Method and device for quantitatively evaluating imaging effect of seismic data

Abstract

The present application relates to a kind of quantitative evaluation of the method and device of seismic data imaging effect, belong to oil and gas field exploration and development technical field, the present application is obtained by selecting suitable seismic marker layer and with surface data correlation analysis, the average value of the absolute value of correlation coefficient is obtained to obtain first average value, simultaneously, adjacent seismic marker layer is carried out root mean square amplitude energy attribute extraction, correlation analysis is carried out between the root mean square amplitude obtained two by two, similarly, the average value of the absolute value of correlation coefficient is obtained to obtain second average value, finally, first average value and second average value are weighted summation to obtain comprehensive correlation coefficient, and the imaging effect of seismic data is evaluated using comprehensive correlation coefficient, the imaging effect of seismic data is evaluated by considering multiple factors, and the imaging effect of seismic data is classified to facilitate subsequent processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas field exploration and development, in particular to a method and device for quantitatively evaluating imaging effect of seismic data. BACKGROUND

[0002] With the gradual deepening of exploration and development, and the collection and application of a large number of three-dimensional seismic deployments, the requirement for seismic data quality is getting higher and higher, especially in the region dominated by lithology-structure oil and gas reservoirs, it is particularly important to further identify the problem of inaccurate structure imaging caused by insufficient long-wavelength static correction due to surface low-velocity zone, which is manifested as some false local structures on the time-domain seismic section and the problem of consistency of underground wave field energy amplitude caused by surface factors. Especially in the early stage of oil and gas exploration, the exploration degree is low, the well control is less, and the local structure development area cannot be corrected by actual drilling wells, but only can be evaluated and related work can be carried out from the time-domain seismic section, which causes certain disturbance to the implementation of exploration traps and well deployment. In order to be able to judge whether the imaging effect of seismic processing has a certain correlation with the surface in the early stage of seismic data processing, the present method can quickly give a quantitative evaluation standard for the relative relationship between the structure and amplitude energy of seismic data in combination with the interpretation of seismic marker horizon, and timely feedback to the processing personnel, and further eliminate the imaging problems caused by the surface through multiple iterations.

[0003] The existing technology has many means for data evaluation. Through relevant literature retrieval, for example, the paper "Seismic data quality comprehensive evaluation method" by Zhao Liang and Hu Zhifang in the first issue of "Petroleum Geology and Engineering" in 2003 only evaluates the signal-to-noise ratio and spectrum of seismic data, which is a whole evaluation of the recognition ability of seismic data, and does not discuss the factors affecting the surface. In addition, the "Method and device for evaluating seismic data processing results" (authorized publication number CN107678073B) Chinese invention patent mainly discloses the matching degree between the synthetic record and the seismic based on the synthetic record calibration, which mainly analyzes the resolution of well points, does not make a quantitative evaluation of the whole seismic data imaging, and does not make a specific evaluation of the surface influencing factors, and the evaluation process is relatively complicated, which is not conducive to large-scale development.

[0004] In summary, how to improve the rapid quantitative determination of the imaging effect of time-domain seismic data, effectively identify the false structure and the problem of inaccurate river channel depiction caused by surface energy attenuation, quantitatively evaluate the imaging effect of seismic data, and timely feedback the processing to realize the integration and collaborative work of seismic data processing and interpretation, and quickly solve the problem of inaccurate seismic imaging caused by surface factors. SUMMARY

[0005] The present application aims to provide a method and device for quantitatively evaluating the imaging effect of seismic data, so as to solve the problem of inaccurate seismic imaging caused by less consideration of influencing factors in the prior art.

[0006] To solve the above technical problems, the present application provides a method for quantitatively evaluating the imaging effect of seismic data, comprising the following steps:

[0007] 1) obtaining seismic marker layers at different depths and making horizon interpretation on the seismic marker layers;

[0008] 2) selecting a surface area and obtaining surface elevation data thereof, and processing the surface elevation data so that the interval of the surface elevation data is the same as the interval of the seismic horizon; performing correlation analysis on the seismic horizon data at different depths obtained in step 1) and the processed surface elevation data respectively to obtain different correlation coefficients, and calculating the average value of the absolute values of all the correlation coefficients to obtain a first average value;

[0009] 3) further dividing the interpreted seismic marker horizons obtained in step 1) into multiple sections, extracting the root mean square amplitude energy attribute between adjacent horizons, performing correlation analysis on the obtained root mean square amplitudes two by two to obtain a plurality of correlation coefficients, and calculating the average value of the absolute values of all the correlation coefficients to obtain a second average value;

[0010] 4) weighting and summing the first average value and the second average value according to the geological characteristics to obtain a comprehensive correlation coefficient, and using the comprehensive correlation coefficient to evaluate the imaging effect of the seismic data; wherein the smaller the result of the comprehensive correlation coefficient is, the better the imaging effect of the seismic data is.

[0011] The above technical solution has the following beneficial effects: by selecting a suitable surface area and performing correlation analysis on the seismic horizon data, the influencing factors of the surface area are taken into account in the evaluation of the imaging effect of the seismic data, effectively distinguishing the problem of inaccurate seismic imaging caused by false structures due to surface factors, extracting the root mean square amplitude energy attribute between adjacent horizons, evaluating whether the seismic data has the problem of poor amplitude preservation of the target section caused by lateral energy inconsistency of the surface and shallow layers, and further quickly and quantitatively determining the imaging effect of the time domain seismic data to make a quantitative evaluation and timely feedback to the seismic processing and optimize the corresponding processing flow parameters. This solves the problems faced in production and improves the efficiency of seismic interpretation. At the same time, in subsequent seismic data, a systematic evaluation is made on the areas with high correlation between data quality and surface.

[0012] Further, when the oil and gas field is mainly lithologic oil and gas reservoirs, the first average value is in the range of 0.7-0.9, the second average value is in the range of 0.1-0.3, and the sum of the first average value and the second average value is 1.

[0013] The beneficial effects of the above technical solution are: when targeting oilfields dominated by lithologic oil and gas reservoirs, different weights are selected for the average value, making the results more reliable.

[0014] Furthermore, in an oil and gas field dominated by structural oil and gas reservoirs, the first average value ranges from 0.1 to 0.3, the second average value ranges from 0.7 to 0.9, and the sum of the first average value and the second average value is 1.

[0015] The beneficial effects of the above technical solution are: when targeting oilfields with mainly structural oil and gas reservoirs, different weights are selected for the average value, making the results more reliable.

[0016] Further, the method for selecting seismic marker layers at different depths in step 1) is as follows: select multiple wells in the study area, standardize the logging curves of each well, and then perform synthetic record calibration. Select wells with obvious marker layers and a correlation coefficient between the synthetic record calibration and the seismic wave group greater than a set threshold. The marker layers with seismic wave groups that are evenly distributed vertically and stable in the region are the seismic marker layers.

[0017] The beneficial effects of the above technical solution are: selecting a suitable seismic marker layer to make the internal seismic wave group distribution uniform and stable, which facilitates subsequent processing.

[0018] Further, in step 2), the surface elevation data is processed, specifically by interpolating the surface elevation data.

[0019] The beneficial effects of the above technical solution are: interpolating the surface data to match the seismic horizon intervals facilitates subsequent correlation calculations.

[0020] Furthermore, the correlation analysis formula is as follows:

[0021] R xy =S xy / S x S y (1)

[0022]

[0023]

[0024]

[0025] In the formula, R xy S is the sample correlation coefficient. xy S is the sample covariance. x S represents the sample standard deviation of sample X. y This represents the sample standard deviation of sample Y, where n is the sample size.

[0026] The beneficial effects of the above technical solution are that the correlation analysis is performed on the data to obtain the correlation coefficient, and the subsequent processing is facilitated.

[0027] Further, the evaluation standard for evaluating the imaging effect of the seismic data by using the comprehensive correlation coefficient is that the imaging effect is good when the comprehensive correlation coefficient is between 0 and 0.001, the imaging effect is secondary when the comprehensive correlation coefficient is between 0.001 and 0.1, the imaging effect is poor when the comprehensive correlation coefficient is between 0.1 and 0.5, and the imaging effect is extremely poor when the comprehensive correlation coefficient is between 0.5 and 1.

[0028] The beneficial effects of the above technical solution are that different correlation evaluation standard ranges are selected to judge the imaging effect.

[0029] Further, in the step 2), the surface area is selected to be a region with a surface elevation maximum difference of more than 100 meters, and the region below the surface imaging is similar to the region, a small-scale anticline with a structural amplitude of 10-40 meters, an area of 0-1 square kilometers and a total area of more than 10% of the whole region is selected to be a trend surface smoothing, and the local structural characteristics are highlighted.

[0030] The beneficial effects of the above technical solution are that the region with large surface changes is selected, the local structural characteristics are highlighted, and the influence of the surface factor on the seismic imaging effect is highlighted.

[0031] Further, the surface elevation data is geodetic elevation data.

[0032] The beneficial effects of the above technical solution are that the geodetic elevation data is selected to facilitate subsequent processing and make the result more accurate.

[0033] To solve the above technical problem, the application further provides a device for quantitatively evaluating the imaging effect of seismic data, which comprises a memory and a processor, and a computer program stored in the memory and running on the processor, and the processor is used to execute the computer program instructions stored in the memory to realize the method for quantitatively evaluating the imaging effect of seismic data. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a flowchart of the method for quantitatively evaluating the imaging effect of seismic data of the application;

[0035] Figure 2 is a synthetic recording calibration chart in the embodiment of the application;

[0036] Figure 3 is a seismic marker horizon interpretation chart in the embodiment of the application;

[0037] Figure 4 is an interpolated surface elevation chart in the embodiment of the application;

[0038] Figure 5is the root mean square amplitude extraction of each layer section in the embodiment of the present application;

[0039] Figure 6 is the correlation coefficient statistical chart in the embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the present application more clear and obvious, the present application is further described in detail below in combination with the drawings and embodiments.

[0041] The method embodiment for quantitatively evaluating the imaging effect of seismic data is as follows:

[0042] The present application is aimed at the problem that the power module design fails to evaluate whether the power module structure design meets the requirement of stray inductance limit value, and proposes an effective design evaluation method. Figure 1 The specific implementation steps of the method are as follows:

[0043] 1) Obtain seismic data, wherein the seismic data is post-migration time domain seismic data, is pure wave data, has good amplitude preservation, clear wave group characteristics, and no excessive modification processing, is selected from a relatively uniform area in the study area, and has good well quality without obvious diameter expansion, and the drilling wells penetrate the target layer and reach the bedrock, and the wells are from shallow to deep and have relatively complete well logging curves and obvious marker layers, the well logging curves of the wells are standardized and then the synthetic record calibration is performed, the wells with a correlation coefficient between the synthetic record calibration and the seismic wave group being greater than 0.8 are selected, the correlation coefficient can be set as needed, the marker layers of the seismic wave group with clear geological meaning, relatively uniform longitudinal distribution and relative stability in the region are selected, and the seismic marker layer is interpreted;

[0044] 2) Select a region with large surface fluctuation and similar subsurface structure imaging, wherein the region with developed micro-amplitude structure is trend surface smoothed to highlight the local structure characteristics, and the surface elevation data is obtained, wherein the surface elevation data is geodetic elevation data, the surface elevation data is obtained through seismic acquisition or processing submitted SPS file, the SPS file contains shot point and receiver point elevation data, and the surface elevation data is interpolated, the grid interval of the interpolation is the same as the seismic horizon interval, and the interpolation method can be Kriging, inverse distance weighting or the like; the seismic horizon data at different depths obtained in step 1) is respectively correlated with the processed surface elevation data to obtain different correlation coefficients, and the correlation coefficient formula is as follows:

[0045] R xy =S xy / S x S y (1)

[0046]

[0047]

[0048]

[0049] wherein R xy is a sample correlation coefficient, S xy is a sample covariance, S x denotes a sample standard deviation of the sample X, S y denotes a sample standard deviation of the sample Y, and n is a sample size.

[0050] An average value of absolute values of all correlation coefficients is taken to obtain a first average value.

[0051] 3) The marker horizon obtained in step 1) is further divided into multiple layer segments, and a root-mean-square amplitude energy attribute is extracted between adjacent layer segments. When the root-mean-square amplitude energy attribute is extracted, the middle layer segment is extended by 30 milliseconds upward and downward, correlation analysis is performed between the obtained root-mean-square amplitudes to obtain a plurality of correlation coefficients, and an average value of absolute values of all correlation coefficients is taken to obtain a second average value.

[0052] 4) The first average value and the second average value are weighted and summed to calculate a comprehensive correlation coefficient according to the geological characteristics, and the imaging effect of the seismic data is evaluated by using the comprehensive correlation coefficient. The smaller the result of the comprehensive correlation coefficient is, the better the imaging effect of the seismic data is. In the calculation of the comprehensive correlation coefficient, the weight is specifically selected as follows. In a region mainly containing lithologic oil and gas reservoirs, the structure of the target layer segment is not developed, and sand bodies and channels are taken as the geological depiction targets. Therefore, the amplitude correlation value is high, being in a range from 0.7 to 0.9. At the same time, a certain structural change also needs to be considered, and the structural weight can be taken to be in a range from 0.1 to 0.3. Therefore, when the oil and gas field mainly contains lithologic oil and gas reservoirs, the first average value is taken to be in a range from 0.7 to 0.9, the second average value is taken to be in a range from 0.1 to 0.3, the sum of the first average value and the second average value is 1, the weight values are selected to be 0.3 and 0.7 respectively, in a region mainly containing structural oil and gas reservoirs, the structure weight is 0.7-0.9, and a certain amplitude change also needs to be considered, and the amplitude weight can be taken to be in a range from 0.1 to 0.3. Therefore, when the oil and gas field mainly contains structural oil and gas reservoirs, the first average value is taken to be in a range from 0.1 to 0.3, the second average value is taken to be in a range from 0.7 to 0.9, the sum of the first average value and the second average value is 1, the weight values are selected to be 0.7 and 0.3 respectively, and the calculation formula is as follows:

[0053] C 岩性 = 0.3 * C1 + 0.7 * C2 (5)

[0054] C 构造 = 0.7 * C1 + 0.3 * C2 (6)

[0055] wherein C 岩性 is a lithologic hydrocarbon reservoir comprehensive correlation coefficient, C 构造 is a structural hydrocarbon reservoir comprehensive correlation coefficient, C1 is an absolute value average of a structural-surface correlation coefficient, and C2 is an absolute value average of an interlayer seismic root-mean-square amplitude correlation coefficient.

[0056] The comprehensive correlation coefficient evaluation standard can be set by the user, and in this case, the imaging effect is good when the comprehensive correlation coefficient is between 0 and 0.001, the imaging effect is second when the comprehensive correlation coefficient is between 0.001 and 0.1, the imaging effect is poor when the comprehensive correlation coefficient is between 0.1 and 0.5, and the imaging effect is extremely poor when the comprehensive correlation coefficient is between 0.5 and 1. When the imaging effect is poor or extremely poor, the surface static correction amount needs to be re-confirmed, the processing flow and parameters need to be re-determined, and the seismic data processing work needs to be re-performed.

[0057] Next, a development well area in the northern margin of a certain basin is taken as an example. The lithologic gas reservoir is dominant in the area, and the surface types are diverse, including desert, grassland, mountain, gully, and the maximum elevation difference is 250 meters horizontally, but the seismic geological marker horizon is relatively stable, and the whole is a monocline structure. The seismic data in the area is collected in 2014, and the corresponding repeated processing has been completed, and the seismic data needs to be evaluated. The specific implementation steps are as follows:

[0058] (1) Obtain the seismic data and establish the related seismic interpretation work area, and provide the seismic data type as pre-stack time migration seismic data, and the seismic data is pure wave data, and the seismic wave group characteristics are clear, and no excessive modification processing is performed.

[0059] (2) 15 wells are selected for well logging curve standardization processing and synthetic record calibration, which are relatively uniform in the whole area, and the well conditions are good, and all drill through the upper Paleozoic target interval, and drill into the Proterozoic basement stratum, and the logging curves are complete from shallow to deep. Among them, the marker layers of group A, group B and group C are relatively obvious, and the correlation coefficients between the synthetic record calibration and the seismic wave group are all above 0.8, as shown in Figure 2 .

[0060] (3) Select the seismic marker layer and do horizon interpretation, and select three groups of A group (T6), B group (T8) and C group (T9) as major geological interfaces, which are distributed relatively uniformly in the vertical direction, and the seismic wave groups T6, T8 and T9 are relatively stable in the region, as shown in Figure 3 , and have good lateral continuity, and are easy to interpret horizons. The horizon interpretation density in the area is at least 16x16, and is interpolated to 1x1, and the grid interval is 25x25 meters.

[0061] (4) Collect the surface elevation and make a plane interpolation. The surface elevation data is geodetic elevation data. The acquisition approach is to collect the SPS file submitted by the seismic acquisition or processing, which contains the elevation data of the shot point and the geophone point. The original shot line interval is 400 meters, and the geophone line interval is 200 meters. The interpolation is needed to be made into the same interval as the interpreted horizon, which is 25x25 meters, as shown in FIG. 6. Figure 4

[0062] (5) Correlation analysis is made between the seismic marker horizon and the surface elevation. The surface data is compared with the interpreted horizon. A region with large surface fluctuation and similar imaging of the underlying structure is selected in the test area, as shown in FIG. 7. The red box shows that the local microstructure in the test area is less developed, and no trend surface is made. The correlation coefficient analysis is made between the interpolated surface elevation and the shallow (T6), medium (T8), and deep (T9) layers, respectively. The correlation coefficients are -0.41, -0.33, and -0.65, respectively, as shown in FIG. 8. Figure 4 Figure 6

[0063] (6) Large time window energy correlation analysis is made for the shallow, medium, and deep parts. According to the interpreted marker horizon, the region is further divided into ① 0-T6, ② T6-T8, and ③ T8-T9. The root mean square amplitude attribute extraction is made, as shown in FIG. 9. The middle horizon is extended by 30 milliseconds upward and downward. The correlation analysis is made between the seismic attributes, and the correlation coefficients are -0.25 between ① and ②, -0.75 between ① and ③, and 0.27 between ② and ③, as shown in FIG. 10. Figure 5 Figure 6

[0064] (7) The imaging effect of the seismic data is evaluated. The correlation coefficient analysis is made between the interpreted seismic horizon of the shallow, medium, and deep layers and the surface elevation. The average of the absolute values is 0.46, and the average of the absolute values between the root mean square amplitudes of the layers is 0.42. The region is a lithologic gas reservoir. The comprehensive correlation coefficient is calculated by the weighted coefficient to be 0.432. According to the evaluation standard of the correlation coefficient, the correlation coefficient is between 0 and 0.001, the imaging effect is good, between 0.001 and 0.1, the imaging effect is secondary, between 0.1 and 0.5, the imaging effect is poor, and between 0.5 and 1, the imaging is very poor. According to the calculation, the comprehensive evaluation of the imaging effect of the seismic data is poor. Further optimization and processing parameters are needed for reprocessing to eliminate the influence of the surface factor on the identification of the underground structure and the geological body, further improve the quality of the seismic data, and reduce the risk of well site demonstration.

[0065] The device embodiment for quantitatively evaluating the imaging effect of the seismic data is as follows.

[0066] ​​​​​The application further provides a device for quantitatively evaluating imaging effect of seismic data, which comprises a memory and a processor, and a computer program stored in the memory and running on the processor, and the processor is used for executing computer program instructions stored in the memory to realize the method for quantitatively evaluating imaging effect of seismic data. The specific process has been described in detail in the method embodiment for quantitatively evaluating imaging effect of seismic data, and will not be repeated here. The memory can be selected from a microprocessor MCU, a programmable logic device FPGA and the like, and the processor can be selected from a mobile hard disk, a read-only memory (ROM), a random access memory (RAM) and the like.

[0067] The above gives a specific embodiment, but the application is not limited to the described embodiment. The basic idea of the application is the above basic scheme, and according to the teaching of the application, various transformed models, formulas and parameters can be designed without creative labor for ordinary skilled in the art. Changes, modifications, replacements and variations of the embodiments without departing from the principles and spirits of the application still fall within the protection scope of the application.

Claims

1. A method for quantitatively evaluating the imaging effect of seismic data, characterized in that, The method comprises the following steps: 1) obtaining seismic marker layers at different depths and making horizon interpretation on the seismic marker layers; 2) selecting a surface area and obtaining surface elevation data thereof, and processing the surface elevation data to make the interval of the surface elevation data the same as the interval of the seismic horizon, and performing correlation analysis on the processed surface elevation data and the seismic horizon data obtained in step 1) at different depths to obtain different correlation coefficients, and calculating the average value of the absolute values of all the correlation coefficients to obtain a first average value; the surface area is selected to have a surface elevation difference of more than 100 meters, and the area under the surface is similar to the area under the surface of the selected area, the structural amplitude is between 10-40 meters, the area is between 0-1 square kilometers, and the total area accounts for more than 10% of the whole area; a small-scale anticline with a structural amplitude of 10-40 meters, an area of 0-1 square kilometers and a total area of more than 10% of the whole area is selected to perform trend surface smoothing to highlight the local structural characteristics; 3) further dividing the interpreted seismic marker horizon obtained in step 1) into multiple horizons, extracting the root mean square amplitude energy attribute between adjacent horizons, and performing correlation analysis on the obtained root mean square amplitudes to obtain a plurality of correlation coefficients, and calculating the average value of the absolute values of all the correlation coefficients to obtain a second average value; 4) weighting and summing the first average value and the second average value according to the geological characteristics to obtain a comprehensive correlation coefficient, and using the comprehensive correlation coefficient to evaluate the imaging effect of the seismic data; wherein the smaller the result of the comprehensive correlation coefficient is, the better the imaging effect of the seismic data is.

2. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that, When in a lithologic oil and gas field, the first average value is in the range of 0.7-0.9, the second average value is in the range of 0.1-0.3, and the sum of the first average value and the second average value is 1.

3. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that, When in a structural oil and gas field, the first average value is in the range of 0.1-0.3, the second average value is in the range of 0.7-0.9, and the sum of the first average value and the second average value is 1.

4. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that, The method for selecting the seismic marker layer at different depths in step 1) is as follows: a plurality of wells in the study area are selected, the well logging curves of the wells are standardized and then are calibrated to obtain synthetic records, the wells with obvious marker layers and the correlation coefficient between the synthetic record calibration and the seismic wave group being greater than a set threshold value are selected, and the marker layers of the seismic wave group that are vertically distributed uniformly and stable in the region are the seismic marker layers.

5. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that, In step 2), the surface elevation data is processed by interpolation.

6. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that, The correlation analysis formula is as follows: ’ ， wherein is the sample correlation coefficient, is the sample covariance, is the sample standard deviation of sample X, is the sample standard deviation of sample Y, n is the sample size.

7. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that, The evaluation standard for evaluating the imaging effect of the seismic data by using the comprehensive correlation coefficient is: when the comprehensive correlation coefficient is between 0 and 0.001, the imaging effect is good, when the comprehensive correlation coefficient is between 0.001 and 0.1, the imaging effect is better, when the comprehensive correlation coefficient is between 0.1 and 0.5, the imaging effect is poor, and when the comprehensive correlation coefficient is between 0.5 and 1, the imaging effect is extremely poor.

8. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that, The surface elevation data is geodetic elevation data.

9. An apparatus for quantitatively evaluating the imaging effect of seismic data, characterized in that, The device comprises a memory and a processor, and a computer program stored in the memory and running on the processor, and the processor is used to execute the computer program instructions stored in the memory to realize the method for quantitatively evaluating the imaging effect of the seismic data according to any one of claims 1-8.

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