阅读说明：本技术 一种利用微测井数据构建品质因子体的方法 (method for constructing quality factor body by using micro-logging data ) 是由 刁瑞 王常波 尚新民 刘立彬 韩站一 冯玉苹 崔庆辉 于 2019-07-22 设计创作，主要内容包括：本发明公开了一种利用微测井数据构建品质因子体的方法,收集工区范围内的微测井测量数据,对微测井数据进行分组处理；在同一组微测井数据内,利用修正S域变换方法,对微测井数据进行二维S域频谱分析,计算得到每一道数据的主频、低频值、高频值、有效频宽、特定频带能量占比等参数属性,通过不同深度激发和不同位置接收的地震波数据计算主频、低频值、高频值、有效频宽、特定频带能量占比等参数属性,结合传播距离权值和参数属性比例,得到综合参数属性,最终通过克里金延拓插值方法将不同测量点位置的品质因子曲线内插为近地表体三维品质因子体,对野外施工井深设计、近地表吸收衰减补偿和高分辨率处理、真地表粘弹性介质偏移等具有重要作用。(The invention discloses a method for constructing a quality factor body by using micro-logging data, which collects the micro-logging measurement data in a work area range and carries out grouping processing on the micro-logging data; in the same set of micro-logging data, a correction S-domain transformation method is utilized to perform two-dimensional S-domain spectral analysis on the micro-logging data, the parameter attributes such as the dominant frequency, the low frequency value, the high frequency value, the effective bandwidth, the specific frequency band energy ratio and the like of each channel of data are obtained through calculation, the parameter attributes such as the dominant frequency, the low frequency value, the high frequency value, the effective bandwidth, the specific frequency band energy ratio and the like are calculated through seismic wave data excited at different depths and received at different positions, the comprehensive parameter attributes are obtained through combination of propagation distance weight and parameter attribute ratio, and finally, the quality factor curves at different measuring point positions are interpolated into near-surface body three-dimensional quality factor bodies through a kriging interpolation method, so that the method has important effects on field construction well depth design, near-surface absorption attenuation compensation and high resolution processing, real-surface viscoelastic medium migration.)

1. A method for constructing a quality factor body by using micro-logging data is characterized by comprising the following steps:

S1, exciting at different depths in a shallow well within the range of a work area, receiving seismic wave data, namely micro-logging data, on the ground surface by adopting a plurality of detectors, and grouping the micro-logging data, wherein the different detectors at each excitation depth receive data as a group;

s2, in the same group of micro-logging data, performing two-dimensional S-domain frequency spectrum analysis on the micro-logging data by using a modified S-domain transformation method, and calculating to obtain the following parameter attributes of each data: main frequency, low frequency value, high frequency value, effective bandwidth and specific frequency band energy ratio;

S3, weighting propagation distances according to the arrangement positions of the detectors in the same group to obtain parameter attributes containing the distance weights, comparing the parameter attributes of different detectors, and eliminating abnormal attribute values;

S4, respectively calculating the parameter attributes of the dominant frequency, the low frequency value, the high frequency value, the effective bandwidth and the specific frequency band energy ratio of different excitation depths to obtain comprehensive parameter attributes corresponding to all the depths;

S5, calculating a propagation distance according to the excitation depth, and obtaining a quality factor curve at the measurement position of the micro logging well by combining the variation of the comprehensive parameter attributes corresponding to different excitation depths;

And S6, calculating quality factor curves of all the micro-logging measurement positions, and interpolating the measurement position scattered points in the work area range into a three-dimensional near-surface quality factor body by adopting a Krigin continuation interpolation method.

2. The method of constructing a quality factor volume using micro-log data of claim 1, wherein: in S1, shot excitation depth is denoted by i, i.e., i is 1, 2,...... times.n, different detectors at each excitation depth receive data in one group, each excitation depth receives data by using 5 or 9 or 13 detectors, and different detectors in the same group of data are denoted by j, i.e., j is 1, 2,.... times.m.

3. The method of constructing a quality factor volume using micro-log data of claim 2, wherein: in the S2, under the control of an S-domain signal-to-noise ratio curve and an S-domain purity curve, the main frequency Mf is obtained through an S-domain frequency spectrum_i，jLow frequency value Lf_i，jHf high frequency value_i，jEffective bandwidth Bf_i，jSpecific frequency band energy ratio Ef_i，jA parameter attribute, wherein: i is shot point excitation depth, i is 1, 2. j is a different detector in the same group of data, j is 1, 2. The signal-to-noise ratio curve is the ratio of effective signals and noise interference obtained by near-surface data in an S-domain frequency spectrum; wherein the purity curves are the purity of the respective band signals of the near-surface data in the S-domain spectrum.

4. The method of constructing a quality factor volume using micro-log data of claim 1, wherein: in S3, the propagation distance weighting value is δ_jWherein: j 1, 2.... m, representing different detectors; the obtained parameter attributes containing the distance weight are respectively as follows: main frequency delta_j×Mf_i，jLow frequency value delta_j×Lf_i，jHigh frequency value delta_j×Hf_i，jEffective bandwidth delta_j×Bf_i，jspecific frequency band energy ratio delta_j×Ef_i，jWherein: i is shot point excitation depth, i is 1, 2. j is a different detector in the same group of data, j is 1, 2. And (4) comparing the parameter attributes of different detectors j 1, 2, and rejecting abnormal attribute values in all m detectors.

5. The method of claim 1the method for constructing the quality factor body by using the micro-logging data is characterized by comprising the following steps of: in S4, the specific step of calculating the corresponding comprehensive parameter attribute is: when calculating the comprehensive parameter attribute, each attribute is endowed with different proportions, and the comprehensive parameter attributeWherein: delta_jAs propagation distance weight, M_jIs the dominant frequency Mf_i，jratio of (A) to (B), L_jis a low frequency value Lf_i，jRatio of (A) to (B), H_jAt a high frequency value Hf_i，jratio of (A) to (B)_jIs available bandwidth Bf_i，jProportion of (E), E_jEnergy fraction Ef for a particular frequency band_i，j1, 2, a.

Technical Field

The invention relates to the technical field of oil and gas field exploration, in particular to a method for constructing a quality factor body by using micro-logging data.

Background

Most oil fields in China belong to typical alluvial plains, and a complex near-surface can be roughly divided into a low-speed layer and a deceleration layer. Due to the thickness change and the severe transverse velocity change of the complex near-surface low-deceleration layer, the seismic wave propagation has the characteristics of propagation time delay and severe attenuation of energy and frequency. In the past three-dimensional seismic exploration, the problem of complex near-surface is not sufficiently researched, generally, the submergence surface of the surface layer is considered to be basically stable, the transverse change is relatively small, in the field data acquisition stage, only typical test point positions are selected in the whole area, small refraction or micro-logging measurement is carried out, and a spectral ratio method is adopted to calculate quality factors, so that the problems that the quality factor calculation is unstable and the numerical value difference is large exist, the accurate construction of complex near-surface quality factor bodies is restricted, the field acquisition of high-quality original data is influenced, the subsequent seismic data processing is influenced, and the fine three-dimensional seismic exploration and development are restricted.

Because the longitudinal and transverse speeds and lithology of the complex near-surface are changed greatly, and the drastic difference exists, the near-surface fine detection needs to be carried out from point to line and from line to surface, and the three-dimensional near-surface quality factor body is obtained. But due to the limitations of the prior art, the complex near-surface quality factor body is difficult to establish.

Therefore, it is highly desirable to develop a method for constructing quality factor using micro-logging data.

Disclosure of Invention

The invention aims to solve the defects in the prior art and provides a method for constructing a quality factor body by using micro-logging data.

In order to achieve the purpose, the invention is implemented according to the following technical scheme:

A method for constructing a quality factor body by using micro-logging data comprises the following steps:

S1, exciting at different depths in a shallow well within the range of a work area, receiving seismic wave data, namely micro-logging data, on the ground surface by adopting a plurality of detectors, and grouping the micro-logging data, wherein the different detectors at each excitation depth receive data as a group;

S2, in the same group of micro-logging data, performing two-dimensional S-domain frequency spectrum analysis on the micro-logging data by using a modified S-domain transformation method, and calculating to obtain the following parameter attributes of each data: main frequency, low frequency value, high frequency value, effective bandwidth and specific frequency band energy ratio;

s3, weighting propagation distances according to the arrangement positions of the detectors in the same group to obtain parameter attributes containing the distance weights, comparing the parameter attributes of different detectors, and eliminating abnormal attribute values;

S4, respectively calculating the parameter attributes of the dominant frequency, the low frequency value, the high frequency value, the effective bandwidth and the specific frequency band energy ratio of different excitation depths to obtain comprehensive parameter attributes corresponding to all the depths;

S5, calculating a propagation distance according to the excitation depth, and obtaining a quality factor curve at the measurement position of the micro logging well by combining the variation of the comprehensive parameter attributes corresponding to different excitation depths;

And S6, calculating quality factor curves of all the micro-logging measurement positions, and interpolating the measurement position scattered points in the work area range into a three-dimensional near-surface quality factor body by adopting a Krigin continuation interpolation method.

further, in S1, shot excitation depth is denoted by i, i.e., i is 1, 2,...... n, a group of different detectors receive data at each excitation depth, each excitation depth is received by using 5 or 9 or 13 detectors, and different detectors in the same group of data are denoted by j, i.e., j is 1, 2,...... m.

Further, in S2, under the control of the S-domain snr curve and the S-domain purity curve, the dominant frequency Mf is obtained by the S-domain spectrum_i，jLow frequency value Lf_i，jHf high frequency value_i，jEffective bandwidth Bf_i，jSpecific frequency band energy ratio Ef_i，jA parameter attribute, wherein: i is shot point excitation depth, i is 1, 2. j is a different detector in the same group of data, j is 1, 2. The signal-to-noise ratio curve is the ratio of effective signals and noise interference obtained by near-surface data in an S-domain frequency spectrum; wherein the purity curves are the purity of the respective band signals of the near-surface data in the S-domain spectrum.

Further, in S3, the propagation distance weighting value is δ_jWherein: j 1, 2.... m, representing different detectors; the obtained parameter attributes containing the distance weight are respectively as follows: main frequency delta_j×Mf_i，jLow frequency value delta_j×Lf_i，jHigh frequency value delta_j×Hf_i，jeffective bandwidth delta_j×Bf_i，jSpecific frequency band energy ratio delta_j×Ef_i，jWherein: i is shot point excitation depth, i is 1, 2. j is a different detector in the same group of data, j is 1, 2. And (4) comparing the parameter attributes of different detectors j 1, 2, and rejecting abnormal attribute values in all m detectors.

Further, in S4, the specific step of calculating to obtain the corresponding comprehensive parameter attribute is: when calculating the comprehensive parameter attribute, each attribute is endowed with different proportions, and the comprehensive parameter attributeWherein: delta_jAs propagation distance weight, M_jIs the dominant frequency Mf_i，jRatio of (A) to (B), L_jIs a low frequency value Lf_i，jRatio of (A) to (B), H_jAt a high frequencyvalue Hf_i，jRatio of (A) to (B)_jIs available bandwidth Bf_i，jProportion of (E), E_jEnergy fraction Ef for a particular frequency band_i，j1, 2, a.

Further, in step 5, the propagation distance is estimated by the excitation depth, and the comprehensive parameter attribute theta is calculated according to different excitation depths_iAnd i is 1, 2, a.

As a preferred technical scheme, in step 6, after calculating the quality factor curves of all the micro-logging measurement positions, a Krigin continuation interpolation method is adopted to interpolate scattered points of the measurement positions in the work area range into a three-dimensional body. In step 5, the quality factor curve at each measurement point position is obtained through calculation, and the change curve can be interpolated into a three-dimensional data volume, namely a near-surface volume three-dimensional quality factor volume, according to a kriging continuation interpolation method.

Compared with the prior art, the method gives full play to the characteristics of micro-logging data, adopts the S-domain signal-to-noise ratio curve and the S-domain purity curve constraint condition, calculates the parameter attributes such as the dominant frequency, the low frequency value, the high frequency value, the effective bandwidth, the specific frequency band energy ratio and the like through seismic wave data excited at different depths and received at different positions, combines the propagation distance weight and the parameter attribute proportion to obtain the comprehensive parameter attribute, and finally interpolates the quality factor curves at different measuring point positions into near-surface body three-dimensional quality factor bodies through a Krigin extension interpolation method, thereby having important effects on field construction well depth design, near-surface absorption attenuation compensation and high-resolution processing, real-surface viscoelastic medium migration and the like.

Drawings

FIG. 1 is a flow chart of the present invention.

FIG. 2 is a schematic diagram of a complex near-surface three-dimensional quality factor volume according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. The specific embodiments described herein are merely illustrative of the invention and do not limit the invention.

Referring to fig. 1-2, a method for constructing a quality factor volume using micro-logging data according to this embodiment includes the following steps:

(1) Collecting micro-logging measurement data within a work area range, wherein the micro-logging measurement data are seismic wave data which are excited at different depths in a shallow well, receiving the seismic wave data by using a plurality of detectors on the ground surface, shot point excitation depths are represented by i, namely i is 1, 2, a.

(2) Grouping the micro-logging data, wherein firstly, the micro-logging data at different measuring point positions are a group; secondly, the micro-logging data of different shot excitation depths are grouped, and then comprehensive parameter attribute calculation is carried out on the data received by different detectors of each excitation depth, as shown in fig. 1.

(3) In the micro-logging data of different detectors with the same excitation depth, a correction S-domain transformation method is utilized to perform two-dimensional S-domain spectral analysis on each piece of micro-logging data, the correction S-domain transformation method adopts an energy normalization variable width window function, time-division window spectral analysis is performed on the micro-logging data, different width window functions are adopted in different frequency bands, and frequency characteristics changing along with time are obtained through calculation. Obtaining main frequency Mf through S-domain frequency spectrum_i，jLow frequency value Lf_i，jHf high frequency value_i，jEffective bandwidth Bf_i，jSpecific frequency band energy ratio Ef_i，jIsoparametric attributes, wherein: i is shot point excitation depth, i is 1, 2. j is a different detector in the same group of data, j is 1, 2.

(4) And performing two-dimensional S-domain spectral analysis on the micro-logging data, and calculating the main frequency, low frequency value, high frequency value, effective bandwidth, specific frequency band energy ratio and other parameter attributes of each channel of data under the control of an S-domain signal-to-noise ratio curve and an S-domain purity curve, wherein the signal-to-noise ratio curve is the ratio of effective signals and noise interference of the near-surface data in the S-domain spectrum, and the purity curve is the purity of each frequency band signal of the near-surface data in the S-domain spectrum.

(5) According to the arrangement positions of the detectors in the same group, the propagation distance is weighted to be delta_jWherein: j 1, 2, the distance of propagation determines the weight δ_jThe size of (2).

(6) After the propagation distance is weighted, the parameter attribute containing the distance weight can be obtained, and the parameter attribute is respectively the main frequency delta_j×Mf_i，jLow frequency value delta_j×Lf_i，jhigh frequency value delta_j×Hf_i，jEffective bandwidth delta_j×Bf_i，jSpecific frequency band energy ratio delta_j×Ef_i，jWherein: i is shot point excitation depth, i is 1, 2. j is a different detector in the same group of data, j is 1, 2.

(7) And (3) comparing the parameter attributes of different detectors, namely comparing the parameter attributes of different detectors j 1, 2, i.

(8) after the abnormal attribute is removed, the comprehensive parameter attribute calculation is carried out, each attribute is endowed with different proportions according to the importance of the parameter attribute, and the comprehensive parameter attributeWherein: delta_jAs propagation distance weight, M_jIs the dominant frequency Mf_i，jRatio of (A) to (B), L_jIs a low frequency value Lf_i，jratio of (A) to (B), H_jAt a high frequency value Hf_i，jratio of (A) to (B)_jIs available bandwidth Bf_i，jProportion of (E), E_jEnergy fraction Ef for a particular frequency band_i，j1, 2, a.

(9) And calculating to obtain the comprehensive parameter attribute of the shot excitation depth according to the parameter attributes of the excitation depth such as the main frequency, the low frequency value, the high frequency value, the effective bandwidth, the specific frequency band energy ratio and the like, and returning to calculate the micro-logging data of different excitation depths in the same process if the calculation of the shot excitation depth is not completed.

(10) Respectively calculating the main frequencies Mf of different excitation depths_i，jLow frequency value Lf_i，jHf high frequency value_i，jEffective bandwidth Bf_i，jSpecific frequency band energy ratio Ef_i，jThe parameter attributes are equal, and a comprehensive parameter attribute theta of the corresponding depth is obtained_i1, 2,.. n. if all shot firing depths have been calculated, the process continues.

(11) Calculating propagation distance by excitation depth, and calculating the propagation distance according to the comprehensive parameter attribute theta of different excitation depths_iAnd i is 1, 2, a. The quality factor value is determined by the excitation depth and the comprehensive parameter attribute, the quality factor value of the excitation depth can be calculated according to the change conditions of the comprehensive parameter attribute at the current excitation depth and the next excitation depth through an amplitude and frequency attenuation relation formula, and after the quality factor values of all the excitation depths are calculated, the quality factor curve at the measuring position can be calculated through the excitation depth and the quality factor values.

(12) If the micro-logging data of different measuring point positions are not calculated, returning, calculating the micro-logging data of different measuring point positions in the same process to obtain quality factor curves of all measuring point positions, and if all the measuring points are calculated, continuing the process.

(13) After calculating the quality factor curves of all the micro-logging measurement positions, interpolating scattered points of the measurement positions in the work area range into a three-dimensional body by adopting a kriging extension interpolation method, carrying out weighted assignment through a mathematical covariance function based on a least square algorithm by the kriging extension interpolation method, and realizing three-dimensional interpolation of curves in a three-dimensional space, thereby completing extension modeling from the curves to the three-dimensional body. The kriging extension interpolation method is used to interpolate the quality factor curve into a three-dimensional data volume, i.e., a near-surface volume three-dimensional quality factor volume, as shown in fig. 2.

The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.