A method and system for evaluating observation systems

Abstract

This invention provides a method and system for evaluating observation systems, belonging to the field of three-dimensional seismic acquisition and observation systems. The method evaluates the observation system based on the coverage number, offset, and azimuth of the target stratigraphic unit. The method includes: Step 1: collecting and organizing the coverage number at different offsets and azimuths on sub-regions or surface elements of all observation systems; Step 2: calculating the similarity distance or similarity coefficient of the observation systems; Step 3: analyzing and evaluating the observation systems. This invention achieves multi-parameter and multi-dimensional evaluation of the observation system from multiple parameters such as the coverage number, offset, and azimuth, meeting the requirements for comprehensive quantitative evaluation of the observation system in seismic acquisition design, conforming to field practice, and applicable to both field production and theoretical research.

Description

Technical Field

[0001] This invention belongs to the field of three-dimensional seismic acquisition and observation systems, specifically relating to an evaluation method and system for observation systems. Background Technology

[0002] With the deepening of oil and gas exploration and the development and maturation of 3D seismic exploration technology, it has become one of the important technical solutions to the complex problems currently facing the oil and gas industry. High-density and ultra-high-density 3D seismic technologies have played a crucial role in oil and gas exploration, including conventional complex oil and gas reservoirs, concealed oil and gas reservoirs, and unconventional oil and gas reservoirs.

[0003] The design of the observation system plays a very important role in the engineering of seismic acquisition systems. The fundamental reason is that, given the complexity of seismic geological conditions, the design of the observation system takes into account the factors that affect seismic acquisition, imaging and interpretation, and provides solutions to eliminate or improve these factors, while also striving to effectively reduce the cost of seismic acquisition.

[0004] The design of a 3D observation system involves the estimation of many parameters, such as pixel size, effective coverage count, maximum offset, minimum offset, receiver / line spacing, shot / line spacing, and array width. The size of the target layer and the required seismic resolution determine the pixel size. The effective coverage count depends on the signal-to-noise ratio of the seismic data and the desired signal-to-noise ratio of the seismic profile. The maximum offset must meet the accuracy requirements of velocity analysis for the target layer. Without considering the issue of subdivision of the pixel, the receiver / shot spacing is generally equal to twice the pixel grid. The minimum offset and shallow coverage count will affect the accuracy of shallow layer inversion and static correction modeling, etc. The estimation of receiver / shot spacing is related to the array length, width, and designed coverage count.

[0005] As is well known, an observation system is a combination of shot and receiver lines. Given a known 3D model block with a specified pixel size and coverage number, variations in receiver and shot line spacing influence the length and width of the array, resulting in many different 3D observation systems. Clearly, aside from economic factors, the design and selection of an observation system presents multiple solutions.

[0006] The quality of an observation system depends on the effectiveness of seismic imaging. The basic requirements for a seismic imaging observation system are: ① sufficiently small receiver-line spacing and shot-line spacing to maintain a certain number of coverage times for the shallow target layer, in order to obtain accurate shallow velocity and static correction models; ② sufficiently large offset distance to meet the accuracy requirements of velocity analysis; ③ high coverage times and uniformity of azimuth distribution for the target layer to improve the signal-to-noise ratio of the profile.

[0007] Starting from the basic requirements of seismic imaging for the observation system, key parameters such as the number of shallow target layer coverages, the number of effective target layer coverages, the uniformity of azimuth distribution, and the maximum offset can be regarded as a multi-dimensional space composed of tensors such as offset, azimuth, and effective coverages of the three-dimensional observation system. The observation system is a surface or point in the multi-dimensional space. By calculating the similarity of the multi-dimensional space surface or the distance between points, the similarity of the observation system can be evaluated and the observation system can be optimized. Summary of the Invention

[0008] The purpose of this invention is to solve the problems existing in the prior art and provide an evaluation method and system for observation systems. This method is based on a multi-parameter and multi-dimensional evaluation method, such as the number of target layer coverages, offset, and azimuth, to meet the requirements for comprehensive quantitative evaluation of observation systems in seismic acquisition design.

[0009] This invention is achieved through the following technical solution:

[0010] In a first aspect, the present invention provides an evaluation method for an observation system, the method evaluating the observation system based on the coverage number, offset, and azimuth of the target stratum.

[0011] A further improvement of the present invention is that:

[0012] The method includes:

[0013] Step 1: Collect and organize the coverage counts at different offsets and azimuths on sub-regions or surface elements of all observation systems;

[0014] Step 2: Calculate the similarity distance or similarity coefficient of the observation system;

[0015] Step 3: Analyze and evaluate the observation system.

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

[0017] The operations in step 1 include:

[0018] The offset and azimuth of all coverages on a sub-area or surface element are directly calculated using the coordinates of the shot receiver, and the number of coverages is obtained by classification and statistics.

[0019] A further improvement of the present invention is that:

[0020] The operations in step 1 include:

[0021] Offset, azimuth, and coverage number can be obtained directly from the rose diagram in the 3D observation system design software.

[0022] A further improvement of the present invention is that:

[0023] The operations in step 2 include:

[0024] Based on the seismic geological conditions and geological tasks, the weighting coefficient κ for different target stratigraphic systems is determined. kj ;

[0025] The similarity distance D or similarity coefficient S between observation systems A and B is calculated using the following formula:

[0026]

[0027]

[0028] Where: K and J are the number of segments for offset distance and azimuth angle division, respectively; N kjA N is the coverage count of the k-th offset and j-th azimuth range in observation system A. kjB It represents the number of times the range of the k-th offset and the j-th azimuth angle is covered in the B observation system.

[0029] A further improvement of the present invention is that:

[0030] The operations in step 3 include:

[0031] The smaller the similarity distance D and the larger the similarity coefficient S, the more similar the two observation systems are; conversely, the greater the difference between the two observation systems.

[0032] A further improvement of the present invention is that:

[0033] Step 3 further includes:

[0034] The observation system is selected based on the principle of economy: if observation system A is more economical than observation system B, then observation system A is selected as the optimal observation system.

[0035] A further improvement of the present invention is that:

[0036] Step 3 further includes:

[0037] The observation system is selected based on technical principles: the union of observation systems A and B is used as the standard observation system;

[0038] Calculate the similarity distance D or similarity coefficient S between observation system A and the standard observation system, and the similarity distance D or similarity coefficient S between observation system B and the standard observation system;

[0039] Choose the observation system with the smaller similarity distance D or the larger similarity coefficient S as the optimal observation system.

[0040] A second aspect of the present invention provides an evaluation system for an observation system, the system comprising:

[0041] Acquisition Unit: Used to collect and organize the number of coverages at different offsets and azimuths on sub-regions or surface elements of all observation systems;

[0042] Calculation unit: connected to the acquisition unit, used to calculate the similarity distance or similarity coefficient of the observation system;

[0043] Evaluation unit: Connected to the computing unit, used for analyzing and evaluating the observation system.

[0044] A third aspect of the present invention provides a computer-readable storage medium storing at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the above-described observation system evaluation method.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] This invention enables the evaluation of the observation system from multiple parameters and dimensions, such as the number of times the target layer is covered, the offset, and the azimuth. It meets the requirements for comprehensive quantitative evaluation of the observation system in seismic acquisition design, conforms to actual field conditions, and is applicable to field production and theoretical research. Attached Figure Description

[0047] Figure 1-1 Schematic diagram of the gun-receiver pair vector;

[0048] Figure 1-2 Schematic diagram of surface element vector set;

[0049] Figure 2 A flowchart illustrating the steps of the method of this invention. Detailed Implementation

[0050] The present invention will now be described in further detail with reference to the accompanying drawings:

[0051] This invention provides a method for the quantitative evaluation and optimization of three-dimensional observation systems. It has the advantages of rigorous derivation, clear physical meaning, simple calculation, and reasonable conclusions. It is applicable to field production and has great significance for the quantitative design of observation systems. It has broad application prospects.

[0052] This invention proposes an observation system evaluation method (FLA) based on the tensor of target layer coverage number (F)-offset distance (L)-azimuth angle (A), which is applied to the design evaluation and optimization of three-dimensional observation systems.

[0053] The principle of the method of this invention is as follows:

[0054] 1. Basic concepts of the coverage number-offset-azimuth tensor

[0055] Assume the shot point and the check point are S respectively.i (Xsi, Ysi), R i (Xri, Yri), where the center point of the element is M(Xm, Ym). Any shot-receiver pair SR i This constitutes one effective coverage count of element M centered on its center point ¤ (this symbol represents the center point of the element). For element M, the shot-receiver ratio to SR... i The contribution of the number of times the element is covered is the offset and azimuth function, which can be represented by a vector. Description (e.g.) Figure 1-1 (As shown)

[0056] For a surface element M with N coverage times, it can be viewed as a set consisting of many shot-receiver pairs (e.g., Figure 1-2 As shown), denoted as:

[0057]

[0058] Different target layers have different ∪M.

[0059] Dividing the set ∪M into segments K and J, i.e., K*J different zones, according to the burial depth of the target layer and a certain offset distance and azimuth angle, we have:

[0060]

[0061]

[0062] In the formula: K and J are the number of segments for the offset distance and azimuth angle, respectively, representing a certain offset distance or azimuth angle range; N kj It represents the number of coverages within the range of the k-th offset and the j-th azimuth. The observation system is a set consisting of coverage counts minus offsets and azimuths.

[0063] any one It can be viewed as a dimension, all of them This forms a K*J dimensional space, called the Coverage Count-Offset-Azimuth Tensor matrix, i.e., the Coverage Count-Offset-Azimuth Tensor, N ij It is Corresponding to the magnitude of this dimension vector, the tensor can be understood as the coordinate axes of a K*J dimensional space, N. ij It represents a point in space or on a certain coordinate axis. The set ∪M is a tensor matrix in a K*J dimensional space, and the observation system is a K*J dimensional space with an intercept of N. ij A point.

[0064] The coverage number-offset-azimuth tensor is similar to the azimuth-offset coverage number analysis in seismic acquisition design software, as shown in Table 1. Here, the offset is divided into 5 segments, and the azimuth is divided into 12 segments, forming a 5*12 dimensional space. The coverage number N is the magnitude of the vector in this dimension. Table 1 shows the calculation results of the relative coordinates of the vectors and the vector subtended angle of the simulated observation system A.

[0065]

[0066] Table 1

[0067] 2. Similarity assessment methods for observation systems

[0068] Suppose there are two observation systems, A and B. In K-dimensional space, the difference between observation systems A and B is the total difference in the number of coverages across multiple dimensions of offset and azimuth (the sum of the differences in the number of coverages at different offsets and azimuths). Since the vector set of all observation systems can be represented as distinct points in space, the similarity between observation systems A and B can be described by the average of the relative distance between two points in K*J-dimensional space. Therefore:

[0069]

[0070] Coverage count N kj Viewed as a set in a K*J dimensional space, the similarity of observation systems can be described by the similarity of two sets, as follows:

[0071]

[0072] Where: Min is the intersection of the sets, taking A and BN. kj The minimum value (i.e., finding the minimum number of covers between A and B), where Max is the union of sets, and A and B are N. kj The maximum value (i.e., finding the maximum number of times A and B are covered).

[0073] D is the similarity distance, and S is the similarity coefficient. They are calculated using different methods.

[0074] Under different seismic geological conditions, the distribution of offset and azimuth coverage frequency of the observation system has varying impacts on seismic imaging. In the quantitative evaluation of actual seismic acquisition and observation systems, subsets of dimensions and subsets representing dimensions can be used, or different weights can be assigned to different dimensions based on their importance in seismic imaging. For example, the subset of azimuth angles with offsets less than 100 in Table 1 {100|0,0,1,1,0,0,0,0,1,1,0,0} can be summed to {100|4}, etc. From formulas 4 and 5, we have:

[0075]

[0076]

[0077] In the formula: κ kj These are the weighting coefficients. The "∩" in the above formula represents the union operation in mathematics.

[0078] Table 2 shows observation system B, which has the same number of coverage times as Table 1 (the actual number of coverage times for observation system design evaluation is usually the same), both being 540 times.

[0079]

[0080] Table 2

[0081] According to the calculation results of Formula 4: the distance coefficient between observation systems A and B is D = 0.067.

[0082] According to the results calculated by Formula 5, the similarity coefficient between observation systems A and B is S = 0.9355.

[0083] Considering the impact of different offset coverage times on target layer imaging, we assume a weighting factor of 2 for offsets <100, a weighting factor of 3 for offsets 2000-4000, and a weighting factor of 0 for other offsets (for the selection of the target layer, if the target layer depth is <4000m, an offset greater than 4000m is meaningless). The results calculated by formulas 6 and 7 are: D = 0.075, S = 0.942.

[0084] The more similar the observation systems, the smaller D and the larger S. Analysis of the calculation results shows that observation systems A and B have a high degree of similarity.

[0085] This invention defines the basic concept of the coverage number-offset distance-azimuth tensor. Based on the mathematical formula of the distance between two points or the similarity of sets in multidimensional space, it gives a method for calculating the similarity of observation systems based on the coverage number-offset distance-azimuth tensor. The results are practical in production, and an offset distance-azimuth weighting coefficient is proposed. The formula is rigorous and the physical meaning is clear.

[0086] The embodiments of the method of the present invention are as follows:

[0087] Example 1

[0088] An evaluation method for observation systems includes three steps:

[0089] Step 1: Collect and organize the coverage counts at different offsets and azimuths of all observation system sub-regions or surface elements (the smallest area divided by two adjacent shot lines and two receiver lines in an observation system sub-region), or the coverage counts at different azimuths of the main target stratigraphic system.

[0090] The selection of the offset range should take into account the depth of the main target layer; the division of the azimuth should take into account the effective coverage times of the target layer. Data can be directly calculated from the coordinates of the shot receiver to obtain the offset and azimuth of all coverages in a sub-area or area, and the coverage times can be classified and statistically analyzed; alternatively, it can be directly downloaded from the three-dimensional observation system design software (rose diagram).

[0091] In step 1, the most efficient method is to download the data using the surface element azimuth attribute analysis in the 3D observation system design software. The downloaded data should include offset-azimuth coverage data for all surface elements in a sub-region at different target layers.

[0092] Step 2: Calculate the similarity distance or coefficient of the observation system. Based on the seismic geological conditions and geological tasks, determine the weighting coefficients for different target stratigraphic systems;

[0093] For different observation systems, calculate the difference between D or S.

[0094] In step 2, based on the seismic geological conditions, the weighting coefficients for different offsets (target layers) should be determined. Key factors that are related to data processing and seismic imaging should be given priority consideration, such as the number of shallow layer coverages, the distribution of the azimuth of the target layer coverages, the distribution of the maximum offset, etc., to ensure the objectivity of the quantitative evaluation of the observation system.

[0095] Step 3: Analyze and evaluate the observation system.

[0096] The general principle for evaluating observation systems is: the smaller D is and the larger S is, the more similar the two observation systems are, and vice versa.

[0097] Based on the similarity evaluation of observation systems, combined with the economic evaluation of observation systems, it is possible to achieve the optimal selection of observation systems based on quantitative analysis.

[0098] In step 3, the similarity evaluation of the observation systems simply represents the degree of similarity between two observation systems. When selecting an observation system, one can directly choose based on the principle of economy. For example, given two observation systems, A and B, if A is more economical than B, then A is selected, making it the optimal observation system. Alternatively, the union of observation systems A and B can be used as the standard observation system. Based on the weighting principle that benefits data processing and seismic imaging, the observation system is evaluated and selected (when economy is the dominant principle, the difference between the two is small, and the more economical observation method, such as A, can be chosen; when technology is the dominant principle, A and B are merged into a standard observation system (combining the advantages of both systems), and the best system is selected based on the evaluation results). For example, in the above example, the surface layer weighting coefficient is 2, the shallow layer (200-500) is 1, the target layer (2000-4000) weighting coefficient is 1, and the layer greater than 4000 is 1. The merged observation systems are:

[0099] D B =0.0246,D A =0.0640,S B =0.9754,S A =0.9507 (calculated using formulas 6 and 7)

[0100] The smaller the D value and the larger the S value, the more similar the observation system is to the standard observation system. The calculation results show that observation system B is slightly better than A (the smaller the D value and the larger the S value, the more similar the observation system is to the standard observation system). This is reflected in the fact that the number of shallow layer coverages is significantly better for B than for A. Therefore, observation system B is selected as the optimal observation system.

[0101] This invention provides an evaluation method for observation systems based on the three-dimensional target stratigraphic coverage number (F) - offset distance (L) azimuth angle (A) tensor. Compared with traditional graphical analysis or qualitative evaluation, it has the advantages of clear physical meaning, rigorous formula derivation, and concise and scientific expression, and is applicable to field production and theoretical research.

[0102] This invention relates to a method and principle for calculating the distance between two points in a multidimensional space and the similarity of a multidimensional set in a space. Using the number of coverages at different offsets and azimuths in a sub-region as source data, a multidimensional offset-azimuth vector is constructed in space. The number of coverages is the modulus of the offset-azimuth vector. The observation system is a point in space or a set of elements with offset-azimuth as its feature. The similarity of the observation system is evaluated using the distance between spatial points or the similarity of the sets. The observation system is then optimized by referencing its economic indicators and geological performance.

[0103] The present invention also provides an evaluation system for observation systems, and an embodiment of the system is as follows:

[0104]

Example 2

[0105] The system includes:

[0106] Acquisition Unit: Used to collect and organize the number of coverages at different offsets and azimuths on sub-regions or surface elements of all observation systems;

[0107] Calculation unit: connected to the acquisition unit, used to calculate the similarity distance or similarity coefficient of the observation system;

[0108] Evaluation unit: Connected to the computing unit, used for analyzing and evaluating the observation system.

[0109] The present invention also provides a computer-readable storage medium, embodiments of which are as follows:

[0110]

Example 3

[0111] The computer-readable storage medium stores at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the above-described observation system evaluation method.

[0112] This invention is applicable to theoretical research and field production of three-dimensional seismic acquisition and observation systems. It provides a method for quantitative evaluation and optimization of three-dimensional observation systems, with advantages such as rigorous derivation, clear physical meaning, simple calculation, and reasonable conclusions. It is suitable for field production, has great significance for the quantification of observation system design, and has broad application prospects.

[0113] Finally, it should be noted that the above technical solution is only one embodiment of the present invention. For those skilled in the art, based on the application methods and principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the methods described in the above specific embodiments of the present invention. Therefore, the methods described above are only preferred and have no limiting significance.

Claims

1. An observation system evaluation method characterized by: The method evaluates the observation system based on the number of coverages, offset distance and azimuth angle of the target layer system, and comprises step 2: calculating the similarity distance or similarity coefficient of the observation system, comprising: According to the seismic geological conditions and geological tasks, the weighting coefficients ω of different target layer systems are determined kj ; The weighted similarity distance D between the A observation system and the B observation system is calculated using the following equation ω or the weighted similarity coefficient S ω : Wherein: K, J are offset distance, the number of azimuth angle division segment respectively; N kjA is the coverage times of the kth offset distance and the jth azimuth angle range in A observation system, kjB is the coverage times of the kth offset distance and the jth azimuth angle range in B observation system, ω S represents the weighted similarity distance, ω ω represents the weighted similarity coefficient, kj is the weighting coefficient.

2. The observation system evaluation method of claim 1, wherein: Before step 2, the method comprises: Step 1: collecting and organizing the number of coverages of different offset distances and different azimuth angles on the sub-area or bin of all observation systems; After step 2, the method comprises: Step 3: analyzing and evaluating the observation system.

3. The method of claim 2, wherein: The operation of step 1 comprises: The offset distance and azimuth angle of all coverages on the sub-area or bin are directly calculated through the coordinates of the shot points, and the number of coverages is classified and counted.

4. The method of claim 2, wherein: The operation of step 1 comprises: The offset distance, azimuth angle and number of coverages are directly downloaded from the rose diagram in the three-dimensional observation system design software.

5. The method of claim 1, wherein: The operation of step 3 comprises: the weighted similarity distance D ω the smaller, the weighted similarity coefficient S ω the greater, the more similar the two observation systems, and vice versa, the greater the difference between the two observation systems.

6. The method of claim 2, wherein: The operation of step 3 further comprises: Selecting the observation system according to the economic principle: if the A observation system is more economical than the B observation system, the A observation system is selected as the optimal observation system.

7. The method of claim 5, wherein: The operation of step 3 further comprises: Selecting the observation system according to the technical principle: The union of the A observation system and the B observation system is taken as the standard observation system. calculating a weighted similarity distance D between the A observation system and the standard observation system ω or a weighted similarity coefficient S ω , a weighted similarity distance D between the B observation system and the standard observation system ω or a weighted similarity coefficient S ω ; Selecting a similarity distance D ω Small observation systems, or similarity coefficient S ω Large observation systems as optimal observation systems.

8. An observation system evaluation system characterized by: The system comprises: A collection unit for collecting and organizing the number of coverages of different offset distances and different azimuth angles on the sub-area or bin of all observation systems; A calculation unit connected with the collection unit for calculating the similarity distance or similarity coefficient of the observation system, comprising: According to the seismic geological conditions and geological tasks, the weighting coefficients ω of different target layer systems are determined kj ; The weighted similarity distance D between the A observation system and the B observation system is calculated using the following equation ω or the weighted similarity coefficient S ω : Wherein: K, J are offset distance, the number of azimuth angle division segment respectively; N kjA is the coverage times of the kth offset distance and the jth azimuth angle range in A observation system, kjB is the coverage times of the kth offset distance and the jth azimuth angle range in B observation system, ω S represents the weighted similarity distance, ω ω represents the weighted similarity coefficient, kj is the weighting coefficient; An evaluation unit connected with the calculation unit for analyzing and evaluating the observation system.

9. A computer-readable storage medium, characterized in that: The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the observation system evaluation method according to any one of claims 1-7.

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