Interpretation horizon jump point identification and correction method based on variable balance difference algorithm

Abstract

The invention belongs to the technical field of seismic reflection interface tracking interpretation and quality control in the field of oil exploration and development, and discloses an interpretation horizon jump point recognition and correction method based on a variable balance difference algorithm. The method comprises the following steps of: calculating a jump point value generated due to non-closing at a horizon intersection point, determining a jump point coordinate or a lane number so as to quickly position a jump point, and calculating a correction value and automatically modifying a corresponding explanation scheme by taking the jump point as a circle center and utilizing a variable balance difference value algorithm according to the jump point value. The horizon explaining algorithm is applied to the horizon explaining process, the horizon explaining algorithm is applied to the horizon explaining process, the horizon explaining efficiency and precision are greatly improved, and the horizon explaining method has the advantages that the horizon explaining efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of seismic reflection interface tracking interpretation and quality control technology in the field of petroleum exploration and development, and the present invention relates to an interpretation layer jump point identification and correction method based on a variable balance difference algorithm. The present invention specifically relates to an interpretation layer jump point automatic identification and correction method based on a cross variable balance difference algorithm. Background Art

[0002] The exploration and development process of oil and gas reservoirs is an extremely complex process of comprehensive application of multiple disciplines, and mutual verification and complementation of research results. The success of oil and gas reservoir exploration and development is also a process of coordinated cooperation among multiple disciplines. In this process, each discipline has the components of basic research and technological innovation. For the final exploration and development plan, basic research and technological innovation are equally important. When facing common problems, we usually need to continuously consolidate basic research. However, the geological targets faced in the process of oil exploration and development are indeed complex and changeable. This requires oil exploration and development workers to continuously innovate technology and continuously improve accuracy. During the entire research process, it is necessary to carry out quality control for each link such as logging, geology, seismic, and engineering to eliminate the adverse effects of technical shortcomings in the entire process. When it comes to how to understand underground structures and reservoirs in the early stages of exploration and development, using logging information to solve point problems obviously cannot meet the needs of exploration and development. Stratum tracking and attribute extraction based on seismic data can greatly improve the description accuracy of inter-well reservoirs and structures and improve the success rate of exploration and development.

[0003] In the usual layer tracing process, it is carried out by converting the three-dimensional seismic data into two dimensions. In this process, the three-dimensional seismic data is actually digitized according to the three directions of X, Y, and Z, and the analog signals are converted and recorded in the form of arrays to facilitate computer processing. This means that in the layer tracing process, the seismic reflection time Z (Time) value of the geological layer has to be determined respectively according to the two directions of X (Line) and Y (Trace). Figure 3 It is the Y direction in the interpretation process. In this direction, the stratum interpretation is manually tracked according to the maximum wave crest. In the X direction, due to different understandings, the interpretation schemes are also very different. Obvious jump points appear in some directions. These jump points will leave serious "interpretation footprints" on the attribute plane map during the subsequent attribute analysis, affecting the accuracy of the interpretation results. If you want to eliminate this jump point, you need to compare back and forth in two directions. Not only is the efficiency low, but the accuracy is also difficult to meet the requirements. Usually, "pressing down one problem will cause another to float up." If this basic work takes too long or has too low accuracy, it will seriously affect the reliability of subsequent results. Therefore, if the accuracy and efficiency of stratum interpretation can be improved, the success rate of exploration and development will be greatly improved, the cost of discovering tons of oil will be reduced, the benefits will be mobilized, and the profits for the company will be increased. Summary of the invention

[0004] In the usual layer tracing process, it is carried out by converting the three-dimensional seismic data into two dimensions. In this process, the three-dimensional seismic data is actually digitized according to the three directions of X, Y, and Z, and the analog signals are converted and recorded in the form of arrays to facilitate computer processing. This means that in the layer tracing process, the seismic reflection time Z (Time) value of the geological layer has to be determined in the two directions of X (Line) and Y (Trace) respectively. Due to different understandings, the interpretation schemes are also very different. Obvious jump points appear in some trace directions. These jump points will leave serious "interpretation footprints" on the attribute plane map when the attribute analysis is carried out later, affecting the accuracy of the interpretation results. If you want to eliminate this jump point, you need to use only two directions. It can be compared back and forth, which is not only inefficient but also difficult to meet the required accuracy. Therefore, the present invention aims at this problem. Firstly, the present invention innovatively provides an automatic identification and correction method for interpretation layer jump points based on a cross-variable balance difference algorithm. On the basis of the interpretation layer, the jump point value caused by the non-closure at the layer intersection is calculated, and the jump point coordinates or line number are determined to quickly locate the jump point. According to the size of the jump point value, the correction amount is calculated with the jump point as the center of the circle and the variable balance difference algorithm is used to automatically modify the corresponding interpretation plan. This method proposes a cross-variable balance algorithm for the first time, and is applied to the jump point inspection and modification process with the largest workload in the manual layer interpretation process. Through this algorithm, fast and efficient automatic identification and correction of jump points are achieved, which greatly improves the efficiency and accuracy of layer interpretation.

[0005] The above object of the present invention is achieved through the following technical solutions:

[0006] The present invention can automatically find out the jump points generated in the manual layer interpretation process and automatically correct them within the expected accuracy.

[0007] A method for automatically identifying and correcting jump points in interpretation horizons based on a cross-variable balance difference algorithm comprises the following steps:

[0008] 1) Determine the interpretation horizon and start processing according to the method of the present invention;

[0009] 2) Using the cross jump point fast detection algorithm, the line coordinates of the cross jump point are automatically located according to the set threshold;

[0010] in,

[0011]

[0012]

[0013] N t =|Nx -N y |-T Formula 3

[0014] Where P(x,y) is the intersection point, T is the allowed threshold range, L is the time window range, Nx is the theoretical trend value in the x direction of the jump point; Ny is the theoretical trend value in the y direction of the jump point; Nt is the difference between the theoretical trend values ​​in the x and y directions of the jump point; Nt is the jump point deviation value. x is the x coordinate of the jump point; y is the y coordinate of the jump point; i is a natural number that changes within the window L. For intersection points where Nt is greater than zero, they are marked as jump points, and the deviation value is Nt.

[0015] Step 3) using the variable balance difference algorithm, according to the size of the jump point, within a given time window, calculate the correction amount and automatically modify the layer position;

[0016] in,

[0017]

[0018]

[0019] Among them, P(x,y) is the intersection point, L is the time window range, Nx is the theoretical trend value in the x direction of the jump point; Ny is the theoretical trend value in the y direction of the jump point; Nt is the difference between the theoretical trend values ​​in the x and y directions of the jump point; Nt is the jump point deviation value. x is the x coordinate of the jump point; y is the y coordinate of the jump point; i is a natural number that changes within the window L.

[0020] 4) Check the quality of the corrected layer, and finally realize fast and efficient automatic identification and correction of jump points, while achieving the purpose of improving the efficiency and accuracy of layer interpretation.

[0021] 5) Repeat steps 2)-4) in sequence until all the layer interpretation results of the 3D data volume of the entire work area are identified and modified, the closure standard of the accuracy requirements is met, all jump points that do not meet the accuracy requirements are eliminated, and the interpretation quality control is passed.

[0022] Furthermore, step 1) is the basic work in the process of seismic data interpretation. After the target layer is determined by calibration, the interval of the interpretation line is selected according to the accuracy requirement.

[0023] Furthermore, in step 2), the interpretation results of the seismic data layers are used to establish an array model in two directions of the line. The interpretation values ​​of the line intersections are compared through the above algorithm, and the comparison results are calculated. If the comparison result is not 0, it is considered that there is a "jump point" phenomenon. The jump points within the given threshold range can be extracted and output as data, or displayed on the plane according to coordinates, so that the interpreter can check the distribution of the jump points and analyze the causes of the jump points.

[0024] Furthermore, step 3) is based on the jump points calculated in step 2). After analyzing the causes of the jump points, it is determined which jump points will affect the accuracy of subsequent attribute extraction and inversion modeling. For these jump points, a longitudinal time window and a lateral search radius can be given, and automatic correction can be performed according to the above algorithm.

[0025] Furthermore, step 4) is to intuitively display the location and size of the jump point during the interpretation process, conduct real-time interaction, and provide a basis for the jump point classification, time window selection and parameter selection in step 3), and also provide support for improving the efficiency of researchers in analyzing jump points.

[0026] The beneficial effects of the present invention compared with the prior art are:

[0027] The present invention provides an automatic identification and correction method for interpretation horizon jump points based on a cross-variable balance difference algorithm. The method proposes a cross-variable balance algorithm for the first time and is applied to the jump point inspection and modification process, which is the most labor-intensive process in the manual horizon interpretation process. Through the algorithm, fast and efficient automatic identification and correction of jump points are achieved, greatly improving the efficiency and accuracy of horizon interpretation.

[0028] The present invention solves the problem of multiple solutions of seismic data interpretation in the process of oil and gas exploration and development from the perspective of the basic work of seismic data interpretation. The present invention has the characteristics of simple operation, high degree of automation, flexibility and convenience. It can check the distribution of jump points and display the size of jump points to facilitate manual quality control and selection of appropriate thresholds. Finally, it can also form a map of the distribution of correction amounts and the size of correction amounts as a basis for seismic data evaluation or as a reference for optimizing interpretation schemes, which not only improves work efficiency but also improves interpretation accuracy.

[0029] Since 2012, it has been well applied in Jilin Oilfield, North China Oilfield and Zigong block of Southwest Oil and Gas Field in the southern Songliao Basin, improving the accuracy of seismic data interpretation in the above-mentioned study areas, reducing the errors of reservoir prediction and inversion in seismic data interpretation, avoiding the multi-solution of reservoir prediction and inversion in seismic data interpretation to a certain extent, and improving the accuracy and efficiency of stratigraphic interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the automatic identification and correction method of interpretation horizon jump points based on the cross-variable balance difference algorithm.

[0031] Figure 2 It is a schematic diagram explaining the layer density and output data format.

[0032] Figure 3 It is a cross-section diagram that interprets the jump points produced by the strata.

[0033] Figure 4It is a plan view of the jump point distribution generated by the interpreted strata.

[0034] Figure 5 It is a comparison chart of the profile interpretation scheme before and after automatic identification of interpretation layer jump points and correction.

[0035] Figure 6 It is a comparison chart of the automatic identification of jump points in the interpretation layer and the plane distribution of jump points before and after correction. DETAILED DESCRIPTION

[0036] The present invention is described in detail below by specific examples, but the protection scope of the present invention is not limited. Unless otherwise specified, the experimental methods adopted in the present invention are all conventional methods, and the experimental equipment, materials, reagents, etc. used can be obtained from commercial sources. The present invention can be implemented by software or system as a carrier. However, it is not specifically limited, and it can output and load data, and the algorithm of the present invention can be applied, without special limitation.

[0037] In order to make the key links, core technologies, implementation effects and advantages of the present invention clearer, the following will be described according to the specific process of the present invention ( Figure 1 ),

[0038] S1. Load interpretation layer data;

[0039] S2. Identify all intersections through a fast intersection detection algorithm;

[0040] S3. Determine whether it is a jump point; if it is a jump point, mark the jump point and display the quality control result; if it is not a jump point, directly display the quality control result.

[0041] S4. Calculate the correction amount and automatically modify the layer position according to the size of the jump point within the given time window through the variable balance difference algorithm; if no correction is required, directly output the quality control result; if correction is required, read the jump point, correct the value near the jump point, and output the corrected layer position data to step S2.

[0042] Taking a certain actual 3D seismic work area in the southern part of Songliao Basin as an example, the present invention is further described in detail in conjunction with the accompanying drawings.

[0043] Example 1

[0044] 1) Select a typical seismic work area with an area of ​​100 square kilometers, a line spacing of 20 meters, and a trace spacing of 20 meters. Manually interpret the horizon and determine the target layer. After the target layer is determined by calibration, the interval of the interpretation line and trace is selected according to the accuracy requirements, and every 4 lines and every 4 traces are interpreted.

[0045] 2) Using the cross jump point fast detection algorithm, the line coordinates of the cross jump point are automatically located according to the set threshold;

[0046] in,

[0047]

[0048]

[0049] N t =|N x -N y |-T Formula 3

[0050] Where P(x,y) is the intersection point, T is the allowed threshold range, L is the time window range, Nx is the theoretical trend value in the x direction of the jump point; Ny is the theoretical trend value in the y direction of the jump point; Nt is the difference between the theoretical trend values ​​in the x and y directions of the jump point; Nt is the jump point deviation value. x is the x coordinate of the jump point; y is the y coordinate of the jump point; i is a natural number that changes within the window L. For intersection points where Nt is greater than zero, they are marked as jump points, and the deviation value is Nt.

[0051] In this step, the interpretation results of the seismic data layers are used to establish an array model in two directions of the line. The interpretation values ​​of the line intersections are compared through the above algorithm, and the comparison results are calculated. If the comparison result is not 0, it is considered that there is a "jump point" phenomenon. The jump points within the given threshold range can be extracted and output as data, or displayed on the plane according to the coordinates ( Figure 3 ), which makes it easier for interpreters to check the distribution of jump points and analyze the causes of jump points.

[0052] Step 3) using the variable balance difference algorithm, according to the size of the jump point, within a given time window, calculate the correction amount and automatically modify the layer position;

[0053] in,

[0054]

[0055]

[0056] Among them, P(x,y) is the intersection point, L is the time window range, Nx is the theoretical trend value in the x direction of the jump point; Ny is the theoretical trend value in the y direction of the jump point; Nt is the difference between the theoretical trend values ​​in the x and y directions of the jump point; Nt is the jump point deviation value. x is the x coordinate of the jump point; y is the y coordinate of the jump point; i is a natural number that changes within the window L.

[0057] This step is based on the jump points calculated in step 2). After analyzing the causes of their occurrence, it is determined which jump points will affect the accuracy of subsequent attribute extraction and inversion modeling. For these jump points, a vertical time window and a horizontal search radius can be given, and automatic correction can be performed according to the above algorithm.

[0058] 4) Check the quality of the corrected layer ( Figure 4), ultimately achieving fast and efficient automatic identification and correction of jump points, while achieving the purpose of improving the efficiency and accuracy of layer interpretation. This step is to intuitively display the location and size of jump points during the interpretation process, facilitate researchers to conduct real-time interactive analysis, and provide a basis for jump point classification, time window selection and parameter selection in step 3), based on which, the efficiency of researchers in analyzing jump points can be improved.

[0059] 5) Repeat steps 2)-4) in sequence until all the layer interpretation results of the 3D data volume of the entire work area are identified and modified, the closure standard of the accuracy requirements is met, all jump points that do not meet the accuracy requirements are eliminated, and the interpretation quality control is passed.

[0060] The embodiment disclosed in this specification is only an example of a unilateral feature of the present invention, and the protection scope of the present invention is not limited to this embodiment, and any other functionally equivalent embodiments fall within the protection scope of the present invention. For those skilled in the art, various other corresponding changes and deformations can be made according to the technical solutions and concepts described above, and all of these changes and deformations should fall within the protection scope of the claims of the present invention.

[0061] The above-described embodiments are only preferred embodiments of the present invention, but not all feasible embodiments of the present invention. For those skilled in the art, any obvious changes made thereto without departing from the principles and spirit of the present invention should be considered to be included in the scope of protection of the claims of the present invention.

Claims

1. A method for automatically identifying and correcting jump points in interpretation horizons based on a cross-variable balance difference algorithm, characterized in that: Based on the interpretation of the layers, the jump point value caused by the non-closure at the layer intersection is calculated, and the jump point coordinates or line number are output to quickly locate the jump point. According to the size of the jump point value, with the jump point as the center of the circle, the variable balance difference algorithm is used to calculate the correction amount and automatically modify the corresponding interpretation plan. This method proposes the cross-variable balance algorithm for the first time and is applied to the jump point inspection and modification process, which has the largest workload in the manual layer interpretation process.

2. The method for automatically identifying and correcting jump points in interpretation horizons based on a cross-variable balance difference algorithm according to claim 1, characterized in that: 1) Determine the interpretation horizon, 2) Using the cross jump point fast detection algorithm, the line coordinates of the cross jump point are automatically located according to the set threshold; in, N t =|N x -N y |-T Formula 3 Among them, P(x,y) is the intersection point, T is the allowed threshold range, L is the time window range, Nx is the theoretical trend value of the jump point in the x direction; Ny is the theoretical trend value of the jump point in the y direction; Nt is the difference between the theoretical trend values ​​of the jump point in the x and y directions; Nt is the jump point deviation value. x is the x coordinate of the jump point; y is the y coordinate of the jump point; i is a natural number that changes within the window L; for the intersection point where Nt is greater than zero, it is marked as a jump point, and the deviation value is Nt; Step 3) using the variable balance difference algorithm, according to the size of the jump point, within a given time window, calculate the correction amount and automatically modify the layer position; in, Among them, P(x,y) is the intersection point, L is the time window range, Nx is the theoretical trend value in the x direction of the jump point; Ny is the theoretical trend value in the y direction of the jump point; Nt is the difference between the theoretical trend values ​​in the x and y directions of the jump point; Nt is the jump point deviation value. x is the x coordinate of the jump point; y is the y coordinate of the jump point; i is a natural number that changes within the window L range; 4) Check the quality of the corrected horizons, and ultimately achieve fast and efficient automatic identification and correction of jump points, while achieving the goal of improving the efficiency and accuracy of horizon interpretation; 5) Repeat steps 2)-4) in sequence until all the layer interpretation results of the 3D data volume of the entire work area are identified and modified, the closure standard of the accuracy requirements is met, all jump points that do not meet the accuracy requirements are eliminated, and the interpretation quality control is passed.

3. The method for automatically identifying and correcting jump points in interpretation horizons based on a cross-variable balance difference algorithm as claimed in claim 2, characterized in that: Step 1) is the basic work in the process of seismic data interpretation. After the target layer is determined by calibration, the interval of the interpretation line is selected according to the accuracy requirements.

4. The method for automatically identifying and correcting jump points in interpretation horizons based on a cross-variable balance difference algorithm as claimed in claim 3, characterized in that: In step 2), the interpretation results of the seismic data layers are used to establish an array model in two directions of the line. The interpretation values ​​of the line intersections are compared through the above algorithm, and the comparison results are calculated. If the comparison result is not 0, it is considered that there is a "jump point" phenomenon. The jump points within the given threshold range can be extracted and output as data, or displayed on the plane according to coordinates, so that the interpreter can check the distribution of the jump points and analyze the causes of the jump points.

5. The method for automatically identifying and correcting jump points in interpretation horizons based on a cross-variable balance difference algorithm as claimed in claim 4, characterized in that: Step 3) is based on the jump points calculated in step 2). After analyzing the causes of the jump points, it is determined which jump points will affect the accuracy of subsequent attribute extraction and inversion modeling. For these jump points, a vertical time window and a horizontal search radius can be given, and automatic correction can be performed according to the above algorithm.

6. The method for automatically identifying and correcting jump points in interpretation horizons based on a cross-variable balance difference algorithm as claimed in claim 5, characterized in that: Step 4) is to intuitively display the location and size of the jump point during the interpretation process, conduct real-time interaction, and provide a basis for the jump point classification, time window selection and parameter selection in step 3). It can also provide support for improving the efficiency of researchers in analyzing jump points.

7. Application of the method according to any one of claims 1 to 6 in the three-dimensional seismic work area in the southern Songliao Basin.

8. The use according to claim 7, characterized in that: The specific process includes: S1. Load interpretation layer data; S2. Identify all intersections through a fast intersection detection algorithm; S3. Determine whether it is a jump point; if it is a jump point, mark the jump point and display the quality control result; if it is not a jump point, directly display the quality control result; S4. Calculate the correction amount and automatically modify the layer position according to the size of the jump point within the given time window through the variable balance difference algorithm; if no correction is required, directly output the quality control result; if correction is required, read the jump point, correct the value near the jump point, and output the corrected layer position data to step S2.

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