Sub-salt reservoir prediction method and device based on gradient modulus attribute constraint

Abstract

The invention discloses an under-salt reservoir prediction method and device based on gradient modulo attribute constraint, and the method comprises the steps: determining a salt bed boundary, and extracting gradient modulo attributes with the salt bed boundary as a constraint condition, thereby obtaining a weight body; carrying out reservoir prediction inversion on the target layer to obtain a reservoir prediction body; and according to the weight body and the reservoir prediction body, carrying out fusion to obtain the under-salt reservoir development area.

Description

Technical Field

[0001] This article relates to the technical field of carbonate reservoir prediction, and in particular to a method and device for pre-salt reservoir prediction based on gradient modulus attribute constraints. Background Art

[0002] The recoverable reserves of carbonate gas reservoirs in the world account for about half of the recoverable reserves of natural gas. In some carbonate gas reservoir distribution areas, due to the interference of overlying salt layers on seismic reflection, reservoir prediction is multi-solution, unable to accurately reflect the spatial distribution characteristics of the reservoir, and affecting the success rate of gas well drilling. Therefore, it is necessary to study new methods for pre-salt reservoir prediction to improve the accuracy of reservoir prediction and thus meet the actual needs of gas field production. Summary of the Invention

[0003] To address the above problems, a subsalt reservoir prediction method based on gradient modulus attribute constraints is proposed. By introducing constraint weights within the salt layer to perform fusion calculation of reservoir inversion, the reservoir prediction results are made more reliable and reasonable.

[0004] In the first aspect, the present application provides a subsalt reservoir prediction method based on gradient modulus attribute constraints, the method comprising: determining the salt layer boundary, extracting the gradient modulus attribute with the salt layer boundary as a constraint condition, and obtaining a weight body; performing reservoir prediction inversion on the target layer to obtain a reservoir prediction body; and fusing the weight body and the reservoir prediction body to obtain a subsalt reservoir development area.

[0005] In the second aspect, an embodiment of the present invention also provides a subsalt reservoir prediction device based on gradient modulus attribute constraints, the device comprising: a memory and a processor; the memory is used to store a program for performing subsalt reservoir prediction based on gradient modulus attribute constraints, and the processor is used to read and execute the program for performing subsalt reservoir prediction based on gradient modulus attribute constraints, and perform any one of the methods described in the above embodiments.

[0006] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a data processing program stored thereon, and the data processing program is executed by a processor to provide a method for predicting subsalt reservoirs based on gradient modulus attribute constraints as described in any one of the above embodiments.

[0007] Compared to related technologies, this application provides a subsalt reservoir prediction method based on gradient modulus attribute constraints. The method includes: determining the salt layer boundary, extracting gradient modulus attributes using the salt layer boundary as a constraint, and obtaining a weighted volume; performing reservoir prediction inversion on the target layer to obtain a reservoir prediction volume; and fusing the weighted volume with the reservoir prediction volume to obtain the subsalt reservoir development zone. By introducing constraint weights within the salt layer to perform fusion calculations in reservoir inversion, this application improves the reliability and rationality of reservoir prediction results.

[0008] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0010] Figure 1 This is a flow chart of a method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to an embodiment of the present application;

[0011] Figure 2 Schematic diagram of a subsalt reservoir prediction device based on gradient modulus attribute constraints according to an embodiment of the present application;

[0012] Figure 3 A flow chart of a method for pre-salt reservoir prediction based on gradient modulus attribute constraints in some exemplary embodiments;

[0013] Figure 4 A schematic diagram of the planar distribution range of salt layers in some exemplary embodiments;

[0014] Figure 5 A schematic diagram of a calculation window for neighboring points in seismic data in some exemplary embodiments;

[0015] Figure 6 Schematic diagram of the superposition of the gradient mode attribute section and the seismic section in some exemplary embodiments;

[0016] Figure 7 Schematic diagram of a cross section of reservoir prediction inversion results without gradient modulus constraint in some exemplary embodiments;

[0017] Figure 8 Schematic diagram of a cross-section of reservoir prediction results based on gradient modulus attribute constraints in some exemplary embodiments. DETAILED DESCRIPTION

[0018] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0019] This application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the appended claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the appended claims.

[0020] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0021] The main methods for pre-salt reservoir prediction include constrained sparse pulse inversion, geostatistical inversion and waveform indication inversion.

[0022] Reservoir prediction using constrained sparse pulse inversion is simple, efficient, and fast, and has therefore been the most widely used method in the past 20-30 years. However, since the resolution is affected by the dominant frequency of post-stack seismic data, the resolution of the inversion results is low, making it impossible to accurately characterize subsalt reservoirs.

[0023] Geostatistical inversion can theoretically predict thin reservoirs of the order of 2-3 meters, but the inversion results lack seismic constraints, and the lateral changes are constrained by the model, making it impossible to identify the response characteristics of salt layers.

[0024] The inventors discovered that:

[0025] While waveform-based inversion technology can, to a certain extent, leverage post-stack seismic attributes to constrain subsalt reservoirs, it places high demands on seismic data quality and the number of wells drilled, making widespread application difficult. Generally speaking, current methods for presalt reservoir prediction rely solely on seismic and drilling data for medium- and high-frequency predictions, without further constraining low frequencies through optimization models. This makes it difficult to precisely characterize the distribution characteristics of presalt reservoirs and fails to meet the requirements for refined reservoir prediction.

[0026] Aiming at the problems existing in the prediction of subsalt carbonate reservoirs, and considering that most of the current reservoir prediction methods use seismic data and drilling data in the high-frequency band, which are difficult to meet the accuracy requirements, a subsalt reservoir prediction method based on gradient modulus attribute constraints is provided. By optimizing the model to achieve low-frequency constraints, the accuracy of subsalt carbonate reservoir prediction is improved.

[0027] The embodiment of the present invention provides a method for predicting subsalt reservoirs based on gradient modulus attribute constraints, such as Figure 1 As shown, the method includes steps S100-S120:

[0028] S100: Determine the salt layer boundary, extract the gradient modulus attribute with the salt layer boundary as a constraint condition, and obtain a weighted body;

[0029] S110: performing reservoir prediction inversion on the target layer to obtain a reservoir prediction volume;

[0030] S120: Fusing the weight body and the reservoir prediction body to obtain a subsalt reservoir development area.

[0031] In an exemplary embodiment, the process of determining the salt layer boundary is as follows:

[0032] The first step is to extract post-stack seismic attributes;

[0033] The specific process of extracting post-stack seismic attributes can be:

[0034] Conduct stratigraphic interpretation of the salt layer top and bottom within the target interval;

[0035] The interpretation horizons at the top and bottom of the salt layer are used as vertical constraints to extract seismic attributes. In this embodiment, the salt layer has a strong amplitude response in the vertical direction of the seismic profile, and the top and bottom ranges can be roughly tracked by seismic events.

[0036] The second step is to determine the threshold value of the salt layer within the work area by vertically comparing the drilled wells with the corresponding seismic attributes;

[0037] The third step is to perform horizon tracing interpretation on the post-stack seismic attributes according to the determined threshold value to determine the plane distribution range of the salt layer. Figure 4 As shown, the planar distribution range of the salt layer is determined by the threshold.

[0038] In an exemplary embodiment, the seismic attribute may be a root mean square amplitude attribute (RMS);

[0039]

[0040] Among them, a is the instantaneous amplitude of the earthquake, a i is the instantaneous earthquake amplitude value of the i-th sample point, and N is the number of sample points in the time window.

[0041] In an exemplary embodiment, if the seismic attribute is a root mean square amplitude attribute, the process of determining the threshold value of the salt layer within the work area by vertically comparing the drilled wells with the corresponding seismic attributes is as follows:

[0042] The first step is to determine the vertical distribution range of the salt layer of the drilled wells and vertically compare it with the root mean square amplitude attribute to determine the amplitude value of the salt layer of each well;

[0043] The second step is to average the amplitude values ​​of the salt layer in each well to determine the threshold value of the salt layer within the work area;

[0044]

[0045] Where b is the amplitude of the drilling salt layer, N is the number of sample points in the time window, b i is the amplitude value of the salt layer at the i-th sample point, and K is the threshold value of the salt layer.

[0046] In an exemplary embodiment, determining the salt layer boundary and extracting the gradient modulus attribute with the salt layer boundary as a constraint condition to obtain a weighted volume includes:

[0047] The first step is to extract the gradient modulus properties with the salt layer boundary as the constraint condition;

[0048] The second step is to normalize the gradient modulus attribute within the salt layer to establish a weighted data volume within the salt layer. In this embodiment, the specific process can be: performing histogram statistics on the gradient modulus attribute value range, determining the maximum and minimum values, and performing normalization processing; normalizing the gradient modulus attribute value range to the range of 0-1 (dividing the data volume by the maximum value of the value range), and defining it as the weighted data volume Q.

[0049] In an exemplary embodiment, extracting gradient modulus attributes with the salt layer boundary as a constraint includes:

[0050] The first step is to calculate the structure tensor field;

[0051] The second step is the gradient norm of the structure tensor field;

[0052]

[0053] In the formula, c is the center point, and o represents the eight directions in its neighborhood. The tensor field has two eigenvalues: the denominator is the difference between the first eigenvalue and the numerator is the difference between the second eigenvalue. The gradient norm of the structural tensor field can be expressed as the sum of the differences between the eigenvalues ​​of the tensor field centered on C and the eigenvalues ​​of the tensors in its eight neighborhoods. For a point c in 3D seismic data, the sum of the two norms of the tensor gradient norms in the inline, Xline, and T directions can be calculated.

[0054] In an exemplary embodiment, the process of calculating the structure tensor field is:

[0055] The first step is to calculate the amplitude gradient field g within the plane window:

[0056]

[0057] Where I(X) represents the earthquake amplitude function in the plane window, X = (x, y); g x 、g y are the directional derivatives in the x and y directions respectively; G is the Gaussian kernel function, σ g is the scale of the Gaussian kernel function used when calculating the amplitude gradient field.

[0058] The second step is to construct the gradient structure tensor T in the plane window based on the amplitude gradient field g:

[0059]

[0060] T is the gradient structure tensor, T xx The value obtained by multiplying the gradient in the X direction and the Y direction; T xy The value obtained by multiplying the gradient in the X direction and the Y direction; g x 、g y are the directional derivatives in the x and y directions respectively.

[0061] The third step is to perform Gaussian smoothing on the gradient structure tensor field to obtain a smoothed structure tensor field.

[0062]

[0063] is the smoothed structure tensor field, σ Tis the scale of the Gaussian kernel function G used to smooth the gradient structure tensor field, and T is the gradient structure tensor.

[0064] Step 4: Determine the main directions within the plane window

[0065] Constructed a tensor field Then, solve the main directions in the plane window

[0066]

[0067] The noise resistance of directional information estimation is enhanced by using a block consistency weighted directional field calculation method.

[0068]

[0069] Among them, T b represents the structure tensor field of block b centered on ij, μ represents the consistency weight coefficient matrix of block b, where the weight coefficient μ ij It can be calculated by the following formula:

[0070]

[0071] Where R represents the distance from the point (i, j) where the coefficient needs to be calculated to the center point of block b; A ij Represents the estimated direction angle of point (i, j); λ is the distance weight factor.

[0072] In an exemplary embodiment, the reservoir prediction inversion method is a waveform indication inversion method. Waveform indication inversion reservoir prediction is performed within the target layer. Waveform indication inversion is a conventional reservoir prediction method and is not specifically limited thereto, thereby obtaining a reservoir prediction result data body S1.

[0073] In an exemplary embodiment, the weight body and the reservoir prediction body are fused to obtain a subsalt reservoir development area, including:

[0074] If the salt layer distribution range J = 0, the reservoir prediction result S = S1;

[0075] If the salt layer distribution range J = 1, then the reservoir prediction result S = S1 * Q. In this embodiment, weight body constraints are performed within the salt layer constraint range, and the waveform indication inversion data body results in step 4 are used outside the salt layer constraint range.

[0076] The embodiments of the present application achieve the following technical effects through the above method:

[0077] First, the post-stack root mean square amplitude attribute is used to identify the salt layer range. Introducing the amplitude attribute for salt layer identification avoids the multi-solution problem caused by simple manual interpretation;

[0078] Second, gradient model attribute extraction is performed within the plane distribution range of the salt layer to obtain the influence coefficient (weight body) of the salt layer on the reservoir;

[0079] 3. Reservoir prediction inversion is performed on the basis of weight body constraints, and the subsalt reservoir prediction results with seismic phase constraints are obtained. This ensures that the lateral changes of the reservoir inversion are based on seismic characteristics, and introduces constraint weight calculation within the salt layer, making the reservoir prediction results more reliable and reasonable.

[0080] In a second aspect, an embodiment of the present invention further provides a subsalt reservoir prediction device based on gradient modulus attribute constraints, such as Figure 2 As shown, the device includes: a memory 200 and a processor 210; the memory is used to store a program for performing subsalt reservoir prediction based on gradient modulus attribute constraints, and the processor is used to read and execute the program for performing subsalt reservoir prediction based on gradient modulus attribute constraints, and execute any one of the methods described in the above embodiments.

[0081] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a data processing program stored thereon, and the data processing program is executed by a processor to provide a method for predicting subsalt reservoirs based on gradient modulus attribute constraints as described in any one of the above embodiments.

[0082] Example 1

[0083] This example shows the method flow of pre-salt reservoir prediction based on gradient modulus attribute constraints. Figure 3 The specific steps are as follows:

[0084] Step 1: Identify the distribution range of the salt layer through seismic attributes, interpret the salt layer, and determine the salt layer boundary; this step is to extract the post-stack seismic amplitude attributes within the salt layer segment, pick up and mark the amplitude anomaly area, and interpret and delineate the plane distribution range of the salt layer; Figure 4 The figure shows the distribution range of salt layers, where the black area represents the salt layer distribution area and the white area represents the area without salt layers.

[0085] Step 2: Extract the gradient modulus attributes with the salt layer boundary as the constraint condition to obtain the salt layer influence range;

[0086] Step 3: Normalize the gradient modulus attributes within the salt layer and establish a weighted volume based on the salt layer’s influence range;

[0087] Step 4: Carry out reservoir prediction inversion of the target layer to obtain the reservoir prediction volume;

[0088] Step 5: Use the weight body obtained in step 3 to perform weight constraints on the reservoir prediction inversion body obtained in step 4, and obtain the subsalt reservoir prediction results under the gradient modulus attribute constraints.

[0089] In step 1, the salt layer distribution range is identified through seismic attributes, and the salt layer interpretation is performed to determine the salt layer boundary. The process is as follows:

[0090] (1) Post-stack attribute extraction

[0091] The top and bottom of the salt layer above the target layer were interpreted at a 1*1 grid (grid spacing 25m). The top and bottom layers were used as vertical constraints to extract seismic attributes. The root mean square amplitude attribute (RMS) was used for salt layer identification.

[0092]

[0093] Where a is the instantaneous amplitude of the earthquake.

[0094] (2) Determination of salt layer threshold value

[0095] The vertical distribution range of the salt layer revealed by the completed drilling is vertically compared with the root mean square amplitude attribute extracted after stacking to determine the amplitude value of the salt layer in each well. The amplitude values ​​are then averaged to determine the threshold value (K) of the salt layer in the entire work area.

[0096]

[0097] Where: b is the drilling salt layer amplitude.

[0098] (3) Salt layer interpretation

[0099] The horizon tracing interpretation of the salt layer range is performed on the post-stack RMS amplitude data volume with the salt layer threshold value determined (interpretation accuracy 1*1 grid, grid spacing 25m) to determine the plane distribution range J of the salt layer.

[0100] If there is salt layer distribution in the vertical strata within the plane range, it is defined as J = 1, otherwise J = 0.

[0101] like Figure 4 The figure shows the distribution range of salt layers, where the black area represents the salt layer distribution area and the white area represents the area without salt layers.

[0102] In step 2, the gradient modulus attribute is extracted with the salt layer boundary as the constraint condition, and the process of obtaining the salt layer influence range is as follows:

[0103] (1) Structural tensor field

[0104] First, the amplitude gradient field g within the plane window is calculated by the following formula:

[0105]

[0106] Where I(X) represents the earthquake amplitude function in the plane window, X = (x, y); g x 、g y are the directional derivatives in the x and y directions respectively; G is the Gaussian kernel function, σ g is the scale of the Gaussian kernel function used when calculating the amplitude gradient field.

[0107] Based on the amplitude gradient field g, the gradient structure tensor T in the plane window can be constructed:

[0108]

[0109] In order to suppress the inaccurate direction caused by the sudden change of the tensor field due to local noise, it is necessary to perform Gaussian smoothing on the gradient structure tensor field to obtain a smooth structure tensor field.

[0110]

[0111] Where, σ T The scale of the Gaussian kernel function G used to represent the smoothed gradient structure tensor field. The scale σ T Generally with σ g Similarly, you can also choose the scale size according to the actual data conditions.

[0112] Constructed a tensor field Then, the main directions in the plane window can be solved by the following formula

[0113]

[0114] Noise data usually exists in seismic data. Therefore, the noise resistance of directional information estimation can be enhanced by using a direction field calculation method based on block consistency weighting.

[0115]

[0116] Among them, T b represents the structure tensor field of block b centered on ij, μ represents the consistency weight coefficient matrix of block b, where the weight coefficient μ of a certain point ij It can be calculated by the following formula:

[0117]

[0118] Where R represents the distance from the point (i, j) where the coefficient needs to be calculated to the center point of block b; A ij Represents the estimated direction angle of point (i, j); λ is the distance weight factor.

[0119] (2) Gradient norm of the structure tensor field

[0120]

[0121] Where c is the center point, and o represents the eight directions in its neighborhood. The tensor field has two eigenvalues: the denominator is the difference between the first eigenvalue and the numerator is the difference between the second eigenvalue. The gradient norm of the structural tensor field can be expressed as the sum of the differences between the eigenvalues ​​of the tensor field centered at c and the eigenvalues ​​of the tensors in its eight neighborhoods. For a point c in 3D seismic data, the sum of the two norms of the tensor gradient norms in the inline, xline, and T directions can be calculated.

[0122] (3) Design of calculation window

[0123] In actual calculation, each adjacent 4 points in the seismic data can be used as a calculation window (I, II, IV, V). The specific calculation is as follows: Figure 5 The structural tensor is calculated using a single seismic data center point and a calculation window of neighboring points in the extrapolated seismic trace data. Multiple windows are combined in pairs to form a new statistical time window. Alternatively, the structural tensor can be calculated using nine points as statistical windows (I-IX). The smaller the span of neighboring points used in the calculation, the higher the accuracy, but also the greater the sensitivity to noise in the data.

[0124] The prediction of any point in space is completed through multi-directional analysis and multiple statistics of any point. The gradient value is analyzed in all directions. The value range is positive and negative. Taking the gradient modulus is taking the absolute value of the gradient, which is returned to a positive value. Multiple statistics of each point are similar to multiple coverage of the channel gather, thereby reducing the impact of noise.

[0125] In step 3, the gradient modulus attributes within the salt layer are normalized, and the weighted volume is established according to the salt layer influence range. The process is as follows:

[0126] Perform histogram statistics on the range of the gradient modulus attribute value in step 2, determine the maximum and minimum values, and perform normalization processing to normalize the gradient modulus attribute value range in step 2 to the range of 0-1 (divide the data volume by the maximum value of the range), and define it as the weighted data volume Q. Figure 6 The gradient modulus attribute profile shows the salt layer influence range picked by the gradient modulus attribute. The black layers represent the top and bottom of the target layer. White and gray areas represent low gradient modulus value areas, while black areas represent high values, representing salt layers with greater influence and greater weight.

[0127] In step 4, the target layer reservoir prediction inversion is carried out to obtain the reservoir prediction volume as follows:

[0128] Perform waveform indication inversion reservoir prediction within the target layer range to obtain reservoir prediction result data body S1.

[0129] Figure 7 Represents the reservoir prediction results before adding gradient modulus attribute constraints; in this cross-section, colored areas are all reservoirs, and the darker the color, the better the surface reservoir; transparent or colorless areas represent non-reservoir areas.

[0130] In step five, the weighted volume obtained in step three is used to weight-constrain the reservoir prediction inversion volume obtained in step four. The process of obtaining the subsalt reservoir prediction results under the gradient modulus attribute constraint is as follows:

[0131] The weight body constraint is performed within the salt layer constraint range, and the waveform indication inversion data body results in step 4 are used outside the salt layer constraint range.

[0132] If the salt layer distribution range J = 0, the reservoir prediction result S = S1;

[0133] If the salt layer distribution range J=1, then the reservoir prediction result S=S1*Q.

[0134] like Figure 8 The figure shows the section of reservoir prediction results based on gradient modulus attribute constraints. Figure 8 That is the final result after adding the gradient modulus constraint. On this section, the colored areas are all reservoirs, and the darker the color, the better the surface reservoir; transparent or colorless areas represent non-reservoir areas.

[0135] The present invention proposes a subsalt reservoir prediction method based on gradient modulus attribute constraints. First, the present invention uses post-stack root mean square amplitude attributes to identify the salt layer range and introduces amplitude attributes for salt layer identification, thereby avoiding the multi-solution problem caused by simple manual interpretation; secondly, gradient modulus attributes are extracted within the plane distribution range of the salt layer to obtain the salt layer influence coefficient (weight body); finally, reservoir prediction inversion is performed on the basis of the weight body constraint to obtain subsalt reservoir prediction results with seismic phase constraints, that is, it ensures that the lateral changes of the reservoir inversion are based on seismic characteristics, and introduces the constraint weight calculation within the salt layer range, so that the reservoir prediction results have better reliability and rationality.

[0136] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A method for predicting subsalt reservoirs based on gradient modulus attribute constraints, characterized in that: The method comprises: Determine the salt layer boundary, extract the gradient modulus attribute with the salt layer boundary as a constraint condition, and obtain a weighted volume; Perform reservoir prediction inversion for the target layer to obtain the reservoir prediction body; The weight body and the reservoir prediction body are fused to obtain the subsalt reservoir development area.

2. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 1, characterized in that: The process of determining the salt layer boundary is: Extract target post-stack seismic attributes; Determine the threshold value of the salt layer based on vertical comparison of the drilled well and the post-stack seismic attributes; According to the determined threshold value, the post-stack seismic attributes are subjected to horizon tracing interpretation to determine the plane distribution range of the salt layer.

3. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 2, characterized in that: The process of extracting the target post-stack seismic attributes is as follows: Conduct stratigraphic interpretation on the salt layer top and bottom in the target layer to obtain the stratigraphic positions of the salt layer top and bottom; The layer at the top of the salt layer and the layer at the bottom of the salt layer are used as vertical constraints to extract post-stack seismic attributes.

4. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 2, characterized in that: The post-stack seismic attribute is a root mean square amplitude attribute; The root mean square amplitude attribute is: Among them, a is the instantaneous amplitude of the earthquake, and N is the number of sample points in the time window.

5. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 4, characterized in that: The determining of the threshold value of the salt layer by vertically comparing the drilled well and the post-stack seismic attributes includes: vertically comparing the vertical distribution range of the salt layer in the drilled wells with the root mean square amplitude attribute to determine the amplitude value of the salt layer in each well; The amplitude values ​​of the salt layer in each well are averaged to determine the threshold value of the salt layer within the working area; Where b is the amplitude of the drilling salt layer, k is the threshold value of the salt layer, and N is the number of sample points in the time window.

6. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 2, characterized in that: The step of determining the salt layer boundary and extracting the gradient modulus attribute with the salt layer boundary as a constraint condition to obtain a weighted body includes: Gradient modulus properties are extracted with the salt layer boundary as the constraint; The gradient modulus attributes within the salt layer are normalized to establish a weighted data volume within the salt layer.

7. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 6, characterized in that: The method of extracting gradient modulus attributes with the salt layer boundary as a constraint condition includes: Compute the gradient structure tensor; Calculating the gradient modulus property corresponding to the gradient structure tensor with the salt layer boundary as a constraint condition; Wherein, the gradient modulus property is: Where Mod gst is the gradient norm property, σ c1 is the tensor eigenvalue in the Inline direction, σ c2 is the tensor eigenvalue in the Xline direction, σ o1 is the gradient calculated at the first sampling point in the vertical direction, σ o2 is the gradient calculated at the second sampling point in the vertical direction, where i, j, and k are the three-dimensional directions.

8. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 7, characterized in that: The gradient structure tensor is: Among them, T is the gradient structure tensor, T xx is the gradient value in the xx direction, T xy is the gradient value in the xy direction; g is the amplitude gradient field in the calculation plane window, I(X) represents the earthquake amplitude function in the plane window, X = (x, y); g x 、g y are the directional derivatives in the x and y directions respectively; G is the Gaussian kernel function, σ g is the scale of the Gaussian kernel function used when calculating the amplitude gradient field.

9. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 1, characterized in that: The reservoir prediction inversion method is: waveform indication inversion method or geostatistical inversion or logging constraint inversion.

10. The method for predicting subsalt reservoirs based on gradient modulus attribute constraints according to claim 1, characterized in that: The step of fusing the weight body and the reservoir prediction body to obtain the subsalt reservoir development area includes: If the salt layer distribution range J = 0, the reservoir prediction result S = S1; If the salt layer distribution range J = 1, then the reservoir prediction result S = S1*Q; Among them, J represents the value of the salt layer, S is the reservoir value, and Q is the weight body.

11. A device for predicting subsalt reservoirs based on gradient modulus attribute constraints, characterized in that: The device includes: a memory and a processor; the memory is used to store a program for performing subsalt reservoir prediction based on gradient modulus attribute constraints, and the processor is used to read and execute the program for performing subsalt reservoir prediction based on gradient modulus attribute constraints, and execute the method described in any one of claims 1 to 10.

12. A computer-readable storage medium having a data processing program stored thereon, wherein the data processing program is executed by a processor to implement the method for pre-salt reservoir prediction based on gradient modulus attribute constraints according to any one of claims 1 to 10.

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