Method and device for quantitatively evaluating seismic data imaging effect

Abstract

The invention relates to a method and device for quantitatively evaluating the imaging effect of seismic data, and belongs to the technical field of exploration and development of oil and gas fields. A proper seismic marker bed is selected, correlation analysis is conducted on the seismic marker bed and surface data, and the average value of absolute values of correlation coefficients is obtained to obtain a first average value; meanwhile, extracting root-mean-square amplitude energy attributes of adjacent seismic marker layers, performing correlation analysis on every two obtained root-mean-square amplitudes, similarly obtaining an average value of absolute values of correlation coefficients to obtain a second average value, and finally performing weighted summation on the first average value and the second average value to obtain a comprehensive correlation coefficient. According to the seismic data imaging effect evaluation method, the seismic data imaging effect is evaluated by comprehensively considering various factors, and the seismic data imaging effect is graded to facilitate subsequent processing.

Description

Technical Field

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

[0002] With the deepening of exploration and development, as well as the acquisition and application of a large number of three-dimensional seismic deployments, the requirements for the quality of seismic data are getting higher and higher, especially in areas dominated by lithologic-structural oil and gas reservoirs. It is necessary to further identify the problem of inaccurate structural imaging caused by insufficient long-wavelength static correction due to the low-velocity zone on the surface, which is manifested as some false local structures on the time-domain seismic profile 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 structural development area cannot be corrected by real drilling. Only from the time-domain seismic profile can evaluation and related work be carried out, which has caused certain troubles for the implementation of exploration closures 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, this method can quickly combine the interpretation of seismic marker layers, give quantitative evaluation standards for the relative relationship between seismic data structure and amplitude energy, and promptly feedback to the processing personnel, and further eliminate the imaging problems caused by the surface through multiple iterations.

[0003] There are many methods for data evaluation in the existing technology. Through the search of relevant literature, for example, the paper "Comprehensive Evaluation Method of Seismic Data Quality" by Zhao Liang and Hu Zhifang in the first issue of "Petroleum Geology and Engineering" in 2003 only evaluated the signal-to-noise ratio and spectrum of seismic data, which is an overall evaluation of the recognition ability of seismic data, and did not discuss the factors affecting the surface. In addition, the Chinese invention patent "A Method and Device for Evaluating Seismic Data Processing Results" (authorization announcement number CN107678073B) mainly discloses the evaluation of post-stack seismic data based on synthetic record calibration, by observing the matching degree between synthetic records and seismic. This method mainly analyzes the resolution of well points, does not make a quantitative evaluation of the overall seismic data imaging, and does not make a specific evaluation of the surface influencing factors. The evaluation process is relatively cumbersome and is not conducive to large-scale development.

[0004] In summary, how to improve the imaging effect of rapid and quantitative determination of time-domain seismic data, effectively identify false structures and inaccurate river channel characterization caused by surface energy attenuation, quantitatively evaluate the imaging effect of seismic data, and provide timely feedback and processing, realize the integrated and coordinated work of seismic data processing and interpretation, and quickly solve the problem of inaccurate seismic imaging caused by surface factors. Summary of the invention

[0005] The purpose of the present invention is 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 insufficient consideration of influencing factors when evaluating the imaging effect of seismic data in the prior art.

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

[0007] 1) Obtain seismic marker layers at different depths and interpret the seismic marker layers;

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

[0009] 3) further dividing the interpreted seismic marker layer obtained in step 1) into multiple layers, extracting root mean square amplitude energy attributes between adjacent layers, performing correlation analysis between the obtained root mean square amplitudes to obtain a number of correlation coefficients, and calculating the average of the absolute values ​​of all correlation coefficients to obtain a second average value;

[0010] 4) A comprehensive correlation coefficient is obtained by weighted summing the first average value and the second average value according to geological characteristics, and the comprehensive correlation coefficient is used to evaluate the imaging effect of the seismic data; wherein, the smaller the comprehensive correlation coefficient result, the better the imaging effect of the seismic data.

[0011] The beneficial effects of the above technical solution are: by selecting appropriate surface areas and conducting correlation analysis with seismic layer data, the influencing factors of the surface area are included in the evaluation of the imaging effect of seismic data, and the problem of inaccurate seismic data imaging caused by false structures caused by surface factors is effectively identified. The root mean square amplitude energy attribute is extracted between adjacent layers to evaluate whether the seismic data has poor amplitude preservation in the target layer due to lateral energy inconsistency such as the surface and shallow layers, and then the imaging effect of the time domain seismic data is quickly and quantitatively determined to make a quantitative evaluation, and timely feedback is given to seismic processing, and the corresponding processing flow parameters are optimized. Solve the problems faced in production and improve the efficiency of seismic interpretation. At the same time, in subsequent seismic data, a systematic evaluation is made for areas with high correlation between data quality and the surface.

[0012] Furthermore, in an oil and gas field dominated by lithologic oil and gas reservoirs, the first average value ranges from 0.7 to 0.9, the second average value ranges from 0.1 to 0.3, and the sum of the first average value and the second average value is 1.

[0013] The beneficial effect of the above technical solution is that when targeting an oil field with mainly lithologic oil and gas reservoirs, different weights are selected for the average value to make the result 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 effect of the above technical solution is that when targeting an oil field mainly composed of structural oil and gas reservoirs, different weights are selected for the average value to make the result more reliable.

[0016] Furthermore, in step 1), the method for selecting seismic marker layers at different depths is as follows: multiple wells in the study area are selected, the logging curves of each well are standardized and then synthetic record calibration is performed, and wells with obvious marker layers and a correlation coefficient between the synthetic record calibration and the seismic wave group greater than a set threshold are selected. The marker layer of the seismic wave group with uniform vertical distribution and stable in the region is the seismic marker layer.

[0017] The beneficial effect of the above technical solution is: selecting a suitable seismic marker layer so that the internal seismic wave group is evenly and stably distributed, which is convenient for subsequent processing.

[0018] Furthermore, in step 2), the surface elevation data is processed, specifically, interpolation processing is performed on the surface elevation data.

[0019] The beneficial effect of the above technical solution is that the interpolation processing of the surface data is the same as the seismic layer interval, which facilitates the subsequent calculation of the correlation.

[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 is the sample correlation coefficient, S xy is the sample covariance, S x represents the sample standard deviation of sample X, S y represents the sample standard deviation of sample Y, and n is the sample size.

[0026] The beneficial effect of the above technical solution is: performing correlation analysis on the data to obtain the correlation coefficient, which is convenient for subsequent processing.

[0027] Furthermore, the evaluation criteria for evaluating the imaging effect of seismic data using the comprehensive correlation coefficient are: if the comprehensive correlation coefficient is between 0-0.001, the imaging effect is good, if it is between 0.001-0.1, it is second best, if it is between 0.1-0.5, the imaging effect is poor, and if it is between 0.5-1, the imaging effect is extremely poor.

[0028] The beneficial effect of the above technical solution is: different relevant evaluation standard ranges are selected to judge the imaging effect.

[0029] Furthermore, in step 2), the surface area is selected from the area with the maximum surface elevation difference of more than 100 meters and the underlying structural image is similar to it, and the small anticlines with a structural amplitude between 10-40 meters, an area between 0-1 square kilometers and a total area of ​​more than 10% of the whole area are selected to smooth their trend surfaces and highlight the local structural features.

[0030] The beneficial effects of the above technical solution are: selecting areas with large surface changes, highlighting local structural features, and highlighting the impact of surface factors on seismic imaging effects.

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

[0032] The beneficial effect of the above technical solution is: selecting geodetic elevation data to facilitate subsequent processing and make the results more accurate.

[0033] In order to solve the above technical problems, the present invention also provides a device for quantitatively evaluating the imaging effect of seismic data, which includes a memory and a processor, and a computer program stored in the memory and running on the processor. The processor is used to execute the computer program instructions stored in the memory to implement a method for quantitatively evaluating the imaging effect of seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of a method for quantitatively evaluating the imaging effect of seismic data according to the present invention;

[0035] Figure 2 is a synthetic record calibration diagram in an embodiment of the present invention;

[0036] Figure 3 is an interpretation diagram of earthquake marker horizons in an embodiment of the present invention;

[0037] Figure 4 is a surface elevation map after interpolation in an embodiment of the present invention;

[0038] Figure 5It is the root mean square amplitude extraction of each layer segment in the embodiment of the present invention;

[0039] Figure 6 1 is a statistical diagram of correlation coefficients in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0041] Example of a method for quantitatively evaluating the imaging effect of seismic data:

[0042] The present invention aims at the problem that the structural design of the power module cannot be evaluated to see whether it meets the stray inductance limit requirement in the power module design, and proposes an effective design evaluation method. The flowchart of the method is as follows: Figure 1 As shown, the specific implementation steps of this method are as follows:

[0043] 1) Obtain seismic data, wherein the seismic data is time-domain seismic data after migration, and is seismic pure wave data, with good amplitude preservation, clear wave group characteristics, and no excessive modification processing. Select the study area that is relatively uniform in the whole area, with good well condition quality, and no obvious expansion effect. Drill through the target layer and drill multiple wells in the bedrock. Each well has a complete logging curve from shallow to deep, and the marker layer is relatively obvious. The logging curve of each well is standardized and then synthetic record calibration is performed. Select the wells with a correlation coefficient of more than 0.8 between the synthetic record calibration and the seismic wave group. The correlation coefficient can be set according to needs; select the marker layer of the seismic wave group with clear geological meaning, relatively uniform distribution in the vertical direction, and relatively stable in the region, and interpret the stratigraphic position of the seismic marker layer;

[0044] 2) Select areas with large surface fluctuations and similar underlying structural images. For areas with more microstructures, smooth the trend surface to highlight local structural features and obtain surface elevation data. The surface elevation data is geodetic elevation data, which is obtained by SPS files submitted by seismic acquisition or processing, which contain shot point and receiver point elevation data, and interpolate the surface elevation data. The interpolation grid interval is the same as the seismic layer interval. The interpolation method can be Kriging, inverse distance weighted, etc.; The seismic layer data of different depths obtained in step 1) are respectively correlated with the processed surface elevation data to obtain different correlation coefficients. The correlation coefficient calculation formula is as follows:

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

[0046]

[0047]

[0048]

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

[0050] The absolute values ​​of all correlation coefficients are averaged to obtain a first average.

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

[0052] 4) According to the geological characteristics, the first average value and the second average value are weighted and summed to calculate the comprehensive correlation coefficient, and the comprehensive correlation coefficient is used to evaluate the imaging effect of the seismic data; the smaller the comprehensive correlation coefficient result, the better the imaging effect of the seismic data; the specific weight selection in the calculation of the comprehensive correlation coefficient is that in the area dominated by lithologic oil and gas reservoirs, the target layer structure is not developed, and the sand body and river channel are the geological characterization targets, so the amplitude correlation value is as high as 0.7-0.9, and it is also necessary to take into account certain structural changes, and the structural weight can be between 0.1-0.3. Therefore, when the oil and gas fields are dominated by lithological oil and gas reservoirs, the first average value ranges from 0.7 to 0.9, the second average value ranges from 0.1 to 0.3, and the sum of the first and second average values ​​is 1. At this time, the weight values ​​are selected as 0.3 and 0.7 respectively. In the area dominated by structural oil and gas reservoirs, the structural weight is 0.7-0.9. At the same time, it is necessary to take into account a certain amplitude change. The amplitude weight can be taken between 0.1 and 0.3. Therefore, when the oil and gas fields are 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 and second average values ​​is 1. At this time, the weight values ​​are selected as 0.7 and 0.3 respectively. The calculation formula is as follows:

[0053] C 岩性 =0.3*C 1 +0.7*C 2 (5)

[0054] C 构造 =0.7*C1 +0.3*C 2 (6)

[0055] In the formula, C 岩性 is the comprehensive correlation coefficient of lithologic oil and gas reservoirs, C 构造 is the comprehensive correlation coefficient of structural oil and gas reservoirs, C 1 is the absolute value average of the correlation coefficient between structure and surface, C 2 The average absolute value of the correlation coefficient of the inter-layer seismic root mean square amplitude.

[0056] The evaluation criteria for the comprehensive correlation coefficient can be set by yourself. Here, it is set as follows: if the comprehensive correlation coefficient is between 0-0.001, the imaging effect is good, if it is between 0.001-0.1, it is second, if it is between 0.1-0.5, the imaging effect is poor, and if it is between 0.5-1, the imaging effect is extremely poor. When the imaging effect is poor or extremely poor, it is necessary to re-implement the surface static correction, re-formulate the processing flow and its parameters, and re-perform seismic data processing related work.

[0057] The following is an example of a development well area on the northern edge of a basin. The area is dominated by lithologic gas reservoirs, and its surface types are diverse, including deserts, grasslands, mountains, gullies, and the maximum lateral elevation drop is 250 meters. However, the seismic geological marker layer is relatively stable, and the overall structure is a monocline. The seismic data in this area was collected in 2014, and the corresponding repeated processing has been completed. The seismic data needs to be evaluated. The specific implementation steps are as follows:

[0058] (1) Obtain seismic data and establish relevant seismic interpretation work areas. The seismic data type provided is pre-stack time migration seismic data, which is pure seismic wave data with clear seismic wave group characteristics and no excessive modification processing.

[0059] (2) 15 wells were selected for logging curve standardization and synthetic log calibration. They were relatively uniform in the whole area and had good well conditions. All of them drilled through the target Paleozoic layer and encountered the basement Proterozoic strata. The well log curves were relatively complete from shallow to deep. Among them, the marker layers of Group A, Group B, and Group C were relatively obvious, and the correlation coefficients between synthetic log calibration and seismic wave groups were all above 0.8. Figure 2 shown.

[0060] (3) Select seismic marker layers and interpret the stratigraphic positions. Select shallow, medium and deep layers, namely Group A (T6), Group B (T8) and Group C (T9) as major geological interfaces. The vertical distribution is relatively uniform. The seismic wave groups T6, T8 and T9 are relatively stable in the region. Figure 3 As shown, the lateral continuity is good and the horizon interpretation is easy. The horizon interpretation density in this area is at least 16×16 and interpolated to 1×1, with a grid spacing of 25×25 meters.

[0061] (4) Collect surface elevation and perform plane interpolation. Surface elevation data is geodetic elevation data, which is obtained from the SPS file submitted by seismic acquisition or processing. It contains shot point and receiver point elevation data. The original shot line interval is 400 meters, and the receiver line interval is 200 meters. It needs to be interpolated to the same interval as the interpretation layer, which is 25×25 meters. Figure 4 shown.

[0062] (5) Correlation analysis is performed between the seismic marker horizon and the surface elevation. The surface data is compared with the interpreted horizon. The test area is selected with large surface fluctuations and areas with similar underlying structural imaging, such as Figure 4 As shown in the red frame, the local microstructures in the test area are less developed, so no trend surface is drawn. The shallow (T6), medium (T8), and deep (T9) layers are analyzed for correlation coefficients with the interpolated surface elevation. The correlation coefficients are -0.41, -0.33, and -0.65, respectively. Figure 6 shown.

[0063] (6) Perform large time window energy correlation analysis on shallow, medium and deep layers; further divide them into ①0-T6, ②T6-T8, ③T8-T9 according to the interpreted marker layers, and extract root mean square amplitude attributes respectively, such as Figure 5 As shown in the figure, the middle layer is extended up and down by 30 milliseconds, and the seismic attributes are correlated with each other. The correlation coefficients are ① and ②-0.25, ① and ③-0.75, and ② and ③0.27, respectively. Figure 6 shown.

[0064] (7) Evaluation of the imaging effect of seismic data. The correlation coefficients of the three interpreted seismic layers of shallow, medium and deep layers and the surface elevation were analyzed. The average absolute value was 0.46 and the average absolute value of the root mean square amplitude between the layers was 0.42. This area is a lithologic gas reservoir. The comprehensive correlation coefficient was calculated to be 0.432 through the weighted coefficient. According to the set correlation coefficient evaluation standard, the correlation coefficient is between 0-0.001, which means the imaging effect is good, between 0.001-0.1, which is second, between 0.1-0.5, which is poor, and between 0.5-1, which is extremely poor. According to the calculation result of 0.432, the imaging effect of the seismic data is poor. It is necessary to further optimize the processing parameters and reprocess them to eliminate the influence of surface factors on the identification of underground structures and geological bodies, further improve the quality of seismic data, and reduce the risk of well location demonstration.

[0065] Embodiment of the device for quantitatively evaluating the imaging effect of seismic data:

[0066] The present invention also provides a device for quantitatively evaluating the imaging effect of seismic data, the device comprising a memory and a processor, and a computer program stored in the memory and running on the processor, the processor is used to execute the computer program instructions stored in the memory to implement a method for quantitatively evaluating the imaging effect of seismic data. The specific process has been described in detail in the embodiment of the method for quantitatively evaluating the imaging effect of seismic data, and will not be repeated here. Among them, the memory can select a processing device such as a microprocessor MCU, a programmable logic device FPGA, etc., and the processor can select a storage device such as a mobile hard disk, a read-only memory (ROM), and a random access memory (RAM).

[0067] Specific implementation methods are given above, but the present invention is not limited to the described implementation methods. The basic idea of ​​the present invention lies in the above basic scheme. For ordinary technicians in this field, it does not take creative work to design various deformed models, formulas, and parameters according to the teachings of the present invention. Changes, modifications, substitutions, and variations of the implementation methods without departing from the principles and spirit of the present invention still fall within the scope of protection of the present invention.

Claims

1. A method for quantitatively evaluating the imaging effect of seismic data, characterized in that: The following steps are involved: 1) Obtain seismic marker layers at different depths and interpret the seismic marker layers; 2) Selecting a surface area and obtaining its surface elevation data, and processing the surface elevation data so that the interval of the surface elevation data is the same as the interval of the seismic layer; performing correlation analysis on the seismic layer data of different depths obtained in step 1) and the processed surface elevation data to obtain different correlation coefficients, and calculating the average of the absolute values ​​of all correlation coefficients to obtain a first average value; 3) further dividing the interpreted seismic marker layer obtained in step 1) into multiple layers, extracting root mean square amplitude energy attributes between adjacent layers, performing correlation analysis between the obtained root mean square amplitudes to obtain a number of correlation coefficients, and calculating the average of the absolute values ​​of all correlation coefficients to obtain a second average value; 4) A comprehensive correlation coefficient is obtained by weighted summing the first average value and the second average value according to geological characteristics, and the comprehensive correlation coefficient is used to evaluate the imaging effect of the seismic data; wherein, the smaller the comprehensive correlation coefficient result, the better the imaging effect of the seismic data.

2. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that: When in an oil and gas field dominated by lithologic oil and gas reservoirs, the first average value ranges from 0.7 to 0.9, the second average value ranges from 0.1 to 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 the oil and gas field is 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.

4. The method for quantitatively evaluating the imaging effect of seismic data according to claim 1, characterized in that: Step 1) The method for selecting seismic marker layers at different depths is as follows: multiple wells in the study area are selected, the logging curves of each well are standardized and then synthetic record calibration is performed, and wells with obvious marker layers and a correlation coefficient between synthetic record calibration and seismic wave groups greater than a set threshold are selected. The marker layer of the seismic wave group with uniform vertical distribution and stable in the region is the seismic marker layer.

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, specifically, interpolation processing is performed on the surface elevation data.

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: R xy =S xy / S x S y In the formula, R xy is the sample correlation coefficient, S xy is the sample covariance, S x represents the sample standard deviation of sample X, S y represents the sample standard deviation of sample Y, and 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 criteria for evaluating the imaging effect of seismic data using the comprehensive correlation coefficient are: if the comprehensive correlation coefficient is between 0-0.001, the imaging effect is good, if it is between 0.001-0.1, it is second best, if it is between 0.1-0.5, the imaging effect is poor, and if it is between 0.5-1, 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: In step 2), the surface area is selected from the area with the maximum elevation difference of more than 100 meters and the underlying structural image is similar to the area, and the small anticline with a structural amplitude between 10-40 meters, an area between 0-1 square kilometers and a total area of ​​more than 10% of the whole area is selected to smooth the trend surface and highlight the local structural features.

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

10. A device for quantitatively evaluating the imaging effect of seismic data, characterized in that: The device includes a memory and a processor, and a computer program stored in the memory and running on the processor, wherein the processor is used to execute computer program instructions stored in the memory to implement the method for quantitatively evaluating the imaging effect of seismic data as described in any one of claims 1 to 9.

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