A gravity identification method for basement faults in sedimentary basins based on regularized inversion

Abstract

The present invention provides a gravity identification method for basement faults in sedimentary basins based on regularized inversion, which specifically includes: gridding measured gravity anomalies to obtain regular grid gravity data, extracting 2.5-dimensional gravity anomaly profiles from the regular grid gravity data along the x and y directions respectively; inverting all 2.5-dimensional gravity anomaly profiles in the x and y directions using a regularized density interface inversion method; calculating the total horizontal gradient thgr according to the inversion result; and identifying the location of the basin basement fault using the maximum position of thgr. Aiming at the characteristics of basement faults in sedimentary basins, the present invention uses regularized density interface inversion, which is more sensitive to basement faults, to identify faults, establishes a model constraint function in the form of an Lp norm in the regularized density interface inversion method, so that the density interface inversion method can better highlight the location of basement faults, and can quickly identify small-scale basement faults in sedimentary basins.

Description

Technical Field

[0001] The invention belongs to the technical field of basin basement fracture position identification, and in particular relates to a sedimentary basin basement fracture gravity identification method based on regularized inversion. Background Art

[0002] Fault structural characteristics play an important role in controlling the formation and evolution of basins, the distribution range of basins, and sedimentation centers. They also have an important impact on the migration and enrichment of oil and gas in basins. Fault interpretation has always been an important part of gravity research on sedimentary basin structures and oil and gas exploration. Existing technologies for identifying faults using gravity anomalies include basic methods such as total horizontal derivative, analytical signal amplitude, Tilt derivative, Theta diagram, and combined methods developed on the basic methods, such as analytical signal amplitude tilt angle method, analytical signal amplitude total horizontal derivative method, tilt angle total horizontal derivative, etc.

[0003] In order to improve the ability of gravity anomaly to identify basin basement faults, the basic potential field edge recognition method is usually enhanced by normalization, boundary enhancement filtering and other technologies. The ratio edge recognition technology constructed by different types of derivatives can better balance the anomalies of deep and shallow geological bodies, which is also one of the main measures to improve the ability to identify deep faults. In addition, the correlation coefficient uses the correlation coefficient of the total horizontal derivative and the vertical derivative for boundary recognition, which is also a common method for identifying deep faults.

[0004] Although the above edge recognition technology can effectively balance the gravity anomaly of deep field sources, it is not effective in identifying small-scale faults in the basement of sedimentary basins when used to identify basement faults in sedimentary basins. The main reason is that the gravity anomaly caused by such faults is very weak, and it is difficult to improve the recognition ability of such faults by directly processing the gravity anomaly. Although measures such as increasing the derivative order can be used to enhance the recognition ability of weak anomalies, the calculation results are often unstable, and the use of smoothing measures will make some details blurred. It is difficult to find the best compromise between improving the recognition accuracy and ensuring the stability of the calculation. It can be seen that from the perspective of calculation alone, it is impossible to process the originally very weak gravity anomaly to obtain a clear identification result of the small-scale fault position in the basement of the basin. It is necessary to change the idea and find an "anomaly" derived from the gravity anomaly that contains clear fault position information, and process it to obtain the position of the basement fault in the basin. Summary of the invention

[0005] The purpose of the present invention is to solve the difficulties existing in the above-mentioned prior art and to provide a gravity identification method for sedimentary basin basement faults based on regularized inversion. The inversion technology is used to derive a data that is closer to the fault location information from the gravity anomaly, and the edge recognition and extraction technology is used to process the data. Small-scale faults in the sedimentary basin basement that cannot be identified by existing methods can be identified.

[0006] The present invention is achieved through the following technical solutions:

[0007] The present invention provides a method for gravity identification of basement fractures in a sedimentary basin based on regularized inversion, the method comprising the following steps:

[0008] Step 1: Grid the measured gravity anomaly to obtain regular grid gravity data, and extract 2.5-dimensional gravity anomaly profiles from the regular grid gravity data along the x and y directions respectively;

[0009] Step 2: Invert all 2.5-dimensional gravity anomaly profiles in the x and y directions using the regularized density interface inversion method;

[0010] Step 3: Calculate the horizontal total gradient thgr according to the inversion result of step 2;

[0011] Step 4: Use the maximum position of thgr to identify the location of basin basement fractures.

[0012] A further improvement of the present invention is:

[0013] Step 2: Invert all 2.5-dimensional gravity anomaly profiles in the x and y directions using the regularized density interface inversion method, including:

[0014] Step 2.1, construct the objective function φ(m) of the regularized density interface inversion method;

[0015] Step 2.2, use the nonlinear conjugate gradient method to solve the minimization problem of the objective function φ(m).

[0016] A further improvement of the present invention is:

[0017] Step 2.1, construct the objective function of the regularized density interface inversion method, specifically:

[0018]

[0019] Where m is a vector of type M×1, which is the base depth of the sedimentary basin and the parameter to be inverted; g p (m) is the gravity anomaly fitted by the model forward modeling; λ is the regularization parameter; τ(m) is the model constraint function, which determines the morphology of the inverted basin basement; It is the measured gravity anomaly data.

[0020] A further improvement of the present invention is:

[0021] The Ekblom norm is used to establish the model constraint function τ(m):

[0022]

[0023] where m s and m t represent two adjacent pairs of basement depth parameters along the profile direction, and L is the number of such pairs; ε is a threshold. When its value is relatively small compared to m, the Ekblom norm is approximately the same as the L p -norm; when the value of p is small, the inversion result may exhibit a non-smooth form. Especially when p ranges from 0 to 0.5, the inversion result is more sensitive to fracture information, which is conducive to highlighting the basement fracture information.

[0024] A further improvement of the present invention lies in:

[0025] Step 2.2: Use the nonlinear conjugate gradient method to solve the minimization problem of the objective function φ(m), which specifically includes the following steps:

[0026] 1) Calculate the gradient of the objective function and let u (k) =-r (k) be the initial search direction;

[0027] 2) Calculate the search step size where A (k) is the Jacobian matrix, which is the partial derivative of the forward-fitting gravity anomaly g p (m) with respect to the inverted basement depth m;

[0028] 3) Update the model m (k+1) =m (k) +α (k) u (k) , calculate and judge: If |r (k+1) |<Err or k>K max then stop the iteration and output m (k+1 ) as the inversion result, otherwise go to the next step;

[0029] 4) Let and calculate the search direction u (k+1) =-r (k+1) +β (k+1) u (k) , let k = k + 1, and go to step 2).

[0030] A further improvement of the present invention lies in:

[0031] Step 3: Calculate the horizontal total gradient thgr according to the inversion result of step 2. The specific operation is as follows:

[0032] The inversion results of all 2.5-dimensional gravity anomaly profiles along the x and y directions are combined into the 3D inversion results basx(x,y) and basy(x,y) along the x and y directions, and the gradients derx(x,y) and dery(x,y) of the 3D inversion results along the x and y directions are calculated using the difference algorithm, and the horizontal total gradient thgr is calculated;

[0033] The overall level

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention aims at the characteristics of basement faults in sedimentary basins, uses regularized density interface inversion which is more sensitive to basement faults to identify faults, establishes a model constraint function in the form of Lp norm in the regularized density interface inversion method, and enables the density interface inversion method to better highlight the location of basement faults;

[0036] In order to solve the problem of slow calculation speed of the regularized inversion method, the present invention reduces the 3D data to 2.5D data, which significantly improves the calculation speed.

[0037] Therefore, compared with the existing methods, the present invention can quickly identify small-scale fractures in the basement of sedimentary basins. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the gravity identification method of basement faults in sedimentary basins based on regularized inversion;

[0039] Figure 2 It is a 3D stepped fault basin basement model;

[0040] Figure 3 yes Figure 2 Gravity anomalies caused by the basement of the fault basin;

[0041] Figure 4 It is the 2.5-dimensional regularized inversion result of all sections along the x direction;

[0042] Figure 5 It is the 2.5-dimensional regularized inversion result of all profiles along the y direction;

[0043] Figure 6 is the processing result of the horizontal total gradient thgr;

[0044] Figure 7 It is the commonly used total horizontal derivative processing result;

[0045] Figure 8 It is the processing result of normalizing the total horizontal derivative and vertical derivative using the existing commonly used enhanced method. DETAILED DESCRIPTION

[0046] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0047] The present invention provides a method for identifying basement fractures in sedimentary basins enhanced by regularized density interface inversion method. The method uses a model constraint function in the form of Lp norm for inversion and uses a 2.5-dimensional model to reduce the 3D order, which significantly enhances the ability of gravity to identify small-scale fractures in the basement of sedimentary basins and improves the calculation speed.

[0048] In order to solve the above technical problems, the present invention provides a method for gravity identification of basement faults in sedimentary basins based on regularized inversion, comprising the following steps:

[0049] Step 1: Grid the measured gravity anomaly to obtain regular grid gravity data, and extract 2.5-dimensional gravity anomaly profiles from the regular grid gravity data along the x and y directions, where the number of profiles in the x and y directions is n and m, respectively;

[0050] The measured gravity anomaly refers to the actual measured gravity anomaly of the entire basin basement, including the fault location and other areas.

[0051] The 2.5-dimensional gravity anomaly profiles are extracted from the regular grid gravity data along the x and y directions. Figure 3 Take the gravity anomaly of as an example, the gravity anomaly is grid data, the grid spacing in the x direction and the y direction is 1km, and the total number of data points is 71 (x direction) × 75 (y direction). The x-direction profile is the east-west profile, with a total of 75 profiles, and the y-direction profile is the north-south profile, with a total of 71 profiles. The extraction process is equivalent to discretizing the grid data into many profile data, which is equivalent to discretizing the two-dimensional array into many one-dimensional arrays according to the x direction or the y direction in the specific program.

[0052] Step 2: Invert all 2.5-dimensional gravity anomaly profiles in the x and y directions using the regularized density interface inversion method;

[0053] It includes the following two steps:

[0054] Step 2.1, construct the objective function of the regularized density interface inversion method:

[0055]

[0056] Where m is a vector of type M×1, which is the base depth of the sedimentary basin and the parameter to be inverted; g p (m) is the gravity anomaly fitted by the model forward modeling; λ is the regularization parameter; τ(m) is the model constraint function, which determines the morphology of the inverted basin basement; It is the measured gravity anomaly data.

[0057] To highlight the faults in the sedimentary basin basement, it is necessary to present the fault positions of the basin basement as much as possible in the 2.5D inversion. Therefore, the present invention uses the Ekblom norm to establish the model constraint function τ(m), which can be written as:

[0058]

[0059] where m s and m t represent two adjacent basement depth parameter pairs along the profile direction, L is the number of parameter pairs; ε is a threshold. When its value is relatively small compared to m, the Ekblom norm is approximately the same as the L p -norm. When p takes a small value (e.g., less than 1), the inversion result can show a non-smooth form. Especially when p ranges from 0 to 0.5, the inversion result is more sensitive to the fault information, which is beneficial to highlighting the basement fault information;

[0060] Step 2.2: Use the non-linear conjugate gradient method to solve the minimization problem of the objective function φ(m),

[0061] which specifically includes the following 4 steps:

[0062] 1) Calculate the gradient of the objective function and let u (k) =-r (k) be the initial search direction;

[0063] 2) Calculate the search step size where A (k) is the Jacobian matrix, which is the partial derivative of the forward-fitting gravity anomaly g p (m) with respect to the inverted basement depth m;

[0064] 3) Update the model m (k+1) =m (k) +α (k) u (k) , calculate and judge: If |r (k+1) |<Err or k>K max then stop the iteration and output m (k+1) as the inversion result, otherwise go to the next step;

[0065] 4) Let and calculate the search direction u (k+1) =-r (k+1) +β (k+1) u (k) , let k = k + 1, and go back to step 2).

[0066] Step 3: The inversion results of all 2.5-dimensional gravity anomaly profiles along the x and y directions are combined into the 3D inversion results basx(x, y) and basy(x, y) along the x and y directions, and the gradients derx(x, y) and dery(x, y) of the 3D inversion results along the x and y directions are calculated using the difference algorithm, and the horizontal total gradient thgr is calculated;

[0067] Overall level

[0068] Step 4: Use the maximum position of thgr to identify the location of basin basement fractures.

[0069] In order to make the technical personnel in this technical field better understand the technical solution of the present invention, the following is a 3D stepped fault basin basement model ( Figure 2 ) as an example, the present invention is further explained, comprising the following main steps:

[0070] (1) The measured gravity anomaly after gridding ( Figure 3 ) extract 2.5-dimensional gravity anomaly profiles along the x and y directions respectively, then use the regularized density interface inversion method to invert all 2.5-dimensional gravity anomaly profiles in the x and y directions, and finally combine the inversion results of all 2.5-dimensional profiles along the x and y directions into 3D inversion results along the x and y directions ( Figure 4 and 5 ). Figure 4 The inversion results along the x direction and Figure 5 The inversion results along the y direction highlight the fault locations in the y and x directions, respectively, and can especially reflect the locations of basement faults at greater depths.

[0071] (2) Use the difference algorithm to calculate the gradients of the 3D inversion results along the x and y directions derx(x, y) and dery(x, y), and calculate the horizontal total gradient thgr, as follows: Figure 6 As shown in Figure 2, the maximum value of the total horizontal gradient can reflect the location of the basement fault in the stepped fault basin.

[0072] The processing result of the present invention has a clear maximum value at the basement fracture position of the fault basin, and the maximum value is narrow, and the spatial resolution is higher.

[0073] Further, in order to illustrate the advantages of the method of the present invention, the existing method is used to process Figure 3 The gravity anomaly shown is Figure 7 and Figure 8 As shown. Figure 7 is the commonly used total horizontal derivative processing result, Figure 8The processing result of the normalized total horizontal derivative and vertical derivative of the existing commonly used enhanced method. It can be seen that the existing processing method has a good effect on the identification of large-scale faults in the shallow layer of the basin basement, but cannot identify the identification problem of small-scale faults in the deep basement. In comparison, the method provided by the present invention can clearly identify the location of small-scale faults in the basement of the sedimentary basin, which has obvious advantages.

[0074] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. A gravity identification method for basement faults in sedimentary basins based on regularized inversion, characterized in that: The method comprises the following steps: Step 1: Grid the measured gravity anomaly to obtain regular grid gravity data, and extract 2.5-dimensional gravity anomaly profiles from the regular grid gravity data along the x and y directions respectively; Step 2: Invert all 2.5-dimensional gravity anomaly profiles in the x and y directions using the regularized density interface inversion method, including: Step 2.1, construct the objective function φ(m) of the regularized density interface inversion method, The objective function of the regularized density interface inversion method is constructed as follows: Where m is a vector of type M×1, which is the base depth of the sedimentary basin, and g is the parameter to be inverted. p (m) is the gravity anomaly fitted by the model forward modeling; λ is the regularization parameter; r(m) is the model constraint function, which determines the morphology of the inverted basin basement; It is the measured gravity anomaly data; Step 2.2, using the nonlinear conjugate gradient method to solve the minimization problem of the objective function φ(m); specifically includes the following steps: 1) Calculate the gradient of the objective function And let u (k) =-r (k) is the initial search direction; 2) Calculate the search step size Among them A (k) is the Jacobian matrix, which is the forward fitting gravity anomaly g p (m) partial derivative with respect to the inverted basement depth m; 3) Update model m (k+1) =m (k) +α (k) u (k) ,calculate And judge if |r (k+1) |<Err or k>K max Then stop the iteration and output m (k+1) as the inversion result, otherwise proceed to the next step; 4) Order And calculate the search direction u (k+1) =-r (k+1) +β (k+1) u (k) , let k = k + 1, go to step 2); Step 3: Calculate the horizontal total gradient thgr according to the inversion result of step 2, the specific operation is: The inversion results of all 2.5-dimensional gravity anomaly profiles along the x and y directions are combined into the 3D inversion results basx(x, y) and basy(x, y) along the x and y directions, and the gradients derx(x, y) and dery(x, y) of the 3D inversion results along the x and y directions are calculated using the difference algorithm, and the horizontal total gradient thgr is calculated; The total horizontal gradient Step 4: Use the maximum position of thgr to identify the location of basin basement fractures.

2. The method for gravity identification of basement faults in sedimentary basins based on regularized inversion according to claim 1 is characterized in that: The Ekblom norm is used to establish the model constraint function τ(m): In the formula, m s and m t Indicates that two adjacent basement depth parameter pairs along the profile direction L is the number of parameter pairs; ε is the threshold value. When its value is small relative to m, the Ekblom norm is p -norm characteristic approximation; when the value of p is small, the inversion result may present a non-smooth shape, especially when p is 0-0.5, the inversion result is more sensitive to the fracture information, which is conducive to highlighting the basement fracture information.

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