value of the upper logging layer closest to the grid node to be estimated to be The depth value of the nearest lower logging layer of the grid node to be estimated is Then the depth value Grid corresponding to the current grid node to be estimated d (x,z) satisfies On this basis, the current node depth value Grid is calculated based on the above formula 3 d (x,z) corresponds to the vertical scale parameter Ratio(x,z), where the value range of the vertical scale coefficient Ratio is [0,1).

[0108] S104: Determine, according to the vertical depth ratio coefficient, a target logging curve value corresponding to the to-be-estimated grid node at the vertical depth ratio.

[0109] In the embodiment of the present invention, determining the target logging curve value corresponding to the to-be-estimated grid node at the vertical depth ratio according to the vertical depth ratio coefficient specifically includes:

[0110] For each grid node to be estimated, determining the target attribute value of the grid node to be estimated at the corresponding vertical depth ratio of each well according to the vertical depth ratio coefficient;

[0111] According to the target attribute value at the corresponding vertical depth ratio of each well, the target logging curve value corresponding to the vertical depth ratio of the grid node to be estimated is determined.

[0112] In the embodiment of the present invention, for each grid node to be evaluated Grid v (x,z)=1, according to its corresponding vertical depth ratio coefficient Ratio, obtain the target attribute value of each conditional well at the corresponding vertical depth ratio, and obtain the target logging curve value Attr corresponding to the vertical depth ratio of the grid node to be estimated well , in, is the target attribute value of the rth conditional well at the vertical depth ratio.

[0113] S105: Determine the target attribute value corresponding to the grid node to be estimated according to the target logging curve value, and construct an underground two-dimensional attribute model.

[0114] In an embodiment of the present invention, determining the target attribute value corresponding to the grid node to be estimated according to the target logging curve value and constructing the underground two-dimensional attribute model specifically includes:

[0115] For each of the grid nodes to be estimated, the target attribute value corresponding to the grid node to be estimated is determined according to the target logging curve value based on the following formula 4:

[0116]

[0117] In the formula, Grid attr (x,z) is the target attribute value corresponding to the grid node to be estimated; is the target attribute value corresponding to the rth well; S r is the target attribute value corresponding to the grid node to be estimated and the rth well The corresponding distance at the target attribute value of each well; S is the sum of the reciprocals of the corresponding distances between the grid node to be estimated and the target attribute value of each well, that is,

[0118] According to the target attribute values ​​corresponding to all the grid nodes to be estimated, a two-dimensional underground attribute model is constructed.

[0119] In the embodiment of the present invention, for each grid node to be evaluated Grid v (x,z), obtain the target logging curve value Attr at the vertical depth ratio well Then, based on the above formula 4, the target attribute value Grid corresponding to the grid node to be estimated is calculated attr (x,z). By traversing all the grid nodes to be estimated and obtaining the target attribute values ​​corresponding to all the grid nodes to be estimated, the two-dimensional modeling calculation of the target attribute can be completed. The results can also be smoothed and filtered to construct an underground two-dimensional attribute model.

[0120] In the embodiment of the present invention, the above-mentioned method for constructing a two-dimensional underground attribute model based on well logging curves can be used to carry out two-dimensional modeling of any well logging attribute, such as acoustic wave transit time logging attribute.

[0121] In a specific embodiment, two-dimensional attribute modeling is performed on the acoustic time difference logging attributes in a certain work area, such as Figure 2 As shown in the figure, the well location information of three wells W4, W401 and W402 in a certain work area is connected according to the preset order, that is, according to the plane position relationship of each well. The dotted line in the figure is the connection line of the three wells in the plane relationship. The result of logging sub-layer division of these three wells is as follows Figure 3 As shown, the curve in the figure is the acoustic time difference curve corresponding to the well, and a short horizontal line represents a logging sublayer. The corresponding depth of each logging sublayer in each well can be obtained.

[0122] Based on the division results of logging sublayers and the well location information, interpolation and extrapolation of logging sublayers are carried out to obtain the distribution information of each logging sublayer in the two-dimensional modeling space, such as Figure 4 As shown, for three wells W4, W401 and W402, interpolation processing is performed based on the division results of the logging sublayers, and the distribution prediction of the seven logging sublayers in the two-dimensional modeling space is obtained. Each dotted line in the figure represents the depth curve corresponding to a logging sublayer.

[0123] After obtaining multiple logging sub-layer depth curves, the grid nodes to be estimated are determined in the two-dimensional modeling space based on the predicted top and bottom logging sub-layers, and no prediction is made for the grid nodes outside this range. For each grid node to be estimated, the logging sub-layers immediately above and below it are determined, and the vertical depth ratio coefficient of the grid node to be estimated between these two sub-layers is calculated. Based on the vertical depth ratio coefficient and the grid coordinates, the acoustic wave time difference attribute value corresponding to the node to be estimated is predicted by combining the logging curve, such as Figure 5 As shown, the acoustic time difference attributes predicted in the two-dimensional modeling space are based on the well location distribution, logging curves and logging layer interpolation results.

[0124] The predicted acoustic time difference attributes extracted at each conditional well location are compared with the actual acoustic time difference logging curve. The comparison results are as follows: Figure 6 As shown, it can be seen that the prediction results of the two-dimensional attribute modeling results at the well point are not only highly correlated with the actual logging curve, but also meet the constraints of the interpreted logging sublayer, which comprehensively verifies the effectiveness of the above method and shows that the two-dimensional attribute model established for the acoustic time difference logging attributes using the above method has high accuracy.

[0125] Embodiment 2

[0126] Based on the same inventive concept, the embodiment of the present invention also provides a device for constructing a two-dimensional underground attribute model based on a well logging curve, referring to Figure 7 As shown, the device comprises:

[0127] The interpolation module 101 is used to determine the interpolation result of each logging layer based on the logging layer division result of the logging curve in the target layer section and according to the well location information, and obtain the depth curve of each logging layer;

[0128] The constraint module 102 is used to determine each grid node to be estimated according to the constraint range of the depth curves of the top logging layer and the bottom logging layer in the modeling network;

[0129] The first determination module 103 is used to determine the vertical depth proportional coefficient of each grid node to be estimated according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated;

[0130] A second determination module 104 is used to determine the target logging curve value corresponding to the vertical depth ratio of the grid node to be estimated according to the vertical depth ratio coefficient;

[0131] The third determination module 105 is used to determine the target attribute value corresponding to the grid node to be estimated according to the target logging curve value, and construct a two-dimensional underground attribute model.

[0132] Embodiment 3

[0133] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a two-dimensional underground attribute model based on logging curves as described in the above-mentioned embodiment 1.

[0134] Embodiment 4

[0135] Based on the same inventive concept, an embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for constructing an underground two-dimensional attribute model based on logging curves as described in the above-mentioned embodiment 1 is implemented.

[0136] Embodiment 5

[0137] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product comprising instructions. When the computer program product is executed on a computer device, the computer device executes the method for constructing a two-dimensional underground attribute model based on logging curves as described in the first embodiment above.

[0138] Embodiment 6

[0139] Based on the same inventive concept, an embodiment of the present invention also provides a chip, the chip includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run a computer program or instruction to implement the method for constructing an underground two-dimensional attribute model based on logging curves as described in the above embodiment one.

[0140] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0141] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for constructing a two-dimensional underground attribute model based on well logging curves, characterized in that: include: Based on the logging sub-layer division result of the logging curve in the target layer section, the interpolation result of each logging sub-layer is determined according to the well location information, and the depth curve of each logging sub-layer is obtained; According to the constraint range of the depth curves of the top logging layer and the bottom logging layer in the modeling network, each grid node to be estimated is determined; For each of the grid nodes to be estimated, determining the vertical depth proportional coefficient of the grid node to be estimated according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated; According to the vertical depth ratio coefficient, determining the target logging curve value corresponding to the grid node to be estimated at the vertical depth ratio; According to the target logging curve value, the target attribute value corresponding to the grid node to be estimated is determined, and an underground two-dimensional attribute model is constructed.

2. The method according to claim 1, characterized in that The logging sub-layer division result based on the logging curve in the target layer section, and the interpolation result of each logging sub-layer is determined according to the well location information to obtain the depth curve of each logging sub-layer, including: Based on the logging sub-layer division result of the logging curve in the target layer section, the depth value corresponding to each logging sub-layer is extracted at each well in the target layer section; Based on the location information of each well in the target layer segment, the coordinates of each well are connected and straightened in a preset order to determine the new coordinates of each well on the straight line; For each logging sublayer, interpolation processing is performed according to the depth values ​​corresponding to each well in the logging sublayer and the new coordinates on the straight line, the interpolation result of the logging sublayer is determined, and the depth curve of the logging sublayer is obtained.

3. The method according to claim 2, characterized in that The method of connecting and straightening the coordinates of each well based on the location information of each well in the target layer segment in a preset order to determine the new coordinates of each well on the straight line includes: Based on the location information of each well in the target layer, the coordinates of each well are connected and straightened in a preset order, and the new coordinates of each well on the straight line are determined based on the following formula 1: In the formula, is the new coordinate of the rth well on the straight line; [x r ,y r ] is the work area coordinate of the rth well; Preset new coordinates for the first well on the line.

4. The method according to claim 2, characterized in that The interpolation process is performed on each logging sublayer according to the depth values ​​corresponding to each well in the logging sublayer and the new coordinates on the straight line to determine the interpolation result of the logging sublayer and obtain the depth curve of the logging sublayer, including: For each logging layer, according to the depth values ​​corresponding to the wells in the logging layer and the new coordinates on the straight line, interpolation processing is performed based on the following formula 2 to determine the interpolation result of the logging layer: Where, L d (g,d i ) represents the interpolation result of the logging layer; f(.) represents the preset interpolation algorithm; The new coordinates are The depth value corresponding to the i-th logging layer of the well; The new coordinates are The depth value of the well in the i-th logging layer; g is the current node coordinate, g∈1,...,N x , N x The number of lateral nodes of the mesh for two-dimensional modeling; According to the interpolation result of each logging layer, the depth curve of each logging layer is obtained.

5. The method according to claim 1, characterized in that For each of the grid nodes to be estimated, determining the vertical depth ratio coefficient of the grid node to be estimated according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated, comprises: For each of the grid nodes to be estimated, the vertical depth proportional coefficient of the grid node to be estimated is determined based on the following formula 3 according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated: Where Ratio(x,z) is the vertical depth ratio coefficient corresponding to the depth value of the grid node to be estimated; d (x, z) is the depth value of the grid node to be estimated; is the depth value of the upper logging layer closest to the grid node to be estimated; is the depth value of the nearest lower logging layer of the grid node to be estimated.

6. The method according to claim 1, characterized in that Determining the target logging curve value corresponding to the to-be-estimated grid node at the vertical depth ratio according to the vertical depth ratio coefficient includes: For each grid node to be estimated, determining the target attribute value of the grid node to be estimated at the corresponding vertical depth ratio of each well according to the vertical depth ratio coefficient; According to the target attribute value at the corresponding vertical depth ratio of each well, the target logging curve value corresponding to the vertical depth ratio of the grid node to be estimated is determined.

7. The method according to claim 1, characterized in that The step of determining the target attribute value corresponding to the grid node to be estimated according to the target logging curve value and constructing the underground two-dimensional attribute model includes: For each of the grid nodes to be estimated, the target attribute value corresponding to the grid node to be estimated is determined according to the target logging curve value based on the following formula 4: In the formula, Grid attr (x,z) is the target attribute value corresponding to the grid node to be estimated; is the target attribute value corresponding to the rth well; S r is the target attribute value corresponding to the grid node to be estimated and the rth well The corresponding distance at the target attribute value of each well; S is the sum of the reciprocals of the corresponding distances between the grid node to be estimated and the target attribute value of each well, that is, According to the target attribute values ​​corresponding to all the grid nodes to be estimated, a two-dimensional underground attribute model is constructed.

8. A device for constructing a two-dimensional underground attribute model based on well logging curves, characterized in that: include: An interpolation module is used to determine the interpolation result of each logging layer based on the logging layer division result of the logging curve in the target layer section and according to the well location information, so as to obtain the depth curve of each logging layer; A constraint module, used to determine each grid node to be estimated according to the constraint range of the depth curves of the top logging layer and the bottom logging layer in the modeling network; A first determination module is used to determine, for each of the grid nodes to be estimated, a vertical depth proportional coefficient of the grid node to be estimated according to the depth curves of the upper and lower logging layers closest to the grid node to be estimated; A second determination module is used to determine the target logging curve value corresponding to the to-be-estimated grid node at the vertical depth ratio according to the vertical depth ratio coefficient; The third determination module is used to determine the target attribute value corresponding to the grid node to be estimated according to the target logging curve value, and construct an underground two-dimensional attribute model.

9. A computer-readable storage medium storing instructions, which, when executed on a terminal, enable the terminal to execute the method for constructing a two-dimensional underground attribute model based on well logging curves as described in any one of claims 1 to 7.

10. A computer device, characterized in that: It includes a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for constructing a two-dimensional underground attribute model based on logging curves as described in each of claims 1 to 7.

11. A computer program product comprising instructions, which, when executed on a computer device, enables the computer device to execute the method for constructing a two-dimensional underground attribute model based on well logging curves as claimed in any one of claims 1 to 7.

12. A chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a computer program or instruction to implement the method for constructing a two-dimensional underground attribute model based on well logging curves as described in each of claims 1 to 7.

Images