A calibration method for the quantitative color scale of 3D seismic layer-by-layer attribute slices
By establishing the well-seismic depth relationship and calculating the compliance rate to adjust the three-dimensional seismic attribute slice color scale, the problem of strong subjectivity of color scales in the existing technology is solved, and more accurate reservoir prediction is achieved.
Patent Information
- Application Number
- CN202111265039.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-10-28
- Publication Date
- 2025-07-22
- Estimated Expiration
- 2041-10-28
AI Technical Summary
The existing three-dimensional seismic attribute slice color scale has strong subjective factors in reservoir prediction, which is difficult to accurately reflect the reservoir characteristics, resulting in insufficient prediction accuracy.
By establishing the depth relationship between the well earthquake, setting the boundary values between seismic attributes and reservoir parameters, calculating the compliance rate, selecting the boundary value of the optimal compliance rate to adjust the three-dimensional seismic attribute slice color scale to achieve quantitative correction.
The accuracy and accuracy of three-dimensional seismic attribute slices in reservoir prediction are improved, so that the color scale can better reflect the reservoir situation in the target area.
Smart Images
Figure CN116047592B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a method for correcting a quantitative color scale of a three-dimensional seismic layer-by-layer attribute slice, and belongs to the technical field of oil and gas field development. Background Technique
[0002] Seismic attribute slices are used to interpret geological information such as horizons and faults. During the oil and gas development process, it is usually necessary to rely on seismic attribute slice information to clarify the spatial distribution characteristics of reservoirs and the distribution of favorable target areas. Currently, when using seismic attributes for reservoir prediction, the common practice is to perform synthetic record calibration based on well drilling and three-dimensional seismic data to clarify the seismic time range (time window) corresponding to the reservoir, form a planar distribution map of seismic attributes, and then conduct linear fitting based on the completed well drilling and seismic attributes to clarify the optimal attribute type as the effective geological attribute for three-dimensional seismic attribute prediction. According to the coincidence degree between the reservoir characteristics of the completed well drilling and the seismic attributes, the attribute slice color scale is manually selected.
[0003] In the above seismic data analysis process, the seismic attributes are qualitatively evaluated mainly by manually determining the form of the attribute slice color scale. This evaluation process has strong subjective factors, making the seismic attribute slices unable to accurately reflect the reservoir characteristics within this range, unable to achieve a quantitative evaluation effect on the attributes, and it is difficult to maximize the reservoir prediction accuracy. Summary of the Invention
[0004] The purpose of the present invention is to provide a method for correcting a quantitative color scale of a three-dimensional seismic layer-by-layer attribute slice to solve the problem that the existing three-dimensional seismic attribute slice color scale cannot accurately reflect and predict the reservoir situation.
[0005] The present invention provides a method for correcting a quantitative color scale of a three-dimensional seismic layer-by-layer attribute slice, and the method includes the following steps:
[0006] 1) Obtain the three-dimensional seismic layer-by-layer attribute slice data of the target area and the well logging data within the target area;
[0007] 2) Establish a well-seismic time-depth relationship for the obtained three-dimensional seismic layer-by-layer attribute slice data and well logging data, determine the relative position relationship between the top and bottom of the well logging layers and the seismic slices, and obtain the top and bottom depth data of the well logging layers;
[0008] 3) Determine the reservoir parameters within this depth range according to the obtained top and bottom depth data of the well logging layers;
[0009] 4) Set several seismic attribute boundary values to divide the seismic slice attributes, and set several reservoir parameter boundary values to divide the reservoir parameters; by cross-calculating the coincidence rate between the seismic attributes and reservoir parameters corresponding to each reservoir parameter boundary value under each seismic attribute boundary value division, select the seismic attribute boundary value and reservoir parameter boundary value corresponding to the optimal coincidence rate;
[0010] 5) Adjust the color scale boundary values of the 3D seismic layer-based attribute slices according to the boundary values of the optimal coincidence rate to achieve the calibration of the color scale of the 3D seismic layer-based attribute slices.
[0011] The calibration method of the quantitative color scale of the 3D seismic layer-based attribute slices provided by the present invention sets corresponding boundary values of several 3D seismic slice attributes and boundary values of several logging layer reservoir parameters, calculates the coincidence rate of the seismic attributes corresponding to each reservoir parameter boundary value under the division of each seismic attribute boundary value through cross-computation, and adjusts the color scale values of the 3D seismic layer-based attribute slices according to the boundary values with the optimal coincidence rate, so that the calibrated color scale of the 3D seismic layer-based attribute slices can more accurately reflect the reservoir situation in the target area.
[0012] Further, in step 1), the seismic attributes include three types: amplitude type, frequency type, and phase type.
[0013] Further, in order to obtain an accurate well-seismic time-depth relationship, before establishing the well-seismic time-depth relationship in step 2), it is necessary to standardize the logging data.
[0014] Further, in step 3), the reservoir parameters are reservoir thickness and effective reservoir thickness.
[0015] Further, the reservoir thickness and effective reservoir thickness are determined according to the logging data.
[0016] Further, in order to improve the calculation efficiency and accuracy, in step 4), the seismic attribute boundary values are selected within a set interval in the corresponding seismic attribute value range, and the reservoir parameter boundary values are selected within a set interval in the corresponding reservoir parameter range.
[0017] Further, in order to improve the calculation efficiency and accuracy, the set interval for selecting the seismic attribute boundary values is from 1 / 3 to 2 / 3 of the corresponding seismic attribute value range, and the set interval for selecting the reservoir parameter boundary values is from 1 / 3 to 2 / 3 of the corresponding reservoir parameter range.
[0018] Further, in order to improve the cross-computation efficiency, in step 4), the cross-computation is implemented through programming. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 is the flowchart of the calibration of the color scale of the 3D seismic layer-based attribute slices of the present invention;
[0020] Figure 2 is the original seismic amplitude attribute slice diagram in the embodiment of the present invention;
[0021] Figure 3 is the distribution diagram of the highest coincidence rate combination calculated in the embodiment of the present invention;
[0022] Figure 4 It is the seismic amplitude attribute slice map after color bar calibration in the embodiment of the present invention. Specific Embodiments
[0023] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.
[0024] The present invention provides a method for calibrating the quantitative color bar of 3D seismic layer-by-layer attribute slices. The specific process is as Figure 1 shown. First, obtain the 3D seismic layer-by-layer attribute slice data of the target area and the well logging data of the target area; secondly, establish the well-seismic time-depth relationship between the two, determine the relative position relationship between the top and bottom of the well logging layers and the seismic slices, obtain the depth data of the top and bottom of the well logging layers, and determine the reservoir parameter range corresponding to the corresponding depth; then, set several seismic attribute boundary values to divide the seismic slice attributes, and set several reservoir parameter boundary values to divide the reservoir parameters; then, calculate the coincidence rate of the seismic attributes and reservoir parameters corresponding to each reservoir parameter boundary value under each seismic attribute boundary value division through cross calculation, and select the seismic attribute boundary value and reservoir parameter boundary value corresponding to the optimal coincidence rate; finally, adjust the color bar boundary value of the 3D seismic layer-by-layer attribute slice according to the boundary value of the optimal coincidence rate to realize the calibration of the color bar of the 3D seismic layer-by-layer attribute slice.
[0025] Step 1. Obtain the data of the target area
[0026] The data that the present invention needs to obtain includes the 3D seismic layer-by-layer attribute slice data of the target area and the well logging data of the target area.
[0027] Among them, the 3D seismic layer-by-layer attribute slice refers to the slice data of seismic attributes. Among them, the types of seismic attributes include amplitude types (such as instantaneous amplitude, average amplitude, maximum peak amplitude, etc.), frequency types (such as instantaneous frequency, response frequency, instantaneous frequency slope, etc.), and phase types (such as instantaneous phase, response phase, instantaneous phase cosine, etc.). In this embodiment, the selected seismic slice is the amplitude attribute slice of 10 - 20 ms above T9d, as Figure 2 shown.
[0028] Among them, the well logging data is used to obtain the reservoir parameters of the target area. The well logging data can determine the relationship between well logging information and geological information, and it contains various parameters, such as the physical properties of the well logging reservoir in the target area (porosity, permeability), electrical properties (acoustic travel time, resistivity, density, neutron), gas-bearing properties (gas saturation, total hydrocarbon), etc. In this embodiment, the well logging data of 96 completed wells in the target area is selected.
[0029] Step 2. Establish the well-seismic time-depth relationship
[0030] For the seismic attribute slice data and well logging data obtained in Step 1, it is necessary to determine the relative position relationship between the slices, the formation, and the top and bottom of the stratification to ensure that the well logging stratification is aligned with the depth of the seismic slices. Extract the position relationship between the top and bottom of the time window used to extract the seismic attribute slices and the corresponding formation, calculate the corresponding stratification position through the time-depth relationship, and combine the well logging stratification data to obtain the depth data of the top and bottom of the well logging stratification.
[0031] To ensure an accurate time-depth relationship, the well logging data needs to be standardized before establishing the time-depth relationship. Due to environmental impacts or different instruments used, there will be certain errors, and it is necessary to reduce or eliminate the errors by standardizing the well logging curves so that the processed data can correctly reflect the relationship between the well logging information and the geological information. Among them, frequency histograms or trend surface analysis can be used to standardize the well logging curves. After the well logging curves are standardized, synthetic seismic records are calibrated for the formation based on well logging curves such as acoustic travel time and density to determine the relative position relationship between the top and bottom of the well logging stratification and the seismic slices.
[0032] Step 3. Determine the range of reservoir parameters
[0033] Based on the relative position relationship between the top and bottom of the well logging stratification and the seismic slices determined in Step 2, the depth data of the top and bottom of the well logging stratification is obtained, the well logging data corresponding to the well logging depth is found, and the corresponding reservoir parameters are determined based on the found well logging data. Among them, the reservoir parameters are the reservoir thickness or the effective thickness of the reservoir. In this embodiment, the selected reservoir parameter is the reservoir thickness, and the obtained reservoir thickness range is 0 - 20.
[0034] Step 4. Calculate the coincidence rate of seismic attributes and reservoir parameters
[0035] To obtain accurate seismic slice color scale values, the coincidence rate of seismic attributes and reservoir parameters needs to be calculated. By setting boundary values for the seismic attributes and reservoir parameters respectively, calculate the coincidence rate of the seismic attributes corresponding to each reservoir parameter boundary value under the boundary value division of the seismic attributes, and obtain the optimal coincidence rate boundary value to adjust the seismic slice color scale.
[0036] Combined with the target geological understanding, several threshold values are set for the seismic attribute values and reservoir parameter values. The seismic slice attributes are divided by setting several seismic attribute threshold values, and the reservoir parameters are divided by setting several reservoir parameter threshold values. Among them, the seismic attribute threshold values are selected between 1 / 3 and 2 / 3 of the seismic attribute range, and the reservoir parameter threshold values are selected between 1 / 3 and 2 / 3 of the reservoir parameter range. The seismic attribute data set after setting several threshold values is divided into two categories: good and poor, where good and poor represent good and poor geology; similarly, the reservoir parameter data set after setting several threshold values is divided into two categories: good and poor, where good and poor represent good and poor geophysical prospecting; among them, good and poor are determined according to the threshold value. Generally, those less than the threshold value are defined as poor, and those greater than or equal to the threshold value are defined as good.
[0037] In this embodiment, the seismic attribute value range is from -2.8 to -39. The 1 / 3 to 2 / 3 (-15 to -27) of the seismic attribute value range is equally divided into 10 parts to obtain 11 threshold values, such as -15, -22, -27, etc.; the seismic attributes are divided into two categories: good and poor according to each threshold value, and a total of 10 groups of seismic attribute classification data can be obtained. Taking the threshold value -22 as an example, the seismic attributes greater than or equal to -22 are defined as good geology, and the seismic attributes less than -22 are defined as poor geology, and a group of seismic attribute classification data can be obtained. In this embodiment, the reservoir parameter value range is from 0 to 20. The 1 / 3 to 2 / 3 (7 to 14) of the reservoir parameter value range is equally divided into 10 parts to obtain 11 threshold values, such as 7, 10.5, 14, etc.; the reservoir parameters are divided into two categories: good and poor according to each threshold value, and a total of 10 groups of reservoir parameter classification data can be obtained. Taking the threshold value 10.5 as an example, the reservoir parameters greater than or equal to 10.5 are defined as good geophysical prospecting, and the reservoir parameters less than 10.5 are defined as poor geophysical prospecting, and a group of reservoir parameter classification data is obtained. Cross-calculations are performed on the obtained 10 groups of seismic classification data and reservoir parameter classification data. The coincidence rates between each group of seismic classification data and the 10 groups of reservoir parameter classification data are calculated respectively to obtain the seismic attribute threshold value and reservoir parameter threshold value with the highest coincidence rate; taking the geological good and poor data obtained with the seismic attribute threshold value -22 and the geophysical good and poor data obtained with the reservoir parameter threshold value 10.5 as an example, when the geology is good and the geophysical prospecting is good or the geology is poor and the geophysical prospecting is poor, the two groups of data are determined to be in coincidence. When the geology is poor and the geophysical prospecting is good or the geology is good and the geophysical prospecting is poor, the two groups of data are determined not to be in coincidence; taking this as an example, by programming to cross-calculate the coincidence rates of the 10 groups of seismic attribute classification data and the 10 groups of reservoir parameter classification data, 100 coincidence rate calculation results can be obtained. Among them, the optimal coincidence rate is 89.8%. Therefore, a combined distribution map under the optimal coincidence rate can be obtained (as shown in Figure 3 shown), and at the same time, the seismic attribute threshold value under the optimal coincidence rate can be obtained as -18 and the reservoir parameter threshold value as 7.
[0038] Step 4. Seismic slice color scale correction
[0039] According to the seismic attribute boundary values and reservoir parameter boundary values corresponding to the optimal coincidence rate obtained in step 3, adjust the corresponding seismic slice color scale values to achieve seismic layer-by-layer attribute slice correction. In this embodiment, according to the obtained seismic attribute boundary value with the highest coincidence rate of -18, adjust the seismic attribute slice color scale values to obtain the optimal seismic attribute slice map (as Figure 4 shown).
Claims
1. A calibration method for the quantitative color scale of 3D seismic horizon attribute slices, characterized in that The method includes the following steps: 1) Obtain the 3D seismic horizon attribute slice data of the target area and the well logging data within the target area; 2) Establish the well-seismic time-depth relationship for the obtained 3D seismic horizon attribute slice data and well logging data, determine the relative position relationship between the top and bottom of the well logging layers and the seismic slices, and obtain the depth data of the top and bottom of the well logging layers; 3) Determine the reservoir parameters within this depth range according to the obtained depth data of the top and bottom of the well logging layers; 4) Set several seismic attribute boundary values to divide the seismic slice attributes, and set several reservoir parameter boundary values to divide the reservoir parameters; By cross-calculating the coincidence rate of the seismic attribute classification data corresponding to each seismic attribute boundary value and the reservoir parameter classification data corresponding to each reservoir parameter boundary value, select the seismic attribute boundary value and reservoir parameter boundary value corresponding to the optimal coincidence rate; 5) Adjust the color scale boundary value of the 3D seismic horizon attribute slice according to the boundary value of the optimal coincidence rate to achieve the color scale correction of the 3D seismic horizon attribute slice.
2. The calibration method of the quantitative color scale of the three-dimensional seismic slice along the layer attributes according to claim 1, wherein, In step 1), the seismic attributes include three types: amplitude type, frequency type, and phase type.
3. The calibration method of the three-dimensional seismic layer-by-layer attribute slice quantification color scale according to claim 1, characterized in that, In step 2), before establishing the well-seismic time-depth relationship, the well logging data needs to be standardized.
4. The calibration method of the quantitative color scale of 3D seismic layer-by-layer attribute slices according to claim 1, characterized in that, In step 3), the reservoir parameters are the reservoir thickness and the effective reservoir thickness.
5. The calibration method of the three-dimensional seismic layer-by-layer attribute slice quantification color scale according to claim 4, characterized in that The reservoir thickness and the effective reservoir thickness are determined according to the well logging data.
6. The calibration method of the three-dimensional seismic layer-by-layer attribute slice quantitative color scale according to claim 1, characterized in that, In step 4), the seismic attribute boundary values are selected within a set interval in the corresponding seismic attribute value range, and the reservoir parameter boundary values are selected within a set interval in the corresponding reservoir parameter range.
7. The calibration method of the three-dimensional seismic layer-by-layer attribute slice quantification color scale according to claim 6, characterized in that, The set interval for selecting the seismic attribute boundary value is 1 / 3 to 2 / 3 of the corresponding seismic attribute value range, and the set interval for selecting the reservoir parameter boundary value is 1 / 3 to 2 / 3 of the corresponding reservoir parameter range.
8. The calibration method of the three-dimensional seismic layer-by-layer attribute slice quantification color scale according to claim 1, characterized in that, In step 4), the cross-calculation is implemented through programming.
Citation Information
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