A method for predicting sandstone-type uranium deposits based on pseudo-three-dimensional seismic data

Abstract

The application provides a sandstone type uranium ore prediction method based on pseudo-three-dimensional seismic data. The method comprises the following steps: a. obtaining two-dimensional seismic SGY profile data of a working area; b. performing trace head analysis on the SGY profile data; c. obtaining point coordinate data; d. processing the point coordinate data; e. processing the SGY profile data; f. collecting gamma and acoustic logging curves of the working area; g. processing the logging curve data; h. performing multi-parameter lithology inversion according to the processed SGY data and the logging curve data; and i. predicting a favorable sandstone type uranium ore forming area. The application fully utilizes two-dimensional seismic data, acoustic logging data and gamma logging data sensitive to sandstone type uranium ore to accurately and effectively predict the favorable sandstone type uranium ore forming area, reduces the uranium resource key exploration range in the working area, provides geophysical prospecting basis for subsequent drilling construction, greatly reduces the exploration cost and improves the exploration efficiency.

Description

Technical Field

[0001] This invention relates to the field of uranium exploration, specifically a method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data. Background Technology

[0002] Currently, my country's seismic exploration of sandstone-type uranium deposits mainly relies on two-dimensional seismic exploration methods. As uranium exploration continues to deepen, the geological problems faced by geophysical workers are becoming increasingly complex. Researchers need to further rely on existing conditions to investigate the deep geological structure and environment of the work area, analyze and evaluate the spatial distribution characteristics of sand bodies of a certain thickness within the target layer, and predict the range of anomalous favorable sand bodies in the work area in order to guide the subsequent drilling and development work of the geological project team.

[0003] Because the favorable sandstone bodies in sandstone-type uranium deposits are generally shallowly buried and mostly exist in adsorbed form within thin interbedded sandstone and mudstone, it is difficult to effectively distinguish between sandstone and favorable ore-bearing sandstone using only impedance data obtained from acoustic impedance inversion. However, gamma-ray logging curves can effectively differentiate between the two. Therefore, this invention first performs pseudo-three-dimensional processing on two-dimensional SGY seismic data, then performs high- and low-frequency separation and weighted fusion of acoustic and gamma-ray logging curves, and finally conducts multi-parameter lithology inversion. The results can effectively improve the accuracy of inference and interpretation of favorable ore-bearing sandstones. Summary of the Invention

[0004] The purpose of this invention is to provide a sandstone-type uranium deposit prediction method based on pseudo-3D seismic data, which can effectively improve the accuracy of inference and interpretation of ore-bearing sandstones.

[0005] This invention is implemented as follows:

[0006] A method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data includes the following steps:

[0007] a. Obtain 2D seismic SGY profile data for the work area; SGY profile data includes trace head information and seismic trace information;

[0008] b. Analyze the track head information in the SGY profile data in step a, and determine the longitudinal survey line number, transverse survey line number, X coordinate, and Y coordinate;

[0009] c. Export the X and Y coordinates of the measurement points from the track information in step b;

[0010] d. Analyze and process the exported measurement point coordinate data; specifically: translate and expand the measurement point coordinate data to form two coordinate data parallel to the original measurement point, and then export these three sets of measurement point coordinate data.

[0011] e. Analyze and process the SGY profile data in step a; specifically: first, copy the SGY profile data to obtain three sets of SGY profile data, then edit the longitudinal survey line number and transverse survey line number in the track head information of the three sets of SGY profile data respectively, then import the coordinate data exported in step d into the track head information of the three sets of SGY profile data, and finally merge these three sets of SGY profile data to form a pseudo-three-dimensional SGY data volume;

[0012] f. Acquire gamma logging curve data and sonic logging curve data for the working area;

[0013] g. Analyze and process the logging curve data; specifically: filter, standardize, separate high and low frequency information, and weightedly fuse high and low frequency information into logging curve data.

[0014] h. Perform multi-parameter lithology inversion based on the pseudo-3D SGY data volume obtained in step e and the well logging curve data obtained in step g;

[0015] i. Predict favorable mineralization areas for sandstone-type uranium deposits based on inversion results.

[0016] Preferably, step d specifically involves: first, importing the coordinate data obtained in step c into the map display software Section for projection, and checking the distance between adjacent coordinate points; based on actual requirements and referring to the distance between adjacent coordinate points, setting the parallel distance between the track head coordinate data of the second and third sets of SGY profile data to be expanded and the coordinate data obtained in step c; and obtaining the track head coordinate data of the second and third sets of SGY profile data by performing equal-interval translation or calculation on the coordinate data obtained in step c.

[0017] Preferably, step e specifically involves: first, importing the SGY profile data obtained in step a into the data processing software Vista, and copying it twice to obtain the second and third sets of SGY profile data; setting the longitudinal survey line number, transverse survey line number, X coordinate, and Y coordinate of the original first set of SGY profile data and the copied second and third sets of SGY profile data to 0; numbering the original first set of SGY profile data and the second and third sets of SGY profile data respectively, and writing their numbers sequentially into their respective longitudinal survey line numbers, with the transverse survey line numbers incrementing from 1; then assigning the three sets of measurement point coordinate data exported in step d to the corresponding X and Y coordinates in the respective trackhead information of the three sets of numbered SGY profile data; finally, merging these three sets of SGY profile data to obtain a pseudo-three-dimensional SGY data volume.

[0018] Preferably, in step g, the curve filtering formula used when filtering the logging curve is:

[0019]

[0020] In the formula, f i f represents the measured value of the well logging curve. i-v and f i+v For distance f i Let ν be the adjacent values ​​of ν, m be the number of data points participating in the smoothing, m be an odd number, and y be the adjacent values ​​of ν. i This is the result after filtering the corresponding data.

[0021] Preferably, in step g, the high- and low-frequency information separation and high- and low-frequency information weighted fusion process is as follows: high- and low-frequency information separation is performed on the acoustic logging curve data to obtain low-frequency acoustic logging information of 0-10Hz, and high- and low-frequency information separation is performed on the gamma logging curve data to obtain high-frequency gamma logging information of 10-60Hz.

[0022] Low-frequency acoustic logging information is denoted as DT. B-L High-frequency gamma logging information is denoted as GR YB-H The curve is then weighted and fused according to the following formula, and the fused curve is denoted as...

[0023]

[0024] Where C is GR YB-H Weighting adjustment coefficient.

[0025] Preferably, in step h, multi-parameter lithological inversion is performed to obtain the wave impedance value F of the pseudo-3D SGY data volume. The specific calculation formula is as follows:

[0026] F = L P (r)+λL q (sd)+α -1 L1ΔZ

[0027] In the formula, r is the reflection coefficient, ΔZ is the impedance trend difference, d is the seismic trace information, s is the seismic trace information calculated from the well logging curve data, λ is the residual weighting factor, α is the trend weighting factor, and p and q are L-mode factors.

[0028] Preferably, the prediction of favorable mineralization areas for sandstone-type uranium deposits in step i includes determining the range of wave impedance values ​​for sandstone-type uranium deposits and delineating favorable mineralization areas.

[0029] This invention fully utilizes three types of measurement data: 2D seismic data, sonic logging data, and gamma logging data. It establishes a systematic sandstone-type uranium deposit prediction method based on pseudo-3D seismic data. By combining multi-parameter lithology inversion and the sensitivity of gamma logging curves to ore-bearing sand bodies, it can accurately and effectively predict favorable uranium mineralization areas, narrow down the exploration target area for sandstone-type uranium deposits, provide geophysical evidence for subsequent drilling operations, significantly reduce exploration costs, and improve exploration efficiency. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method of the present invention.

[0031] Figure 2 This is a schematic diagram of a pseudo-three-dimensional data volume established in an embodiment of the present invention.

[0032] Figure 3 This is a schematic diagram illustrating the decomposition of acoustic logging curves in an embodiment of the present invention.

[0033] Figure 4 This is a comparison chart of the reconstructed logging curve and the original logging curve in an embodiment of the present invention.

[0034] Figure 5 This is a flowchart of the multi-parameter lithology inversion process in an embodiment of the present invention.

[0035] Figure 6 This is an analysis and prediction diagram of favorable uranium mineralization areas based on an embodiment of the present invention; wherein, (a) is the actual seismic profile, and (b) is the favorable mineralization sand body identified and predicted based on the range of wave impedance values ​​after multi-parameter lithology inversion. Detailed Implementation

[0036] This invention provides a method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data. The method includes the following steps (combined with...). Figure 1 ):

[0037] S1. Collect and acquire 2D seismic SGY profile data of the work area to obtain seismic data for key areas to be explored (e.g., Figure 6 (a) shows the two-dimensional seismic SGY profile data of the work area. The acquired data includes lead information and seismic trace information (the seismic trace information is the geophysical measurement data).

[0038] S2. Statistically analyze the track head information of the SGY profile data to determine the number of bytes corresponding to the longitudinal survey line number, transverse survey line number, X coordinate, and Y coordinate data, in order to modify and write them later.

[0039] S3. Export the X and Y coordinate data of the measuring points in the SGY profile head information. The coordinate data of the measuring points in the working area can be exported from the collected SGY profile data using Vista data processing software (head analysis module).

[0040] S4. Analyze and process the coordinate data of the measurement points exported in step S3.

[0041] This step specifically includes the following sub-steps:

[0042] (1) Analyze and confirm the coordinate system of the measurement point coordinate data, such as WGS84 or CGCS2000.

[0043] (2) Import the coordinate data of the measurement points into Section or other map software for projection.

[0044] (3) Use measuring tools to analyze and confirm the spacing between each measuring point (the spacing between each measuring point is roughly the same, and can be considered as an equal spacing arrangement). Specifically: use a tool to measure the distance and angle between straight lines to calculate the distance (longitudinal) between the coordinates of adjacent measuring points. These measuring points are called the original measuring points.

[0045] (4) Establish two sets of related measurement coordinate data that are parallel to the original measurement points at equal intervals. This can be done through a coordinate calculation system or by copying and translating.

[0046] Specifically, using the line distance angle measurement tool, draw a perpendicular line segment with a distance of 'a' from the beginning and end of the line containing the original measuring point (the original measuring points are combined to form a straight line). Connect the endpoints of these two perpendicular line segments that are far from the original measuring point to form a first straight line parallel to the line containing the original measuring point and at a distance of 'a'. Then, draw a second straight line parallel to the line containing the original measuring point and at a distance of 2a. Both the first and second straight lines are located on the same side of the line containing the original measuring point. 'a' should be less than four times the distance between adjacent coordinates of the original measuring point (i.e., the distance between measuring points obtained in step (3)). Using the Section copy point tool, copy the original measuring point coordinate data points to the corresponding positions of the first and second straight lines. At this time, there will be three sets of parallel coordinate data points on the Section. This step is equivalent to translating and expanding the original measuring point coordinate data.

[0047] (5) Export the original coordinate data of the measuring points and the extended coordinate data as a txt file.

[0048] Use the Section coordinate transformation tool to export the three parallel coordinate data points as gpx files. These gpx files can be opened with Notepad. Save the opened gpx files as txt files for later use.

[0049] S5. Analyze and process the SGY profile data to form a pseudo-3D SGY data volume.

[0050] (1) Copy the original SGY profile data obtained in step S1 twice, and form three files together with the original SGY profile data, named SGY1, SGY2 and SGY3 respectively, and then import them into the data processing software Vista.

[0051] (2) Edit the track head information in SGY1, SGY2 and SGY3 files respectively. First, set the longitudinal survey line number values ​​of the track head to 1, 2 and 3 respectively, and then assign the transverse survey line numbers starting from 1.

[0052] Specifically, the longitudinal survey line number, transverse survey line number, X coordinate, and Y coordinate data in the SGY1, SGY2, and SGY3 files are set to 0 respectively; the SGY1, SGY2, and SGY3 files are numbered respectively, and their numbers 1, 2, and 3 are sequentially written into their respective longitudinal survey line numbers; the transverse survey line numbers are assigned incrementally starting from 1 according to the number of seismic gathers in their profiles, such as 1, 2, 3, 4, 5, etc.

[0053] The longitudinal survey line number at the track head determines the longitudinal profile. For example, longitudinal survey line number 1 corresponds to longitudinal profile 1, which is also SGY1; longitudinal survey line number 2 corresponds to longitudinal profile 2, which is also SGY2; and longitudinal survey line number 3 corresponds to longitudinal profile 3, which is also SGY3. ​​Each longitudinal profile consists of 14,168 track gathers, which are the transverse survey line numbers at the track head.

[0054] (3) Import the measurement data in txt and reassign the X and Y coordinates in the trace headers of SGY1, SGY2 and SGY3 files respectively.

[0055] Specifically, the three sets of measurement point coordinate data exported in step S4 are assigned to the corresponding X and Y coordinate traces in the three SGY1 files using the Vista trace head editing module.

[0056] (4) Finally, the Vista data processing module is used to merge the processed SGY1, SGY2, and SGY3 files to obtain a pseudo-3D SGY data volume, such as... Figure 2 As shown. Data processing software such as Vista can be used to view the corresponding track head information: line spacing is 5m, track spacing is 5m, line number increment is 1, track number increment is 1, number of lines is 3, and the number of seismic tracks per longitudinal profile is 14168.

[0057] S6. Collect and acquire gamma and sonic logging data near the two-dimensional seismic profile in the working area. The distance between the borehole and the logging line should be less than or equal to 500m. Figure 2 Three well logs are shown, labeled ZKG9_1, ZKG3_3 and ZKQ19_3 respectively.

[0058] S7. Analyze and process the logging curve data.

[0059] (1) Analyze the range of values ​​and depth distribution of the acoustic and gamma logging curves of each well, as well as the corresponding target layer depth.

[0060] (2) Use the following formula to filter the acoustic and gamma logging curves respectively using the median filtering method to remove the singular values ​​in the logging curves caused by well wall collapse.

[0061]

[0062] In the formula f i f represents the measured value of the well logging curve. i-v and f i+v For distance f i Let y be the adjacent values ​​of ν, m be the number of data points participating in the smoothing (which is an odd number), and y be the adjacent values ​​of ν. i This is the result after filtering the corresponding data.

[0063] (3) Select the well with the smallest change in well diameter data of the target layer as the standard well, and use the measurement value range of the standard well as the constraint to standardize the measurement data of the other wells so that their value ranges are all distributed within the same range.

[0064] (4) Use wavelet transform or variational mode decomposition to separate high- and low-frequency components in acoustic and gamma logging data, such as... Figure 3 As shown, Figure 3 The diagram shows the separation process of high and low frequency components in acoustic logging data. Figure 3 (a) shows the raw acoustic signal from the ZKG3_3 well logging. Figure 3 (b) to make Figure 3 (a) The low-frequency effective signal of 0-10Hz after decomposition of the original acoustic signal. Figure 3 (c) to make Figure 3 (a) The effective low-to-mid frequency signal of 10–60 Hz after decomposition of the original acoustic signal. Figure 3 (d) is to Figure 3 (a) The high-frequency signal greater than 60Hz after decomposition of the original acoustic signal. Figure 3 (e) is to Figure 3(a) The noise signal after separation of the original acoustic signal. The frequency ranges of the separated low-frequency, mid-low-frequency, and high-frequency signals can all be set manually. In this embodiment of the invention, for acoustic logging signals, a low-frequency effective signal of 0-10Hz is obtained by separating the high and low frequency components, which can control the shape of the line curve. Similarly, it can be done according to... Figure 3 The high- and low-frequency component separation process shown applies to gamma-ray logging data. In this embodiment, the gamma-ray logging data is separated into mid-to-low frequency effective signals of 10–60 Hz through high- and low-frequency component separation (the 10–60 Hz mid-to-low frequency effective signals of the gamma-ray logging data are considered high-frequency signals compared to the 0–10 Hz low-frequency effective signals of the sonic logging data; therefore, they will be referred to as high-frequency gamma-ray logging information during subsequent fusion). By decomposing the logging curve information, relatively low-frequency sonic logging information from the sonic logging data and relatively high-frequency gamma-ray logging information from the gamma-ray logging data are extracted. Then, the data within these useful frequency ranges are merged to reconstruct a new logging curve containing useful information from multiple different curves.

[0065] It should be noted that the frequency ranges for extracting low-frequency acoustic logging information from acoustic logging data and high-frequency gamma logging information from gamma logging data can be set manually, but the frequency of low-frequency acoustic logging information is definitely lower than the frequency of high-frequency gamma logging information.

[0066] The separated low-frequency acoustic logging information is denoted as DT. B-L High-frequency gamma logging information is denoted as GR YB-H The curve is then weighted and fused according to the following formula, and the fused curve is denoted as...

[0067]

[0068] Where C is GR YB-H Weighting adjustment coefficient.

[0069] Since low-frequency information determines the shape of the curve, the overall shape of the fused (or reconstructed) curve is roughly consistent with the acoustic logging curve, so the fused curve is also called the pseudo-acoustic logging curve.

[0070] curve The selection principle is that the reconstructed pseudo-acoustic logging curve should not only retain the original characteristics of the original acoustic logging curve well, but also be sufficient to reflect the characteristics of the newly added GR anomaly. For example... Figure 4 As shown, Figure 4The figure shows the original acoustic and gamma logging signals corresponding to ZKG9_1 and ZKG3_3 logging, as well as the signal after fusing the low-frequency information of the acoustic logging signal and the high-frequency information of the gamma logging signal. It can be seen from the figure that the reconstructed pseudo-acoustic logging curve is generally consistent with the original acoustic time difference signal in terms of trend, and can effectively incorporate relevant information of the gamma logging signal in some details.

[0071] S8. Process the pseudo-3D data volume and the reconstructed pseudo-acoustic logging curves.

[0072] (1) The inversion method refers to the post-stack impedance inversion technique.

[0073] (2) See the inversion process. Figure 5 The process involves wavelet extraction, synthetic record production, geological model establishment, principal component analysis, model estimation, model parameter modification, and three-dimensional inversion of well logging and seismic data, ultimately generating a multi-parameter lithology inversion data volume.

[0074] The objective function of the multi-parameter lithology inversion method is:

[0075] F = L P (r)+λL q (sd)+α -1 L1ΔZ

[0076] In the formula, r is the reflection coefficient, ΔZ is the impedance trend difference, d is the seismic trace, s is the synthetic seismic trace (which is calculated based on the pseudo-acoustic logging curve), λ is the residual weighting factor, α is the trend weighting factor, and p and q are L-mode factors.

[0077] S9. Analyze the uranium mineralization conditions in the work area in conjunction with regional geological data, mainly including the lithology and structure related to sandstone-type uranium deposits.

[0078] S10. Predict favorable uranium mineralization areas within the work area. Based on multi-parameter lithological inversion results and regional uranium mineralization conditions, delineate favorable uranium mineralization areas by setting favorable wave impedance ranges, such as... Figure 6 As shown in (b).

[0079] This invention establishes a systematic method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data. This method comprehensively predicts favorable mineralization areas of sandstone-type uranium deposits by combining gamma-ray logging curves with 2D seismic data and sonic logging data, thereby narrowing the exploration target area of ​​sandstone-type uranium deposits, reducing exploration costs, and improving exploration efficiency.

[0080] The present invention has been described in detail above with reference to the accompanying drawings and embodiments. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention. All contents not described in detail in the present invention can be derived from existing technologies.

Claims

1. A method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data, characterized in that, Includes the following steps: a. Obtain 2D seismic SGY profile data for the work area; SGY profile data includes trace head information and seismic trace information; b. Analyze the track head information in the SGY profile data in step a, and determine the longitudinal survey line number, transverse survey line number, X coordinate, and Y coordinate; c. Export the X and Y coordinates of the measurement points from the track information in step b; d. Analyze and process the exported measurement point coordinate data; specifically: translate and expand the measurement point coordinate data to form two coordinate data parallel to the original measurement point, and then export these three sets of measurement point coordinate data. e. Analyze and process the SGY profile data in step a; specifically: first, copy the SGY profile data to obtain three sets of SGY profile data, then edit the longitudinal survey line number and transverse survey line number in the track head information of the three sets of SGY profile data respectively, then import the coordinate data exported in step d into the track head information of the three sets of SGY profile data, and finally merge these three sets of SGY profile data to form a pseudo-three-dimensional SGY data volume; f. Acquire gamma logging curve data and sonic logging curve data for the working area; g. Analyze and process the logging curve data; specifically: filter, standardize, separate high and low frequency information, and weightedly fuse high and low frequency information into logging curve data. h. Perform multi-parameter lithology inversion based on the pseudo-3D SGY data volume obtained in step e and the well logging curve data obtained in step g; i. Predict favorable mineralization areas for sandstone-type uranium deposits based on inversion results.

2. The method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data according to claim 1, characterized in that, Step d specifically involves: first, importing the coordinate data obtained in step c into the map display software Section for projection, and checking the distance between adjacent coordinate points; based on actual requirements and referring to the distance between adjacent coordinate points, setting the parallel distance between the track head coordinate data of the second and third sets of SGY profile data to be expanded and the coordinate data obtained in step c; and obtaining the track head coordinate data of the second and third sets of SGY profile data by performing equal-interval translation or calculation on the coordinate data obtained in step c.

3. The method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data according to claim 1, characterized in that, Step e specifically involves: first, importing the SGY profile data obtained in step a into the data processing software Vista, and copying it twice to obtain the second and third sets of SGY profile data; setting the longitudinal survey line number, transverse survey line number, X coordinate, and Y coordinate of the original first set of SGY profile data and the copied second and third sets of SGY profile data to 0; numbering the original first set of SGY profile data and the second and third sets of SGY profile data respectively, and writing their numbers sequentially into their respective longitudinal survey line numbers, while accumulating the transverse survey line numbers starting from 1; then assigning the three sets of measurement point coordinate data exported in step d to the corresponding X and Y coordinates in the respective trackhead information of the three sets of numbered SGY profile data; finally, merging these three sets of SGY profile data to obtain a pseudo-three-dimensional SGY data volume.

4. The method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data according to claim 1, characterized in that, In step g, the curve filtering formula used when filtering the logging curve is: In the formula, f i f represents the measured value of the well logging curve. i-v and f i+v For distance f i Let ν be the adjacent values ​​of ν, m be the number of data points participating in the smoothing, m be an odd number, and y be the adjacent values ​​of ν. i This is the result after filtering the corresponding data.

5. The method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data according to claim 1, characterized in that, In step g, the high- and low-frequency information separation and high- and low-frequency information weighted fusion process is as follows: high- and low-frequency information separation is performed on the acoustic logging curve data to obtain low-frequency acoustic logging information of 0-10Hz, and high- and low-frequency information separation is performed on the gamma logging curve data to obtain high-frequency gamma logging information of 10-60Hz. Low-frequency acoustic logging information is denoted as DT. B-L High-frequency gamma logging information is denoted as GR YB-H The curve is then weighted and fused according to the following formula, and the fused curve is denoted as... Where C is GR YB-H Weighting adjustment coefficient.

6. The method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data according to claim 1, characterized in that, In step h, multi-parameter lithology inversion is performed to obtain the wave impedance value F of the pseudo-3D SGY data volume. The specific calculation formula is as follows: F=L P (r)+λL q (sd)+a -1 L1ΔZ In the formula, r is the reflection coefficient, ΔZ is the impedance trend difference, d is the seismic trace information, s is the seismic trace information calculated from the well logging curve data, λ is the residual weighting factor, α is the trend weighting factor, and p and q are L-mode factors.

7. The method for predicting sandstone-type uranium deposits based on pseudo-3D seismic data according to claim 1, characterized in that, The prediction of favorable mineralization areas for sandstone-type uranium deposits in step i includes determining the range of wave impedance values ​​for sandstone-type uranium deposits and delineating favorable mineralization areas.

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