Carbonate rock fracture classification detection method

Abstract

The invention discloses a carbonate rock fracture classification detection method, which relates to the technical field of oil and gas reservoir exploration, and is characterized in that attribute analysis is performed on an original seismic data volume of a target area aiming at the problem that complex characteristics of a carbonate fracture wave field influence a fracture detection effect, and fractures are classified into a strong corrosion area and weak and non-corrosion areas; and taking the tensor thinning attribute body as a fracture prediction result of a strong corrosion area, and taking a conventional coherent fracture detection result as a fracture prediction result of a weak corrosion area and a non-corrosion area. Through the classification detection technology, the influence of bead abnormity is effectively eliminated, a strong corrosion fracture detection result is formed, conventional small and medium-scale fractures represented by event disturbance and dislocation are reserved, the two fractures are organically fused in space, and the carbonate rock fracture detection precision is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas reservoir exploration, and more specifically, to a carbonate rock fracture classification detection method. Background Art

[0002] Fault-karst oil and gas reservoirs are a new type of oil and gas reservoir proposed in my country in recent years. They are various irregular fractures and caves formed around large strike-slip fault zones after carbonate rock formations have been subjected to multiple periods of tectonic movement deformation and karstification. Geological research has found that fault-karst oil and gas reservoirs formed on faults with different stress backgrounds are affected by differences in tectonic stress, direction, size, smoothing distance, and degree of dissolution in the plane and vertical direction. They are distributed along strike-slip faults in the plane and have the characteristics of "depth" development along the fault zone in space.

[0003] For fault-karst oil and gas reservoirs, carbonate rock fractures are not only the main channels for oil and gas filling and adjustment, but also favorable oil and gas storage spaces. Therefore, the identification of carbonate rock fractures is very important, and how to accurately describe the existence of fractures underground is the key point and the difficulty.

[0004] There are currently two main methods for post-stack prediction of carbonate faults. One is the use of gradient structure tensor attributes to identify carbonate fault-karst bodies, as used in the journal articles "Research and Application of Carbonate Fault-karst Body Characterization Technology in Tahe Oilfield", "Seismic Response Characteristics and Description Technology of Fault-karst Reservoirs in Shunbei Oilfield" and "Spatial Carving and Quantitative Description Technology of Ultra-deep Fault-karst Reservoirs in Shunbei Area". This type of method has a large width of tensor attributes and obvious spatial magnification, and it is difficult to identify the location of high-quality reservoirs on seismic profiles. The other is the use of correlation analysis of faults, as used in the journal articles "A Review and Research Progress of Fault Identification Methods" and "Identification of Middle and Lower Ordovician Fault-karst Bodies in Yubei Area, Tarim Basin". This type of method can obtain better prediction results on the plane, but it is greatly affected by the "beaded" anomalies. The attribute anomalies detected by the coherent analysis technology are mostly "beaded" strong anomaly boundaries, which cannot represent real faults.

[0005] Based on the problem that the complex characteristics of carbonate fracture wave fields affect the fracture detection effect, the present invention aims to propose a carbonate rock fracture classification detection method to eliminate the fracture illusion caused by the "beading" anomaly in the strong dissolution area and retain the conventional detection fracture in the weak dissolution area to improve the detection accuracy of carbonate rock fractures. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a carbonate rock fracture classification detection method, which classifies the fractures into strong dissolution areas and weak and non-dissolution areas by performing attribute analysis on the original seismic data body of the target area, and uses the tensor thinning attribute body as the fracture prediction result of the strong dissolution area, and uses the conventional coherent fracture detection result as the fracture prediction result of the weak and non-dissolution area. The present invention not only effectively eliminates the influence of beaded anomalies and forms strong dissolution fracture detection results, but also retains the conventional small and medium-scale fractures characterized by phase axis disturbance and fault faulting, and the two are organically integrated in space, effectively improving the detection accuracy of carbonate rock fractures.

[0007] To achieve the above object, the present invention provides the following technical solution: a carbonate rock fracture classification detection method, which comprises the following steps:

[0008] S1. Obtain the gradient structure tensor matrix T based on the three-dimensional seismic data of the sampling area.

[0009] S2, thinning the gradient structure tensor to obtain the tensor thinning attribute body F1, the specific steps are:

[0010] S21, obtain all eigenvalues ​​of the gradient structure tensor matrix T, at a certain eigenvalue λ i On this basis, all sampling points are scanned to obtain a sampling point set G(x) centered on the eigenvalue.

[0011] S22, calculate the azimuth angle of each sampling point and the inclination angle θ k All gradient structure tensor values

[0012] for The total amount of data in is a given value.

[0013] θ k ∈θ s ,θ s ={θ min ,θ min +d θ ,θ min +2d θ ,…,θ max}, θ min Represents the minimum dip angle of the fault distribution in the sampling area, θ max is the maximum dip angle of the fault distribution in the sampling area, θ num is θ s The total number of data in θ num Given value.

[0014] S23. Calculate the integral value c(x) of the azimuth and dip angle along the structural layer at each sampling point. The calculation formula of c(x) is as follows:

[0015]

[0016] S24. For each sampling point, only the largest gradient structure tensor value is retained as m(x).

[0017]

[0018] S25, calculate the thinned gradient structure tensor value h(x) of each sampling point to form a tensor thinning attribute body F1, and the calculation formula of h(x) is as follows:

[0019]

[0020] In the formula, Where N is the total number of all inclination and azimuth combinations,

[0021] S3. Perform coherent fracture detection based on the three-dimensional seismic data of the sampling area to form a coherent attribute body F2.

[0022] S4, given threshold value A, when λ in S21 i When the absolute value of is greater than A, the sampling area is a strong tensor development area, otherwise it is a weak or non-dissolution area. The fracture prediction result F3 in the strong tensor development area is the tensor thinning attribute body F1, and the fracture prediction result F3 in the weak or non-dissolution area is the coherent attribute body F2.

[0023] The present invention is further configured as follows: when the maximum inclination angle θ of the fracture distribution in the sampling area max When it is not possible to measure, θ max Set to 85°.

[0024] The present invention is further configured as follows: the specific steps of S1 are:

[0025] S11. Determine the sampling points in the sampling area, and calculate the gradient vector g at each sampling point based on the three-dimensional seismic data in the sampling area:

[0026]

[0027] In the formula, x, y, and z are the line and track directions of the three-dimensional seismic body and the time direction of the two-way travel, respectively. g1, g2, and g3 are the partial derivatives of g along the x, y, and z directions, respectively. The partial derivative step sizes used in the calculation process of g1, g2, and g3 are all given values.

[0028] S12. Construct the gradient structure tensor matrix T:

[0029]

[0030] The present invention is further configured such that the partial derivative step lengths used in the calculation process of g1, g2, and g3 are the same.

[0031] The present invention is further configured as follows: the partial derivative step length used in the calculation process of g1, g2, and g3 is 300-600 meters.

[0032] The present invention is further configured as follows: the gradient structure tensor matrix T is a real symmetric matrix, the eigenvalue λ satisfies λ1≥λ2≥λ3>0, and λ in S21 i Select λ2.

[0033] The present invention is further configured as follows: S22 uses the coherent volume technology to calculate each sampling point along the azimuth and the inclination angle θ k All gradient structure tensor values

[0034] The present invention is further configured as follows: S22 specifically performs the following steps on each sampling point:

[0035] S221, Fixed azimuth Fixed inclination angle θ k , obtain three-dimensional seismic data of the sampling points.

[0036] S222. For the three-dimensional seismic data of the sampling point, select J adjacent data channels, collect N data for each channel, and a total of N×J sub-data d to form a seismic sub-volume matrix U1.

[0037]

[0038] S223, obtain the covariance matrix W1, And find all non-zero eigenvalues ​​λ of W1 ji .

[0039] S224, calculate each sampling point, at this azimuth and the inclination angle θ k The gradient structure tensor value under

[0040]

[0041] In the formula, λ j1 represents the maximum eigenvalue of the covariance matrix W1.

[0042] S225, maintain azimuth unchanged, change the inclination angle θ k , obtain the 3D seismic data of the sampling point. Repeat S222-S224 to obtain this azimuth Under different inclination angles θ kAll gradient structure tensor values

[0043] S226, Change Azimuth Obtain 3D seismic data of the sampling point. Repeat S222-S225 to obtain data at different azimuths. Different inclination angles θ k All gradient structure tensor values constitute

[0044] The present invention is further configured as follows: the threshold value in S4 is calibrated by drilling time, and the lower limit of the tensor attribute encountered in the reservoir is used as the threshold value of the abnormal body. In the drilling time calibration, the tensor attribute of the reservoir section is determined by the point-by-point arithmetic average, and the drilling time of the reservoir section is determined by the arithmetic average of the drilling time of the stable section.

[0045] The present invention is further configured as follows: the coherent fracture detection of S3 specifically comprises fixing the azimuth and inclination, acquiring the three-dimensional seismic data of all sampling points in the sampling area, and performing the following steps for each sampling point:

[0046] S31. Select Q adjacent data channels, each channel has M data, a total of Q×M sub-data d, to form a seismic sub-volume matrix U2.

[0047]

[0048] S32, obtain the covariance matrix W2, And find all non-zero eigenvalues ​​λ of W2 qi .

[0049] S33, calculating the coherence value C of each sampling point to form a coherence attribute body F2.

[0050]

[0051] In the formula, λ q1 represents the maximum eigenvalue of the covariance matrix W2.

[0052] The present invention is further configured as follows: the above carbonate rock fracture classification detection method is applied in oil and gas exploration to determine the fracture position, quantity, fracture direction, inclination, fracture surface morphology and characteristics of carbonate rocks.

[0053] In summary, the present invention has the following beneficial effects compared with the prior art: In view of the problem that the complex characteristics of carbonate fracture wave fields affect the fracture detection effect, the present invention performs attribute analysis on the original seismic data body of the target area, classifies the fractures into strong dissolution areas and weak and non-dissolution areas, uses the tensor thinning attribute body as the fracture prediction result of the strong dissolution area, and uses the conventional coherent fracture detection result as the fracture prediction result of the weak and non-dissolution area. Through this classification detection technology, not only the influence of beaded anomalies is effectively eliminated to form the detection results of strong dissolution fractures, but also the conventional small and medium-scale fractures characterized by phase axis disturbance and fault fault are retained. The two are organically integrated in space, which effectively improves the detection accuracy of carbonate rock fractures. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is the seismic profile of carbonate rock faults in the target area.

[0055] Figure 2 Schematic diagram of the process of the embodiment.

[0056] Figure 3 It is the tensor profile of the fault structure in the target area.

[0057] Figure 4 It is a structural tensor thinning profile of the target area fault.

[0058] Figure 5 This is the conventional coherent attribute profile of the target area fault.

[0059] Figure 6 This is a conventional coherent plane diagram of the target area fracture.

[0060] Figure 7 The fused fracture profile of the target area.

[0061] Figure 8 This is the plane diagram of the fused fracture in the target area. DETAILED DESCRIPTION

[0062] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the invention.

[0063] Example

[0064] The present invention is described by taking the example of carbonate rock fracture detection in a certain area of ​​Tahe Oilfield. Figure 1 This is the fault seismic profile of the area.

[0065] like Figure 2FIG. 1 is a flow chart of an embodiment of the present invention, a carbonate rock fracture classification detection method, which is applied in oil and gas exploration to determine the fracture position, quantity, fracture direction, inclination, fracture surface morphology and characteristics of carbonate rocks, and comprises the following steps:

[0066] S1, such as Figure 3 As shown, the gradient structure tensor matrix T is obtained based on the three-dimensional seismic data of the sampling area.

[0067] S11. Determine the sampling points in the sampling area, and calculate the gradient vector g at each sampling point based on the three-dimensional seismic data in the sampling area:

[0068]

[0069] Wherein, x, y, z are the line and track directions of the three-dimensional seismic body and the time direction of the two-way travel, respectively. g1, g2, g3 are the partial derivatives of g along the x, y, z directions, respectively, and the partial derivative step lengths used in the calculation process of g1, g2, g3 are all given values. The partial derivative step lengths used in the calculation process of g1, g2, g3 are 300-600 meters, and the partial derivative step lengths used in the calculation process of g1, g2, g3 in this embodiment are all 600 meters.

[0070] S12. Construct the gradient structure tensor matrix T:

[0071]

[0072] S2, such as Figure 4 As shown, the gradient structure tensor is thinned to obtain the tensor thinning attribute body F1. The specific steps are:

[0073] S21, obtain all eigenvalues ​​of the gradient structure tensor matrix T, at a certain eigenvalue λ i On the basis of , all sampling points are scanned to obtain a sampling point set G(x) centered on the eigenvalue. The gradient structure tensor matrix T is a real symmetric matrix, and the eigenvalue λ satisfies λ1≥λ2≥λ3>0. The high-value anomaly of λ1 mainly reflects the horizontal event axis, and the horizontal event axis is the part with the most drastic energy change; the signal-to-noise ratio of λ3 is relatively low; and λ2 mainly reflects the chaotic reflection characteristics, which is ideal for the characterization of the fault solution and is consistent with the profile characteristics of the fault solution. Therefore, in this embodiment, λ i Select λ2.

[0074] S22, calculate the azimuth angle of each sampling point and the inclination angle θ k All gradient structure tensor values

[0075] for The total amount of data in is a given value.

[0076] θ k ∈θ s ,θ s ={θ min ,θ min +d θ ,θ min +2d θ ,…,θ max}, θ min Represents the minimum dip angle of the fault distribution in the sampling area, θ max is the maximum dip angle of the fault distribution in the sampling area, θ num is θ s The total number of data in θ num Given value.

[0077] When the maximum inclination angle θ of the fault distribution in the sampling area max When it is not possible to measure, θ max Set to 85°.

[0078] S23. Calculate the integral value c(x) of the azimuth and dip angle along the structural layer at each sampling point. The calculation formula of c(x) is as follows:

[0079]

[0080] S24. For each sampling point, only the largest gradient structure tensor value is retained as m(x).

[0081]

[0082] S25, calculate the thinned gradient structure tensor value h(x) of each sampling point to form a tensor thinning attribute body F1, and the calculation formula of h(x) is as follows:

[0083]

[0084] In the formula, Where N is the total number of all inclination and azimuth combinations,

[0085] S3, such as Figure 5 , Figure 6 As shown, coherent fracture detection is performed based on the three-dimensional seismic data of the sampling area to form a coherent attribute body F2.

[0086] S4, such as Figure 7 , Figure 8 As shown, given the threshold value A, when λ in S21 iWhen the absolute value of is greater than A, the sampling area is a strong tensor development area, otherwise it is a weak, non-dissolution area. The fracture prediction result F3 in the strong tensor development area is the tensor thinning attribute body F1, and the fracture prediction result F3 in the weak, non-dissolution area is the coherent attribute body F2. In this embodiment, the threshold value is calibrated by drilling time, and the lower limit of the tensor attribute encountered in the reservoir is used as the threshold value of the abnormal body. In the drilling time calibration, the tensor attribute of the reservoir section is determined by the point-by-point arithmetic mean, and the drilling time of the reservoir section is determined by the arithmetic mean of the drilling time of the stable section.

[0087] When drilling with the Ordovician fracture-cavity reservoir as the target, emptying and leakage will occur when drilling into the location where fractures and caves are developed. Generally, it is impossible to obtain the well logging curve. The drilling time curve of the side-drilled horizontal well can be used to calibrate the lateral fracture range of the fracture. In order to achieve the mutual combination between the reservoir and the attribute, the drilling time calibration is performed on multiple wells. In this embodiment, the drilling time calibration is performed on 15 wells, and the tensor attributes are extracted at a sampling rate of 0.125m. Through the calibration of the tensor attribute and the drilling time curve, when the drilling time is less than 20min / m, the tensor attribute abnormal value is above 9.

[0088] Specifically, S22 uses coherent volume technology to calculate the azimuth of each sampling point and the inclination angle θ k All gradient structure tensor values Specifically, the following steps are performed for each sampling point:

[0089] S221, Fixed azimuth Fixed inclination angle θ k , obtain three-dimensional seismic data of the sampling points.

[0090] S222. For the three-dimensional seismic data of the sampling point, select J adjacent data channels, collect N data for each channel, and a total of N×J sub-data d to form a seismic sub-volume matrix U1.

[0091]

[0092] S223, obtain the covariance matrix W1, And find all non-zero eigenvalues ​​λ of W1 ji .

[0093] S224, calculate each sampling point, at this azimuth and the inclination angle θ k The gradient structure tensor value under

[0094]

[0095] In the formula, λ j1 represents the maximum eigenvalue of the covariance matrix W1.

[0096] S225, maintain azimuth unchanged, change the inclination angle θ k , obtain the 3D seismic data of the sampling point. Repeat S222-S224 to obtain this azimuth Under different inclination angles θ k All gradient structure tensor values

[0097] S226, Change Azimuth Obtain 3D seismic data of the sampling point. Repeat S222-S225 to obtain data at different azimuths. Different inclination angles θ k All gradient structure tensor values constitute

[0098] Specifically, the coherent fault detection of S3 is to fix the azimuth and dip angle, obtain the three-dimensional seismic data of all sampling points in the sampling area, and perform the following steps for each sampling point:

[0099] S31. Select Q adjacent data channels, each channel has M data, a total of Q×M sub-data d, to form a seismic sub-volume matrix U2.

[0100]

[0101] S32, obtain the covariance matrix W2, And find all non-zero eigenvalues ​​λ of W2 qi .

[0102] S33, calculating the coherence value C of each sampling point to form a coherence attribute body F2.

[0103]

[0104] In the formula, λ q1 represents the maximum eigenvalue of the covariance matrix W2.

[0105] In summary, this method aims to solve the problem that the complex characteristics of carbonate fracture wave fields affect the fracture detection effect. The original seismic data body of the target area is analyzed for attributes, and the fractures are classified into strong dissolution areas and weak and non-dissolution areas. The tensor thinning attribute body is used as the fracture prediction result of the strong dissolution area, and the conventional coherent fracture detection result is used as the fracture prediction result of the weak and non-dissolution area. Through this classification detection technology, not only the influence of beaded anomalies is effectively eliminated to form the detection results of strong dissolution fractures, but also the conventional small and medium-scale fractures characterized by phase axis disturbance and fault fault are retained. The two are organically integrated in space, which effectively improves the detection accuracy of carbonate rock fractures.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A carbonate rock fracture classification detection method, characterized in that: The steps include: S1. Obtain the gradient structure tensor matrix T according to the three-dimensional seismic data of the sampling area; S2, thinning the gradient structure tensor to obtain the tensor thinning attribute body F1, the specific steps are: S21, obtain all eigenvalues ​​of the gradient structure tensor matrix T, at a certain eigenvalue λ i On the basis of, all sampling points are scanned to obtain a sampling point set G(x) centered on the eigenvalue; S22, calculate the azimuth angle of each sampling point and the inclination angle θ k All gradient structure tensor values for The total amount of data in is a given value; θ k ∈θ s ,θ s ={θ min ,θ min +d θ ,θ min +2d θ ,…,θ max }, θ min Represents the minimum dip angle of the fault distribution in the sampling area, θ max is the maximum dip angle of the fault distribution in the sampling area, θ num is θ s The total number of data in θ num Given value; S23. Calculate the integral value c(x) of the azimuth and dip angle along the structural layer at each sampling point. The calculation formula of c(x) is as follows: S24, for each sampling point, only retain the largest gradient structure tensor value as m(x); S25, calculate the thinned gradient structure tensor value h(x) of each sampling point to form a tensor thinning attribute body F1, and the calculation formula of h(x) is as follows: In the formula, Where N is the total number of all inclination and azimuth combinations, S3, performing coherent fracture detection based on the three-dimensional seismic data of the sampling area to form a coherent attribute body F2; S4, given threshold value A, when λ in S21 i When the absolute value of is greater than A, the sampling area is a strong tensor development zone, otherwise it is a weak or non-dissolution zone; the fracture prediction result F3 in the strong tensor development zone is the tensor thinning attribute body F1, and the fracture prediction result F3 in the weak or non-dissolution zone is the coherent attribute body F2.

2. A carbonate rock fracture classification detection method according to claim 1, characterized in that: When the maximum inclination angle θ of the fault distribution in the sampling area max When it is not possible to measure, θ max Set to 85°.

3. A carbonate rock fracture classification detection method according to claim 1 or 2, characterized in that: The specific steps of S1 are: S11. Determine the sampling points in the sampling area, and calculate the gradient vector g at each sampling point based on the three-dimensional seismic data in the sampling area: In the formula, x, y, and z are the line and track directions of the three-dimensional seismic body and the time direction of the two-way travel respectively; g1, g2, and g3 are the partial derivatives of g along the x, y, and z directions respectively, and the partial derivative step sizes used in the calculation process of g1, g2, and g3 are all given values; S12. Construct the gradient structure tensor matrix T:

4. A carbonate rock fracture classification detection method according to claim 3, characterized in that: The partial derivative step size used in the calculation process of g1, g2, and g3 is the same.

5. A carbonate rock fracture classification detection method according to claim 4, characterized in that: The partial derivative step size used in the calculation process of g1, g2, and g3 is 300-600 meters.

6. A carbonate rock fracture classification detection method according to claim 3, characterized in that: The gradient structure tensor matrix T is a real symmetric matrix, and the eigenvalue λ satisfies λ1≥λ2≥λ3>0. i Select λ2.

7. A carbonate rock fracture classification detection method according to claim 1 or 2, characterized in that: S22 uses coherent volume technology to calculate the azimuth angle of each sampling point. and the inclination angle θ k All gradient structure tensor values 8. A carbonate rock fracture classification detection method according to claim 7, characterized in that: S22 specifically involves performing the following steps on each sampling point: S221, Fixed azimuth Fixed inclination angle θ k , obtain three-dimensional seismic data of sampling points; S222, for the three-dimensional seismic data of the sampling point, select J adjacent data channels, collect N data for each channel, and a total of N×J sub-data d to form a seismic sub-volume matrix U1; S223, obtain the covariance matrix W1, And find all non-zero eigenvalues ​​λ of W1 ji ; S224, calculate each sampling point, at this azimuth and the inclination angle θ k The gradient structure tensor value under In the formula, λ j1 represents the maximum eigenvalue of the covariance matrix W1; S225, maintain azimuth unchanged, change the inclination angle θ k , obtain the 3D seismic data of the sampling point; repeat S222-S224 to obtain this azimuth Under different inclination angles θ k All gradient structure tensor values S226, Change Azimuth Acquire three-dimensional seismic data of sampling points; Repeat S222-S225 to obtain different azimuth angles Different inclination angles θ k All gradient structure tensor values constitute 9. A carbonate rock fracture classification detection method according to claim 1, characterized in that: The threshold value in S4 is calibrated by drilling time, and the lower limit of the tensor attribute encountered in the reservoir is used as the threshold value of the anomaly body; in the drilling time calibration, the tensor attribute of the reservoir section is determined by the point-by-point arithmetic mean, and the drilling time of the reservoir section is determined by the arithmetic mean of the drilling time of the stable section.

10. A carbonate rock fracture classification detection method according to claim 1, characterized in that: The specific coherent fault detection of S3 is to fix the azimuth and dip, obtain the 3D seismic data of all sampling points in the sampling area, and perform the following steps for each sampling point: S31, select Q adjacent data channels, each channel has M data, a total of Q×M sub-data d, to form a seismic sub-volume matrix U2; S32, obtain the covariance matrix W2, And find all non-zero eigenvalues ​​λ of W2 qi ; S33, calculating the coherence value C of each sampling point to form a coherence attribute body F2; In the formula, λ q1 represents the maximum eigenvalue of the covariance matrix W2.

11. A carbonate rock fracture classification detection method, characterized by: A carbonate rock fracture classification detection method as described in any one of claims 1 to 10 is used in oil and gas exploration to determine the fracture location, number, fracture direction, inclination, fracture surface morphology and characteristics of carbonate rocks.

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