A reservoir prediction method for wide-azimuth seismic data based on ellipse minor axis fitting

Abstract

The invention discloses a reservoir prediction method of wide azimuth seismic data based on ellipse minor axis fitting. Firstly, three-dimensional wide azimuth seismic data is collected offshore, and then data analysis is performed on the three-dimensional wide azimuth seismic data to obtain the distribution of azimuth, offset and coverage times of the three-dimensional seismic data. On this basis, the original wide azimuth seismic data is sorted into different sectors, and the seismic gather event axis is flattened by using the residual time difference correction technology. On the basis of the flattened gather, the seismic data is processed to improve the resolution of the seismic gather. Then, reservoir sensitive elastic parameter intersection analysis is carried out to clarify the reservoir sensitive elastic parameters, and pre-stack simultaneous inversion is performed on the sensitive parameters. Finally, the sensitive elastic parameter values ​​of any sample point in the reservoir at different azimuths and their symmetrical azimuths are selected to form a sensitive elastic parameter plane, and ellipse fitting calculation is performed. The reservoir prediction is carried out using the ellipse fitting result.

Description

Technical Field

[0001] The invention belongs to the field of petroleum exploration and development, and in particular relates to a wide-azimuth data reservoir prediction technology based on ellipse minor axis fitting. Background Art

[0002] It is well known that the main cause of anisotropy is the fracture factor. A large amount of data and modeling studies have shown that fractures cause significant azimuth changes in recorded seismic signals. Fractures as reservoirs are mostly carbonate rocks and bedrock blocks, so the research on anisotropy is mostly in carbonate rocks and bedrock areas, while there are fewer studies on clastic reservoir areas. As early as 1969, Nur and Simmons observed in the laboratory that the existence of pressure would cause rocks to produce anisotropy; Thomsen (1988) well explained the azimuthal anisotropy in seismology; Thomsen (1986) showed through experiments that more than 80% of rocks are weakly anisotropic. The research conclusions of predecessors provide a theoretical basis for the study of anisotropy of clastic rocks. In recent years, with the increase in the acquisition of wide-azimuth seismic data, the anisotropy prediction research of reservoirs has become more and more common. Qin Suhua et al. believed that thin interbedded sandstone and mudstone layers were anisotropic and used azimuth data to predict thin clastic reservoirs. They confirmed that the prediction accuracy of the dominant azimuth was better than that of other azimuths and made a good attempt to predict clastic reservoirs using wide azimuth data. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a reservoir prediction method for wide-azimuth data based on ellipse minor axis fitting, which fully utilizes the advantages of wide-azimuth seismic data and highlights the differences in reservoir responses of seismic data in different azimuths to solve the problem of accurate prediction and description of medium-deep reservoirs.

[0004] The objective of the present invention is achieved through the following technical solutions:

[0005] A reservoir prediction method for wide-azimuth seismic data based on ellipse minor axis fitting comprises the following steps:

[0006] (1) Collect three-dimensional wide-azimuth seismic data with an aspect ratio greater than 0.5 at sea;

[0007] (2) Analyze the 3D wide-azimuth seismic data collected at sea to obtain the azimuth, offset and coverage distribution of the 3D wide-azimuth seismic data;

[0008] (3) According to the distribution of azimuth, offset and coverage times of the 3D wide azimuth seismic data, the azimuth m° is taken as the starting azimuth and the azimuth variable dm° is taken as the azimuth range contained in a sector, and the 3D wide azimuth seismic data is sorted into N sectors, where N is the number of sectors.

[0009] (4) using the residual time difference correction technology to flatten the event axes of the three-dimensional wide-azimuth seismic data of the N sectors in step (3), and on the basis of the flattened seismic data, perform a process to improve the resolution of the seismic data, thereby increasing the center frequency of the seismic data to within 10 Hz;

[0010] (5) Carry out reservoir sensitive elastic parameter intersection analysis to clarify reservoir sensitive elastic parameter I;

[0011] (6) For each sector, the data after optimization processing in step (4) are subjected to prestack simultaneous inversion to invert the reservoir sensitive elastic parameter I;

[0012] (7) For any sample point in the reservoir, select the sensitive elastic parameter values ​​of the sample point in different orientations and its symmetrical orientations, then construct a sensitive elastic parameter plane, use the least squares method to perform ellipse fitting calculation, fit an optimal ellipse, and calculate the ellipse minor axis r1;

[0013] (8) Use the calculated ellipse minor axis r1 value to carry out reservoir prediction.

[0014] Furthermore, in step (3), the three-dimensional wide-azimuth seismic data are sorted into N sectors, and the azimuth angle range of sector n (n=1,2…N) is: m°+(n-3 / 2)*dm° to m°+(n-1 / 2)*dm°; at the same time, only the three-dimensional wide-azimuth seismic data within the incident angle of 0-30° are selected for anisotropy analysis.

[0015] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0016] The present invention provides a new method for the anisotropy study of clastic reservoirs, enriching the means of studying the anisotropy of clastic rocks. The present invention uses three-dimensional wide-azimuth seismic data to overcome the limitations of using three-dimensional narrow-azimuth seismic data to study reservoir anisotropy. In the application of wide-azimuth seismic data, the present invention overcomes the errors caused by using only a specific azimuth in the wide-azimuth data to characterize reservoir changes, and also overcomes the average effect caused by using all azimuth information. The method of ellipse minor axis fitting is used to fully utilize the anisotropic response characteristics of the reservoir in the wide-azimuth seismic data, which can better carry out reservoir prediction work. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the reservoir prediction technology based on wide azimuth data fitting based on ellipse minor axis;

[0018] Figure 2 It is the distribution map of azimuth, offset and coverage times of 3D wide-azimuth seismic data;

[0019] Figure 3a and Figure 3b They are respectively the azimuth sorting scheme for wide-azimuth seismic data and the schematic diagram of sorted gathers;

[0020] Figure 4 The middle left, middle, and right are comparison diagrams before gather flattening, after flattening, and after frequency spreading respectively;

[0021] Figure 5 is the longitudinal wave impedance-density cross plot;

[0022] Figures 6a to 6d These are the inversion density profiles through the LB1 and LB4 wells at -49 / 131 degrees, -4 / 176 degrees, 41 / 221 degrees azimuth and full-azimuth angles, respectively;

[0023] Figure 7 is the single-point ellipse fitting result;

[0024] Figure 8 Density inversion profile fitted to the minor axis of the ellipse;

[0025] Fig. 9 Fit the density inversion plane to the minor axis of the ellipse. DETAILED DESCRIPTION

[0026] like Figure 1 As shown, the present invention provides a wide-azimuth data reservoir prediction technology based on ellipse minor axis fitting, comprising the following steps:

[0027] Step 1: Collect 3D wide-azimuth seismic data with an aspect ratio greater than 0.5 at sea;

[0028] Step 2: Analyze the 3D wide-azimuth seismic data collected offshore to obtain the azimuth, offset and coverage distribution of the 3D wide-azimuth seismic data;

[0029] Step 3: According to the distribution of azimuth, offset and coverage of 3D wide azimuth seismic data, with azimuth m° as the starting azimuth and azimuth variable dm° as the azimuth range of a sector, the 3D wide azimuth seismic data is sorted into N sectors, where the azimuth range of sector n (n=1,2…N) is: m°+(n-3 / 2)*dm° to m°+(n-1 / 2)*dm°, and the average azimuth of sector n is m°+(n-1)*dm°. At the same time, considering that the uneven distribution of offset and coverage of the gather data in each azimuth sector will cause anisotropy illusion, only seismic data within the incident angle of 0-30° are selected for anisotropy analysis;

[0030] Step 4: Use the residual time difference correction technology to flatten the phase axis of the three-dimensional wide-azimuth seismic data of the N sectors in the third step, and improve the resolution of the seismic data on the basis of the flattened data; increase the center frequency of the seismic data within 10Hz.

[0031] Step 5: Carry out reservoir sensitive elastic parameter intersection analysis to clarify the reservoir sensitive elastic parameter I.

[0032] Step 6: For the data in each sector that has been optimized in the fourth step, perform pre-stack simultaneous inversion to invert the reservoir sensitive elastic parameter I.

[0033] Step 7: For any sample point in the reservoir, select the sensitive elastic parameter values ​​of the sample point in different orientations and their symmetrical orientations, then construct a sensitive elastic parameter plane, use the least squares method to perform ellipse fitting calculation, fit an optimal ellipse, and calculate the ellipse minor axis r1.

[0034] Step 8: Use the calculated ellipse minor axis r1 value to carry out reservoir prediction.

[0035] The present invention is further described in detail below through specific examples. The following examples can enable those skilled in the art to understand the present invention more comprehensively, but do not limit the present invention in any way.

[0036] This example is an experimental verification of the reservoir prediction of the Paleogene Dongying Formation in the L oil field in the Bohai Sea area. The reservoir is buried at a depth of -1500 to -3000 m and is mainly composed of lacustrine and deltaic deposits.

[0037] The specific method for reservoir prediction of this oil field is as follows: Figure 1 The steps of the technical flow chart are as follows:

[0038] Step 1: In 2019, three-dimensional seismic data of seafloor nodes were collected. The newly collected seismic data has the characteristics of high density, high coverage and wide azimuth. The acquisition bin is 12.5mx 12.5m, the number of coverage is 480 times, and the aspect ratio reaches 0.53 in the target layer.

[0039] Step 2: Perform data analysis on the wide-azimuth seismic data collected at sea to obtain the azimuth, offset and coverage distribution of the three-dimensional wide-azimuth seismic data.

[0040] Figure 2 The distribution diagram of azimuth, offset and coverage times of the acquired three-dimensional wide-azimuth seismic data.

[0041] Step 3: According to the azimuth, offset and coverage times of the 3D wide azimuth seismic data, the 3D wide azimuth seismic data are sorted into 4 sectors, among which the average azimuth of sector 1 is 131°, and the azimuth range is 108.5° to 153.5°; the average azimuth of sector 2 is 176°, and the azimuth range is 153.5° to 198.5°; the average azimuth of sector 3 is 221°, and the azimuth range is 198.5° to 243.5°; the average azimuth of sector 4 is 266°, and the azimuth range is 243.5° to 288.5°. Figure 3a At the same time, considering that the uneven distribution of offset distance and coverage times of the gather data in each azimuth sector will cause anisotropy illusion, only seismic data within the incidence angle of 0-30° are selected for anisotropy analysis. Figure 3b These are the CRP gathers in the four selected directions.

[0042] Step 4: Use the residual time difference correction technology to flatten the gather events of the three-dimensional wide-azimuth seismic data of N sectors in the third step, and use the deconvolution technology to improve the resolution of the seismic gathers based on the flattened gathers. Figure 4 The left part is the data before gather leveling. Figure 4 The middle part is the data after the data set is flattened. Figure 4 The right part shows the data with improved resolution after flattening, and the central frequency of the seismic data is increased by 4Hz.

[0043] Step 5: Carry out reservoir sensitive elastic parameter intersection analysis to clarify the reservoir sensitive elastic parameter I. The main target layer of this oil field is the East 3rd reservoir. Density is a sensitive elastic parameter that distinguishes reservoirs from non-reservoirs. Figure 5 shown.

[0044] Step 6: For the data in each sector that has been optimized in the fourth step, perform pre-stack simultaneous inversion to invert the reservoir sensitive elastic parameter I, which is the density parameter for this oil field. Figures 6a to 6d The density profiles were inverted at azimuth and omnidirectional angles of -49 / 131, -4 / 176, 41 / 221 through wells LB1 and LB4, respectively.

[0045] Step 7: For any sample point in the reservoir, select the sensitive elastic parameter values ​​of the sample point in different orientations and their symmetrical orientations, and project the above parameter values ​​onto the polar coordinate system. To be consistent with the actual situation, the established polar coordinate system stipulates that the north direction is the polar axis and the counterclockwise direction is negative. The least squares method is used to perform ellipse fitting calculations, and an optimal ellipse is fitted to calculate the minor axis r1 of the ellipse. Figure 7 The density values ​​corresponding to the azimuth angles of -49 / 131 degrees, -4 / 176 degrees, and 41 / 221 degrees are 2.3 g / cm 3 , 2.43g / cm 3and 2.57 g / cm 3 The ellipse fitting result, the ellipse minor axis fitting result is 2.2974g / cm 3 . Figure 8 This is the short axis fitting density inversion profile through wells LB1 and LB4.

[0046] Step 8: Use the calculated ellipse minor axis r1 value to carry out reservoir prediction. By comparing the short axis fitting density profiles of wells LB1 and LB4 and the density inversion profiles in all directions, in the inversion results of all directions, the 221-degree density inversion result matches the sandstone of well LB1 better, and the 131-degree density inversion result matches the sandstone of well LB4 better; the ellipse minor axis fitting result reflects the lower value of the inversion results in all directions, which is the best match with the drilling sandstone.

[0047] The sand body thickness encountered by logging drilling in the oil group of the East Section Ⅰ of the L oil field was statistically compared with the sand body thickness predicted in different azimuths, with a total of 15 wells (see Table 1). From the comparison in the table, it can be seen that the prediction error of the ellipse minor axis fitting density inversion is the smallest, which is 8.50%. In other words, the sand body thickness described by the minor axis fitting prediction result is the best match with the drilling sandstone thickness, with a match rate of 91.50%. This reflects the advantages of the technical process proposed in this paper in the prediction of wide-azimuth reservoirs and achieves the expected goal.

[0048] Table 1 Statistics of reservoir prediction errors at different locations in the Dongsan 1 oil formation of L oilfield

[0049]

[0050] The plane map is extracted from the short-axis density inversion result obtained by ellipse fitting, and the reservoir plane distribution characteristics are analyzed. Fig. 9 This is the inversion plane diagram of the short-axis density of the ellipse fitting of the oil group I in the East 3rd Section of the L Oilfield. The diagram well reflects the development of the sand body in this oil group and is consistent with the results of the drilling, providing guidance for subsequent drilling.

[0051] The present invention is not limited to the embodiments described above. The above description of the specific embodiments is intended to describe and illustrate the technical solution of the present invention. The above specific embodiments are merely illustrative and not restrictive. Without departing from the scope of the present invention and the scope of protection of the claims, a person of ordinary skill in the art can also make many forms of specific changes under the guidance of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. A reservoir prediction method for wide-azimuth seismic data based on ellipse minor axis fitting, characterized in that: The following steps are involved: (1) Collect three-dimensional wide-azimuth seismic data with an aspect ratio greater than 0.5 at sea; (2) Analyze the 3D wide-azimuth seismic data collected offshore to obtain the azimuth, offset, and coverage distribution of the 3D wide-azimuth seismic data; (3) According to the distribution of azimuth, offset and coverage times of the 3D wide azimuth seismic data, the azimuth m° is taken as the starting azimuth and the azimuth variable dm° is taken as the azimuth range contained in a sector, and the 3D wide azimuth seismic data is sorted into N sectors, where N is the number of sectors. (4) using the residual time difference correction technology to flatten the event axes of the three-dimensional wide-azimuth seismic data of the N sectors in step (3), and on the basis of the flattened seismic data, perform a process to improve the resolution of the seismic data, thereby increasing the center frequency of the seismic data to within 10 Hz; (5) Carry out reservoir sensitive elastic parameter intersection analysis to clarify reservoir sensitive elastic parameter I; (6) For each sector, the data after optimization processing in step (4) are subjected to prestack simultaneous inversion to invert the reservoir sensitive elastic parameter I; (7) For any sample point in the reservoir, select the sensitive elastic parameter values ​​of the sample point in different orientations and its symmetrical orientations, then construct a sensitive elastic parameter plane, use the least squares method to perform ellipse fitting calculation, fit an optimal ellipse, and calculate the ellipse minor axis r1; (8) Use the calculated ellipse minor axis r1 value to carry out reservoir prediction.

2. The reservoir prediction method of wide azimuth seismic data based on ellipse minor axis fitting according to claim 1, characterized in that: In step (3), the three-dimensional wide azimuth seismic data are sorted into N sectors, and the azimuth angle range of sector n (n=1,2…N) is: m°+(n-3 / 2)*dm° to m°+(n-1 / 2)*dm°; at the same time, only the three-dimensional wide azimuth seismic data within the incident angle of 0-30° are selected for anisotropy analysis.

Images