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

Abstract

The invention discloses an underground three-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 based on a small logging layer division result of a logging curve at a target interval, and obtaining a two-dimensional horizon surface of each small logging layer; according to the constraint range of the two-dimensional horizon surfaces of the top logging small layer and 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 two-dimensional horizon surface 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 three-dimensional attribute model. According to the method, the underground three-dimensional attribute model is constructed quickly and precisely on the basis of a logging curve and a small layer.

Description

Technical Field

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

[0002] In oil and gas exploration, selecting specific target attributes to carry out reservoir evaluation is of great significance for tapping underground oil and gas resources. Seismic data usually has the advantages of wide coverage and deep detection, and is an important guiding information for oil and gas resource exploration and development. However, its data resolution is usually low and it is difficult to meet the needs of high-precision reservoir characterization. Compared with seismic data, well logging data has better vertical resolution. Under dense well networks, underground three-dimensional target attribute modeling based on well logging data can obtain the fine distribution characteristics of target attributes in underground three-dimensional space, which is of great significance for oil and gas exploration.

[0003] Three-dimensional geological modeling can usually be divided into target-based and pixel-based modeling methods. Among them, the target-based modeling method traverses and solves each grid node in the modeling space according to a specific path, while the pixel-based modeling method mainly restores the morphological characteristics of the geological body in the three-dimensional space according to the conditional data in the modeling space; the target-based modeling method fully complies with the hard conditional data, while the pixel-based modeling method attempts to restore the geometric morphology of the geological body to the greatest extent, and there is a possibility that it does not meet some conditional data.

[0004] Target-based geological modeling methods have been developed for many years. For example, the stratigraphic random modeling method has been widely used in recent years. It is based on the theory of random functions. By generating random paths during the modeling process or randomly generating prediction results according to specific probability distribution characteristics at any grid node, different modeling results can be obtained in multiple simulations, and multiple results are used to characterize modeling uncertainty. In addition, there are also deterministic modeling methods to construct underground models. This type of method combines work area data and prior knowledge to obtain an algorithm-based optimal model prediction result, and uses this result to deterministically characterize underground properties. Summary of the invention

[0005] In order to better realize the construction of an underground three-dimensional attribute model, an embodiment of the present invention provides a method, a device and a equipment for constructing an underground three-dimensional attribute model based on a well logging curve.

[0006] In a first aspect, an embodiment of the present invention provides a method for constructing a three-dimensional underground attribute model based on a well logging curve, the method comprising:

[0007] Based on the logging sub-layer division results of the logging curve in the target layer section, the interpolation results of each logging sub-layer are determined according to the well location information, and the two-dimensional layer plane of each logging sub-layer is obtained;

[0008] According to the constraint range of the two-dimensional layer plane of the top logging layer and the bottom logging layer in the modeling network, each grid node to be estimated is determined;

[0009] 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 two-dimensional layer planes of the upper and lower logging layers closest to the grid node to be estimated;

[0010] 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;

[0011] According to the target logging curve value, the target attribute value corresponding to the grid node to be estimated is determined, and an underground three-dimensional attribute model is constructed.

[0012] 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 of each well, and the two-dimensional layer plane of each logging sub-layer is obtained, including:

[0013] 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;

[0014] For each logging sublayer, based on the location information of each well in the target layer, determine the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well;

[0015] Interpolation processing is performed according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer, the interpolation result of the logging sublayer is determined, and the two-dimensional layer plane of the logging sublayer is obtained.

[0016] In one or some optional implementations of the embodiments of the present invention, determining, for each logging sublayer, the horizontal distance between each point on the two-dimensional horizon plane of the logging sublayer and each well based on the well location information of the target layer segment, comprises:

[0017] Based on the well location information of the target layer, the horizontal distance between each point on the plane grid corresponding to the logging layer and each well is determined based on the following formula 1:

[0018] S r (x,y)=sqrt((x r -x) 2 +(y r -y) 2 ) Formula 1;

[0019] In the formula, S r (x,y) is the horizontal distance between each point on the plane grid and each well; [x r ,yr ] is the work area coordinate of the rth well.

[0020] In one or some optional implementations of the embodiments of the present invention, the interpolation processing is performed according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer to determine the interpolation result of the logging sublayer and obtain the two-dimensional layer plane of the logging sublayer, including:

[0021] For each logging sublayer, according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer, interpolation processing is performed based on the following formula 2 to determine the interpolation result of the logging sublayer:

[0022]

[0023] In the formula, is the interpolation result of the logging layer; is the depth value of the rth well in the ith layer; S r (x, y) is the horizontal distance between each point on the plane grid and each well; S is the sum of the reciprocals of the horizontal distances between each point on the plane grid and each well.

[0024] According to the interpolation results of each logging layer, the two-dimensional layer plane 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 scale coefficient of the grid node to be estimated according to the two-dimensional layer planes 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 ratio coefficient of the grid node to be estimated is determined based on the following formula 3 according to the two-dimensional layer planes of the upper and lower logging layers closest to the grid node to be estimated:

[0027]

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

[0029] In one or some optional implementations of the embodiments of the present invention, determining the target logging curve value corresponding to each of the grid nodes to be estimated at the vertical depth ratio according to each of the vertical depth ratio coefficients 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 each 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, y, 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 three-dimensional underground attribute model is constructed.

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

[0038] An interpolation module is used to determine the interpolation results of each logging layer based on the logging curve in the target layer section and the well location information to obtain the two-dimensional layer plane of each logging layer;

[0039] A constraint module, used to determine each grid node to be estimated according to the constraint range of the two-dimensional layer plane 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 ratio coefficient of the grid node to be estimated according to the two-dimensional layer planes 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 three-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 three-dimensional underground property 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 an underground three-dimensional 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 three-dimensional underground property 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 three-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 three-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 distribution characteristics of the two-dimensional layer surface 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 two-dimensional layer surface of the 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 three-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 three-dimensional underground attribute model based on well logging curves provided in an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of the well location information of 10 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 10 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 7 logging sub-layers of 10 wells in a certain work area in a three-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 showing the efficiency comparison between using a well logging curve-based underground three-dimensional attribute model and using a traditional algorithm in a certain work area provided in an embodiment of the present invention;

[0058] Figure 7 A schematic diagram showing the accuracy comparison between the underground three-dimensional attribute model based on the well logging curve and the traditional algorithm in a certain work area provided by an embodiment of the present invention;

[0059] Figure 8 A schematic diagram of isochronous target attribute slices extracted from calculation results of a method for constructing a subsurface three-dimensional attribute model based on well logging curves provided in an embodiment of the present invention;

[0060] Fig. 9 A schematic diagram of extracting predicted target attributes and comparing actual well logging curves at various conditional well locations using calculation results of a method for constructing a three-dimensional underground attribute model based on well logging curves provided in an embodiment of the present invention;

[0061] Fig.10 A schematic diagram of the structure of a device for constructing an underground three-dimensional attribute model based on well logging curves provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0070] The inventors found that in the prior art, the traditional underground three-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 each grid node in the layer, which easily leads to the occurrence of time-lapse of the predicted attribute, thereby reducing the prediction accuracy; directly calculating the three-dimensional distance between each node to be estimated and the reference position of the well logging attribute greatly increases the amount of modeling calculations and reduces the efficiency of the algorithm; does not fully consider the constraint effect of the well logging layer in the two-dimensional modeling space, and the modeling accuracy and efficiency are poor; requires other data constraints such as seismic data, 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 three-dimensional attribute model based on well logging curves.

[0071] Embodiment 1

[0072] The embodiment of the present invention provides a method for constructing a three-dimensional underground attribute model based on a well logging curve. Figure 1 As shown, the method includes:

[0073] 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 two-dimensional layer plane of each logging sub-layer is obtained.

[0074] 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 to obtain the two-dimensional layer surface of each logging sub-layer, which specifically includes:

[0075] 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;

[0076] For each logging sublayer, based on the location information of each well in the target layer, determine the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well;

[0077] Interpolation processing is performed according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer, the interpolation result of the logging sublayer is determined, and the two-dimensional layer plane of the logging sublayer is obtained.

[0078] 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.

[0079] 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 two-dimensional layer surfaces. The specific interpolation processing method is as follows:

[0080] For each logging layer L(w,d i )(i=1,2,...,N), firstly extract the depth value D corresponding to the logging layer at each conditional well point i , as shown in the following formula 5:

[0081]

[0082] 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.

[0083] 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:

[0084] Coord R ={[x1,y1],[x2,y2],...,[x R ,y R ]} Formula 6;

[0085] 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.

[0086] In the embodiment of the present invention, for each logging sublayer, based on the well position information of each well section, determining the horizontal distance between each point on the two-dimensional layer plane of the logging sublayer and each well specifically includes:

[0087] Based on the well location information of the target layer, the horizontal distance between each point on the plane grid corresponding to the logging layer and each well is determined based on the following formula 1:

[0088] S r (x,y)=sqrt((x r -x) 2 +(y r -y) 2 ) Formula 1;

[0089] In the formula, S r (x,y) is the horizontal distance between each point on the plane grid and each well; [x r ,y r ] is the work area coordinate of the rth well; sqrt(.) means finding the square root.

[0090] In the embodiment of the present invention, for the interpolation and extrapolation of the well logging layer, the interpolation plane Grid is first set h The size is N x ,N y , where N x ,N y That is the plane size of the top surface of the 3D modeling grid. Then, traverse each point Grid on the plane grid h (x,y)(x∈[1,N x ],y∈[1,N y ]), the horizontal distance between each point on the plane grid and each well is calculated by the above formula 1. The sum S of the reciprocals of the horizontal distances between each point on the plane grid and each well can be calculated by the following formula 7:

[0091]

[0092] Where S is the sum of the reciprocals of the horizontal distances between each point on the plane grid and each well; S r (x, y) is the horizontal distance between each point on the plane grid and each well; R is the number of wells.

[0093] In the embodiment of the present invention, the interpolation processing is performed according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer to determine the interpolation result of the logging sublayer and obtain the two-dimensional layer plane of the logging sublayer, specifically including:

[0094] For each logging sublayer, according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer, interpolation processing is performed based on the following formula 2 to determine the interpolation result of the logging sublayer:

[0095]

[0096] In the formula, is the interpolation result of the logging layer; is the depth value of the rth well in the ith layer; S r (x, y) is the horizontal distance between each point on the plane grid and each well; S is the sum of the reciprocals of the horizontal distances between each point on the plane grid and each well.

[0097] According to the interpolation results of each logging layer, the two-dimensional layer plane of each logging layer is obtained.

[0098] In the embodiment of the present invention, for each logging layer, the plane grid Grid is traversed by the above formula 2 h All grid nodes (x∈[1,N x ],y∈[1,Ny ]), the interpolation and extrapolation results of the corresponding logging layer can be obtained, and the two-dimensional layer plane of the logging layer can be obtained; then, all the logging layers L(w,d i )(i=1,2,...,N), obtain the two-dimensional layer planes corresponding to multiple logging sub-layers, that is, obtain the distribution information of each logging sub-layer in the two-dimensional modeling space.

[0099] S102: Determine each grid node to be estimated according to the constraint range of the two-dimensional layer plane of the top logging sublayer and the bottom logging sublayer in the modeling network.

[0100] In the embodiment of the present invention, according to the obtained two-dimensional layer planes corresponding to each well logging layer, the two-dimensional layer planes corresponding to the top and bottom well logging layers, i.e. the well logging layers, are selected as constraints to determine the effective prediction range in the three-dimensional attribute modeling space. Assume that the two-dimensional layer plane of the top well logging layer, i.e. the top well logging layer, is The two-dimensional plane of the bottom logging layer is The distribution range of the top and bottom logging planes is completely consistent with the modeling grid plane projection. For each node in the grid divided in the modeling space, v (x,y,z) has:

[0101]

[0102] In the formula, Grid v (x, y, 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,y,z) is the grid node Grid v The depth value corresponding to (x, y, z); is the depth value of the top logging sub-face corresponding to the grid node; It is the depth value of the bottom logging small layer corresponding to the grid node.

[0103] When Grid v When the value corresponding to (x, y, 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, y, 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.

[0104] 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 two-dimensional layer planes of the upper and lower logging layers that are most adjacent to the grid node to be estimated.

[0105] In the embodiment of the present invention, 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 two-dimensional layer planes of the upper and lower logging layers closest to the grid node to be estimated specifically includes:

[0106] For each of the grid nodes to be estimated, the vertical depth ratio coefficient of the grid node to be estimated is determined based on the following formula 3 according to the two-dimensional layer planes of the upper and lower logging layers closest to the grid node to be estimated:

[0107]

[0108] Where Ratio(x,y,z) is the vertical depth ratio coefficient corresponding to the depth value of the grid node to be estimated; d (x, y, z) is the depth value of the grid node to be estimated; is the depth value of the two-dimensional layer plane of the upper logging layer closest to the grid node to be estimated; It is the depth value of the two-dimensional layer plane of the nearest lower logging layer of the grid node to be estimated.

[0109] In the embodiment of the present invention, for each grid node to be evaluated Grid v (x, y, z) = 1, determine the two adjacent well logging layers above and below it, and set the depth value of the upper well 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,y,z) satisfies On this basis, the current node depth value Grid is calculated based on the above formula 3 d (x, y, z) corresponds to the vertical scale parameter Ratio(x, y, z), where the value range of the vertical scale coefficient Ratio is [0, 1).

[0110] 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.

[0111] In the embodiment of the present invention, determining the target logging curve value corresponding to each of the to-be-estimated grid nodes at the vertical depth ratio according to each of the vertical depth ratio coefficients specifically includes:

[0112] 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 each vertical depth ratio coefficient;

[0113] 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.

[0114] In the embodiment of the present invention, for each grid node to be evaluated Grid v (x, y, 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.

[0115] 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 three-dimensional attribute model.

[0116] 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:

[0117] 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:

[0118]

[0119] In the formula, Grid attr (x, y, 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,

[0120] According to the target attribute values ​​corresponding to all the grid nodes to be estimated, a three-dimensional underground attribute model is constructed.

[0121] In the embodiment of the present invention, for each grid node to be evaluated Grid v (x,y,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, y, 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 three-dimensional modeling calculation of the target attribute can be completed. The results can also be processed by a three-dimensional filtering algorithm to construct an underground three-dimensional attribute model.

[0122] In the embodiment of the present invention, the above-mentioned method for constructing a three-dimensional underground attribute model based on well logging curves can be used to carry out three-dimensional modeling of any well logging attribute, such as sonic time difference logging attribute.

[0123] In a specific embodiment, three-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 10 wells W1, W4, W11, W12, W14, W15, W16, W401, W402 and W404 in a certain work area. The results of logging sub-layer division for these 10 wells are 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.

[0124] 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 in the figure, the distribution prediction of 7 logging sublayers in the two-dimensional modeling space is obtained by interpolation processing based on the division results of logging sublayers of 10 wells. Each surface in the figure represents the two-dimensional layer plane corresponding to a logging sublayer.

[0125] After obtaining multiple logging sub-layer depth curves, the grid nodes to be estimated are determined in the three-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 the 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 in the figure, the acoustic wave time difference attributes predicted in the three-dimensional modeling space based on the well location distribution, logging curves and logging small layer interpolation results. The target layer is in the middle part of the volume data and is obliquely distributed. The black part is the grid node outside the target layer and does not participate in this modeling calculation.

[0126] like Figure 6 As shown in the figure, the efficiency comparison between the modeling method of the present invention and the traditional algorithm is shown. For the same modeling grid, the traditional algorithm takes 30 minutes while the method of the present invention takes 17 minutes. It can be seen that the computational efficiency of the method of the present invention is improved by about 45% compared with the traditional algorithm. Figure 7As shown in the figure, it is a comparison of the accuracy of modeling using the method of the present invention and using the traditional algorithm. The left side is the section of the modeling result using the traditional method. Since the vertical scale coefficient of the node to be estimated in the grid space is not considered, the calculation result usually cannot meet the characteristics of high precision and the like. The edge of the formation presents fish scale features on the section of the modeling result. The right side is the section of the modeling result using the method of the present invention. Since the vertical scale relationship between each node to be estimated and the logging curve is accurately controlled, it can be seen that the prediction result has better isochronism, the formation features on the section are continuous and natural, and the appearance of fish scale features is suppressed.

[0127] like Figure 8 As shown, according to Figure 6 From the isochronous target attribute slices extracted from the calculation results using the method of the present invention, it can be seen that the calculation results using the method of the present invention have a continuous change pattern on the plane, and the results can meet the needs of oil and gas resource assessment under dense well network conditions.

[0128] like Fig. 9 As shown, according to Figure 6 The calculation results of the method of the present invention are used to extract the predicted target attributes at each conditional well location and compare the results with the actual logging curves. It can be seen that the prediction results of the three-dimensional attribute modeling results at the well point are not only highly correlated with the actual logging curves, but also meet the constraints of the interpreted logging sublayers. The effectiveness of the above method is comprehensively verified, indicating that the three-dimensional attribute model established for the acoustic time difference logging attributes using the above method has high accuracy.

[0129] Embodiment 2

[0130] Based on the same inventive concept, the embodiment of the present invention also provides a device for constructing an underground three-dimensional attribute model based on a well logging curve, referring to Fig.10 As shown, the device comprises:

[0131] The interpolation module 101 is used to determine the interpolation result of each logging layer based on the logging curve in the target layer segment and the well location information, and obtain the two-dimensional layer plane of each logging layer;

[0132] The constraint module 102 is used to determine each grid node to be estimated according to the constraint range of the two-dimensional layer plane of the top logging layer and the bottom logging layer in the modeling network;

[0133] The first determination module 103 is used to determine the vertical depth ratio coefficient of each grid node to be estimated according to the two-dimensional layer plane of the upper and lower logging layers closest to the grid node to be estimated;

[0134] 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;

[0135] 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 an underground three-dimensional attribute model.

[0136] Embodiment 3

[0137] 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 an underground three-dimensional attribute model based on logging curves as described in the above-mentioned embodiment 1.

[0138] Embodiment 4

[0139] 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 three-dimensional attribute model based on logging curves as described in the above-mentioned embodiment 1 is implemented.

[0140] Embodiment 5

[0141] Based on the same inventive concept, an embodiment of the present invention also provides a computer program product containing instructions. When the computer program product is run on a computer device, the computer device executes the method for constructing an underground three-dimensional attribute model based on logging curves as described in the above embodiment 1.

[0142] Embodiment 6

[0143] 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 three-dimensional attribute model based on logging curves as described in the above embodiment one.

[0144] 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.

[0145] 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.

[0146] 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 including 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.

[0147] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0148] 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 an underground three-dimensional attribute model based on well logging curves, characterized in that: include: Based on the logging sub-layer division results of the logging curve in the target layer section, the interpolation results of each logging sub-layer are determined according to the well location information, and the two-dimensional layer plane of each logging sub-layer is obtained; According to the constraint range of the two-dimensional layer plane 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 ratio coefficient of the grid node to be estimated according to the two-dimensional layer planes 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 three-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 is determined according to the well location information of each well, and the two-dimensional layer plane of each logging sub-layer is obtained, 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; For each logging sublayer, based on the location information of each well in the target layer, determine the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well; Interpolation processing is performed according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer, the interpolation result of the logging sublayer is determined, and the two-dimensional layer plane of the logging sublayer is obtained.

3. The method according to claim 2, characterized in that For each logging sublayer, based on the well position information of each target layer segment, determining the horizontal distance between each point on the two-dimensional layer plane of the logging sublayer and each well, comprises: Based on the well location information of the target layer, the horizontal distance between each point on the plane grid corresponding to the logging layer and each well is determined based on the following formula 1: S r (x,y) = sqrt((x r - x) 2 + (y r - y) 2 ) Formula 1; In the formula, S r (x,y) is the horizontal distance between each point on the plane grid and each well; [x r ,y r ] is the work area coordinate of the rth well.

4. The method according to claim 2, characterized in that The interpolation process is performed according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer to determine the interpolation result of the logging sublayer and obtain the two-dimensional layer plane of the logging sublayer, including: For each logging sublayer, according to the horizontal distance between each point on the plane grid corresponding to the logging sublayer and each well and the depth value corresponding to the logging sublayer, interpolation processing is performed based on the following formula 2 to determine the interpolation result of the logging sublayer: In the formula, is the interpolation result of the logging layer; is the depth value of the rth well in the ith layer; S r (x, y) is the horizontal distance between each point on the plane grid and each well; S is the sum of the reciprocals of the horizontal distances between each point on the plane grid and each well. According to the interpolation results of each logging layer, the two-dimensional layer plane 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 two-dimensional layer planes 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 two-dimensional layer planes of the upper and lower logging layers closest to the grid node to be estimated: Where Ratio(x,y,z) is the vertical depth ratio coefficient corresponding to the depth value of the grid node to be estimated; d (x, y, z) is the depth value of the grid node to be estimated; is the depth value of the two-dimensional layer plane of the upper logging layer closest to the grid node to be estimated; It is the depth value of the two-dimensional layer plane 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 each of the to-be-estimated grid nodes at the vertical depth ratio according to each of the vertical depth ratio coefficients 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 each 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, y, 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 three-dimensional underground attribute model is constructed.

8. A device for constructing an underground three-dimensional attribute model based on well logging curves, characterized in that: include: An interpolation module is used to determine the interpolation results of each logging layer based on the logging curve in the target layer section and the well location information to obtain the two-dimensional layer plane of each logging layer; A constraint module, used to determine each grid node to be estimated according to the constraint range of the two-dimensional layer plane 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 ratio coefficient of the grid node to be estimated according to the two-dimensional layer planes 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 three-dimensional attribute model.

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

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 three-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 three-dimensional underground attribute model based on well logging curves as described in any one of claims 1 to 7.

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