CN116819643A - Shale rock phase combination characterization and analysis method based on spectrum analysis time domain division - Google Patents
Shale rock phase combination characterization and analysis method based on spectrum analysis time domain division Download PDFInfo
- Publication number
- CN116819643A CN116819643A CN202310600329.0A CN202310600329A CN116819643A CN 116819643 A CN116819643 A CN 116819643A CN 202310600329 A CN202310600329 A CN 202310600329A CN 116819643 A CN116819643 A CN 116819643A
- Authority
- CN
- China
- Prior art keywords
- time domain
- shale
- curve
- lithofacies
- depth
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Geophysics And Detection Of Objects (AREA)
Abstract
本发明公开了基于频谱分析时间域划分的页岩岩相组合表征与分析方法，具体步骤包括：(1)对直井的自然伽马曲线作真垂深校正，获取真垂深校正后的自然伽马曲线；(2)对所述自然伽马曲线进行时间序列分析，得到时间域的偏心率旋回曲线以及不同深度下的沉积速率值；(3)基于所述沉积速率值将深度域页岩岩相组合转化为时间域页岩岩相组合，并基于所述时间域的偏心率旋回曲线对所述时间域页岩岩相组合进行划分，得到时间域页岩岩相的分类结果；(4)获取多个直井的所述时间域页岩岩相的分类结果并分析，得到地区内的岩相组合变化趋势。本发明针对时间格架下的岩相组合进行了精细表征与分析，可明确区域内横向岩相组合变化趋势。
The invention discloses a shale lithofacies combination characterization and analysis method based on time domain division of spectrum analysis. The specific steps include: (1) correcting the true vertical depth of the natural gamma curve of the vertical well, and obtaining the true vertical depth corrected natural gamma curve. horse curve; (2) perform time series analysis on the natural gamma curve to obtain the eccentricity cycle curve in the time domain and sedimentation rate values at different depths; (3) based on the sedimentation rate value, the depth domain shale rock The facies combination is converted into a time domain shale lithofacies combination, and the time domain shale lithofacies combination is divided based on the eccentricity cycle curve of the time domain, and the classification result of the time domain shale lithofacies is obtained; (4) The classification results of shale lithofacies in the time domain of multiple vertical wells are obtained and analyzed to obtain the change trend of lithofacies combinations in the region. This invention conducts detailed characterization and analysis of lithofacies combinations under a time frame, and can clarify the changing trend of lateral lithofacies combinations in the region.
Description
Technical Field
The invention relates to the technical field of oil and natural gas geology, in particular to a shale rock phase combination characterization and analysis method based on spectrum analysis time domain division.
Background
The study of shale lithology is the basis and important component of shale geologic features. At present, the shale lithofacies are divided more and more finely, which is favorable for identifying favorable lithofacies combination and distribution and provides important geological basis for shale gas exploration and evaluation. The prior art mainly aims at the shale lithofacies under the depth grids, does not carry out fine characterization on the shale lithofacies combination under the time grids, and cannot well reflect the trend of shale lithofacies combination change in the area, so that partial reasons for longitudinal differences of the lithofacies combination in the analysis area are quite resistant, the analysis is incomplete, and the effect is also not ideal.
And a large number of shale work area tracking researches show that the shale in the work area also has the difference of lithofacies combination, and the lithofacies combination has certain trend change according to certain factors. The traditional analysis aiming at shale lithofacies under a depth grid cannot well explore the trend and cannot clearly determine the reason of lithofacies combination change, so that a detailed and reliable basis cannot be provided when finer three-dimensional modeling of a work area is carried out.
Disclosure of Invention
The invention aims to overcome the defect that the prior art mainly aims at developing analysis on lithofacies under a depth grid and cannot clearly determine the variation trend of horizontal shale lithofacies combination, provides a shale lithofacies combination characterization and analysis method based on spectrum analysis time domain division, carries out fine characterization on the lithofacies combination under the time grid, and can explore the trend variation of the shale lithofacies combination.
In order to achieve the above object, the present invention provides the following technical solutions:
shale rock phase combination characterization and analysis method based on spectrum analysis time domain division comprises the following steps:
s1, performing true vertical depth correction on a natural gamma curve of a vertical well based on logging curve data and electric imaging data to obtain a natural gamma curve after true vertical depth correction;
s2, performing time sequence analysis on the natural gamma curve to obtain an eccentricity gyratory curve of a depth domain, converting the eccentricity gyratory curve of the depth domain into an eccentricity gyratory curve of a time domain, and obtaining deposition rate values at different depths based on the eccentricity gyratory curve of the time domain;
s3, obtaining a classification result of the depth-domain shale lithofacies, converting the depth-domain shale lithofacies combination into a time-domain shale lithofacies combination by combining the deposition rate value, and dividing the time-domain shale lithofacies combination based on an eccentricity gyratory curve of the time domain to obtain a classification result of the time-domain shale lithofacies;
s4, obtaining classification results of the time domain shale lithofacies of the plurality of vertical wells and analyzing the classification results to obtain a lithofacies combination change trend in the region.
As a preferred scheme of the invention, the shale rock phase combination characterization and analysis method based on spectrum analysis time domain division specifically comprises the following steps of:
s11, acquiring logging curve data and electric imaging data from the vertical well, and performing depth correction on the logging curve data and the electric imaging data;
s12, acquiring dynamic and static electric imaging images based on the electric imaging logging data after the depth correction, and picking up and recording stratum layer structural features in the electric imaging images;
s13, collecting logging curve data of the stratum layer structural characteristics, the depth corrected natural gamma data and the borehole inclination and azimuth data into a data set;
s14, performing true vertical depth correction on the natural gamma curve of the vertical well based on the data set, eliminating the influence of well deviation and stratum inclination angle on the form of the natural gamma curve, and obtaining the natural gamma curve after the true vertical depth correction.
As a preferred scheme of the invention, the shale rock phase combination characterization and analysis method based on spectrum analysis time domain division is used for picking up and recording the stratum layer structure of dynamic electric imaging, and the method comprises the following steps: at least one point of the stratigraphic structure is picked up and recorded within each 1 meter of depth of the dynamic electrogram image.
As a preferred scheme of the invention, the shale rock phase combination characterization and analysis method based on spectrum analysis time domain division comprises the following specific steps of:
s21, performing trending analysis on the natural gamma curve subjected to true vertical depth correction to obtain a trend line of the natural gamma curve subjected to true vertical depth correction;
s22, carrying out frequency spectrum analysis on the trend line to obtain a spectrogram, obtaining high-reliability frequency based on the spectrogram, and converting the spectrogram into a thickness spectrogram with logarithmic scale to obtain deposition convolution thickness corresponding to the high-reliability frequency;
s23, obtaining a stable eccentricity ratio period of a stratum in the area where the vertical well is located through investigation and analysis, and comparing the ratio of the eccentricity ratio with the deposition convolution thickness corresponding to the trend line power peak value to obtain the deposition convolution thickness corresponding to the eccentricity ratio period;
s24, calculating the length of a Gaussian filter window through the deposition convolution thickness corresponding to the eccentricity ratio period, and carrying out Gaussian filter on the natural gamma curve subjected to true vertical depth correction based on the length of the window to obtain an eccentricity ratio convolution curve of a depth domain;
s25, processing the eccentricity gyratory curve of the depth domain according to a tuning method in gyroglfrom study, obtaining an astronomical chronology scale, converting the eccentricity gyratory curve of the depth domain into the eccentricity gyratory curve of the time domain based on the astronomical chronology scale, and simultaneously calculating and recording deposition rate values at different depths in the conversion process.
As a preferred scheme of the invention, the shale rock phase combination characterization and analysis method based on spectrum analysis time domain division comprises the following specific steps of: and calculating the ratio of the top-bottom difference value of the eccentricity gyratory curve of the depth domain of the vertical well to the top-bottom difference value of the eccentricity gyratory curve of the time domain to obtain the ratio, namely the deposition rate value.
As a preferred scheme of the invention, the shale rock phase combination characterization and analysis method based on spectrum analysis time domain division comprises the following specific steps of:
s31, acquiring Lithoscanner data from the vertical well, carrying out depth correction on the Lithoscanner data, summarizing and classifying the depth-corrected Lithoscanner data, wherein the Lithoscanner data contains minerals of quartz, feldspar, calcite, dolomite and clay minerals, and classifying the lithofacies of shale according to the content of the minerals and the content difference of the minerals of different shale facies types in a three-terminal element classification scheme to obtain a depth-domain shale lithofacies classification result;
s32, calculating the ratio of the depth data in the depth domain shale lithofacies classification result to the deposition rate under the depth data, obtaining the time data of each shale lithofacies in the depth domain shale lithofacies classification result, and reconstructing the shale lithofacies combination of a time domain by the time data;
s33, dividing the time domain lithology by taking the change period of the eccentricity gyratory curve of the time domain as a reference to obtain a secondary lithology combination;
s34, the secondary lithofacies combination divided by the eccentricity gyratory curve of the same time domain is formed into a primary lithofacies combination, and the primary lithofacies combination and the secondary lithofacies combination jointly construct shale lithofacies combination of a single vertical well time domain.
As a preferred scheme of the invention, the shale rock phase combination characterization and analysis method based on spectrum analysis time domain division comprises the following specific steps of: dividing shale rock phase combination of the time domain into a plurality of independent units of the period number, namely a secondary rock phase combination according to the period number of the change period of the eccentricity gyratory curve of the time domain, and naming the secondary rock phase combination according to the rock phase with the largest proportion in the secondary rock phase combination.
As a preferred scheme of the invention, the shale rock phase combination characterization and analysis method based on spectrum analysis time domain division specifically comprises the following step S4: and S1-S3 steps are carried out on a plurality of vertical wells in different positions in a region to obtain time domain shale lithofacies classification results of the plurality of vertical wells, and rock facies combination change trends among the plurality of vertical wells are judged according to the time domain shale lithofacies classification results of the plurality of vertical wells, and then the rock facies combination change trends in the region are obtained by combining the geographic positions of the plurality of vertical wells.
As a preferred scheme of the invention, the shale rock phase combination characterization and analysis method based on spectrum analysis time domain division comprises the following specific steps of: and obtaining the duty ratio of the same secondary rock phase combination of the plurality of vertical wells according to the time domain shale rock phase combination of the plurality of vertical wells, and judging the change condition of the duty ratio, namely the rock phase combination change trend among the plurality of vertical wells.
Based on the same conception, a shale rock phase combination characterization and analysis device based on spectrum analysis time domain division is also provided, and comprises at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the shale rock phase combination characterization and analysis method based on spectral analysis time domain partitioning of any of the above.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, depth domain data are converted into time domain data based on time sequence analysis, shale rock phase combinations are divided based on time domains finally, fine characterization on the shale rock phase combinations under a time grid is achieved, and the trend of shale rock phase combination change in an area is well reflected.
Description of the drawings:
fig. 1 is a flow chart of a shale rock phase combination characterization and analysis method based on spectrum analysis time domain division in embodiment 1;
FIG. 2 is a detritus graph of the natural gamma curve after true vertical depth correction in example 1;
FIG. 3 is a graph of MTM spectrum obtained by spectral analysis of trend lines in example 1;
FIG. 4 is a three-terminal classification scheme diagram of embodiment 1;
FIG. 5 is a graph of classification results for example 1A well depth domain shale rock phase combinations;
FIG. 6 is a graph of the classification results of time domain shale rock combinations obtained from example 1A well based on time domain eccentricity convolution curve X405;
fig. 7 is a graph of the combination of classification results for time domain shale rock phase combinations obtained for example 1A, B, C and D well based on time domain eccentricity rotation curve X405.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should not be construed that the scope of the above subject matter of the present invention is limited to the following embodiments, and all techniques realized based on the present invention are within the scope of the present invention.
Example 1
The shale rock phase combination characterization and analysis method based on spectrum analysis time domain division as shown in fig. 1 is a flow chart, and specifically comprises the following steps:
s1, performing true vertical depth correction on a natural gamma curve of a vertical well based on logging curve data and electric imaging data to obtain a natural gamma curve after true vertical depth correction;
specifically, S1 includes the following steps:
s11, acquiring logging curve data and electric imaging data from the vertical well, and performing depth correction on the logging curve data and the electric imaging data;
specifically, log curve data, electric imaging data, lithoscan data and XRD data are obtained from a vertical well, a natural gamma curve of conventional log curve data is used as a standard curve, the natural gamma curve of the mobile electric imaging data and Lithoscan data has the same characteristics under depth as the standard curve, XRD data points are projected into the Lithoscan data curve, the XRD data depth is moved until the XRD data has the same trend with the Lithoscan data, and therefore accuracy of the Lithoscan data is judged, and depth correction of the conventional log curve data and the electric imaging data is completed.
S12, acquiring dynamic and static electric imaging images based on the electric imaging logging data after the depth correction, and picking up and recording stratum layer structural features in the electric imaging images;
specifically, since the electrical imaging processing result further includes multiple types of data, such as low-resistance slits, high-resistance slits, and induced slits, the layer seams need to be separately extracted to avoid the influence of other non-layer signals, wherein the layer seams include layer structure characteristics of the stratum.
Further, in order to reduce errors in the dynamic electric imaging image pickup and recording of the stratigraphic structure characteristics, and avoid that the subsequent processing effect is not ideal due to error accumulation, the embodiment picks up and records at least one point of stratigraphic structure in the depth of each 1 meter of the dynamic electric imaging image, so that the pickup and recording lay a good foundation for subsequent true vertical depth correction of a natural gamma curve of a vertical well, further the subsequent time sequence analysis results are more accurate, and the lithofacies division precision of the embodiment is improved.
S13, collecting logging curve data of the stratum layer structural characteristics, the depth corrected natural gamma data and the borehole inclination and azimuth data into a data set;
s14, performing true vertical depth correction on the natural gamma curve of the vertical well based on the data set, eliminating the influence of well deviation and stratum inclination angle on the form of the natural gamma curve, and obtaining the natural gamma curve after the true vertical depth correction.
S2, performing time sequence analysis on the natural gamma curve to obtain an eccentricity gyratory curve of a depth domain, converting the eccentricity gyratory curve of the depth domain into an eccentricity gyratory curve of a time domain, and obtaining deposition rate values at different depths based on the eccentricity gyratory curve of the time domain;
s21, performing trending analysis on the natural gamma curve subjected to true vertical depth correction to obtain a trend line of the natural gamma curve subjected to true vertical depth correction;
specifically, four trending methods, namely a LOWESS local weighted regression scattered point smoothing method, a rLOWESS robust local weighted regression scattered point smoothing method, a LOESS local weighted scattered point regression method and a rlooess robust local weighted scattered point regression method, are adopted to carry out trending analysis on the single vertical well natural gamma curve after true vertical depth correction to obtain a result shown in fig. 2, and the result shown in fig. 2 is compared and found that the morphology trend of the LOESS curve obtained by the LOESS local weighted scattered point regression method is closest to that of the single vertical well natural gamma curve after true vertical depth correction, so that the LOESS curve is adopted as a trend line of the single vertical well natural gamma curve after true vertical depth correction to carry out subsequent processing.
S22, carrying out frequency spectrum analysis on the trend line to obtain a spectrogram, obtaining high-reliability frequency based on the spectrogram, and converting the spectrogram into a thickness spectrogram with logarithmic scale to obtain deposition convolution thickness corresponding to the high-reliability frequency;
specifically, the fast fourier transform provided by Matlab is used for processing the LOESS curve to obtain an MTM spectrogram as shown in fig. 3, high-reliability frequency with reliability exceeding 95% is recorded, and the high-reliability deposition convolution thickness corresponding to the high-reliability frequency is obtained according to logarithmic scale conversion, and fig. 3 shows that the deposition convolution thicknesses corresponding to the frequencies with power peak value exceeding 95% are respectively 11.40m, 3.65m, 3.28m, 2.99m, 2.74m, 2.27m, 2.12m, 1.69m and 1.62m.
S23, obtaining a stable eccentricity ratio period of a stratum in the area where the vertical well is located through investigation and analysis, and comparing the ratio of the eccentricity ratio with the deposition convolution thickness corresponding to the trend line power peak value to obtain the deposition convolution thickness corresponding to the eccentricity ratio period;
specifically, the stable eccentricity period of the stratum is obtained through investigation, for example, the stable long eccentricity period 405kyr and the short eccentricity periods 125kyr and 95kyr of the five-peak group-one section stratum, the ratio of the deposition curl thickness 11.40:3.65:2.74 is most similar to the eccentricity period ratio 405:125:95=4.26:1.32:1, so that the deposition curl thicknesses of 11.40m, 3.65m and 2.74m are respectively 405kyr long eccentricity period, 125kyr short eccentricity period and 95kyr short eccentricity period, which are respectively denoted as S405, S125 and S95, and the embodiment selects S405 for subsequent study.
S24, calculating the length of a Gaussian filter window through the deposition convolution thickness corresponding to the eccentricity ratio period, and carrying out Gaussian filter on the natural gamma curve subjected to true vertical depth correction based on the length of the window to obtain an eccentricity ratio convolution curve of a depth domain;
specifically, the reciprocal is calculated by using the numerical value of S405, a gaussian filter window length is set based on 80% and 120% of the reciprocal, and is denoted as C405, and gaussian filtering is performed on the trend line under the window length of C405, so as to obtain a gaussian filtered curve D405, namely a long eccentricity gyrus curve of the depth domain.
Further, C405 is calculated to be 0.070175-0.105263, so 405kyr long eccentricity periods correspond to gaussian filter window lengths of 0.070175-0.105263.
S25, processing the eccentricity gyratory curve of the depth domain according to a tuning method in gyroglfrom study, obtaining an astronomical chronology scale, converting the eccentricity gyratory curve of the depth domain into the eccentricity gyratory curve of the time domain based on the astronomical chronology scale, and simultaneously calculating and recording deposition rate values at different depths in the conversion process.
Specifically, an astronomical chronograph corresponding to a period 405kyr is established based on a D405 curve according to a tuning method in a gyroglfrom study, and a long-eccentricity gyroid curve of a depth domain is converted into a long-eccentricity gyroid curve of a time domain by using the astronomical chronograph, and is recorded as X405.
Further, the calculation of the deposition rate value specifically includes: setting the difference of the top and bottom of a long eccentricity gyratory curve X405 of the time domain of the part with different formation depths of the A well, namely corresponding depths, as T, and obtaining the deposition rate value V by using the formation thickness H/the deposition duration T=the deposition rate value V.
S3, obtaining a classification result of the depth-domain shale lithofacies, converting the depth-domain shale lithofacies combination into a time-domain shale lithofacies combination by combining the deposition rate value, and dividing the time-domain shale lithofacies combination based on an eccentricity gyratory curve of the time domain to obtain a classification result of the time-domain shale lithofacies;
specifically, the specific step of S3 includes:
s31, acquiring Lithoscanner data from the vertical well, carrying out depth correction on the Lithoscanner data, summarizing and classifying the depth-corrected Lithoscanner data, wherein the Lithoscanner data contains minerals of quartz, feldspar, calcite, dolomite and clay minerals, and classifying the lithofacies of shale according to the content of the minerals and the content difference of the minerals of different shale facies types in a three-terminal element classification scheme to obtain a depth-domain shale lithofacies classification result;
specifically, the depth correction is performed on the Lithoscanner data, and the specific correction method is as described in S11, the depth corrected Lithoscanner special logging data is summarized and classified, the Lithoscanner data includes minerals of quartz, feldspar, calcite, dolomite and clay minerals, wherein the minerals of siliceous=quartz+longstone, carbonate mineral=calcite+dolomite, the shale rock phases are classified according to the content of siliceous minerals, carbonate minerals and clay minerals according to a three-terminal classification scheme as shown in fig. 4, for example, if the mineral content at the depth shows calcia <25%, clay <25% and siliceous >75%, the depth stratum is judged to be siliceous rock, and if calcia <25%, clay <25% and siliceous <75% are judged to be S-2 siliceous shale, and after the classification is completed, the depth domain shale rock phase classification result as shown in fig. 5 is obtained.
S32, calculating the ratio of the depth data in the depth domain shale lithofacies classification result to the deposition rate under the depth data, obtaining the time data of each shale lithofacies in the depth domain shale lithofacies classification result, and reconstructing the shale lithofacies combination of a time domain by the time data;
specifically, dividing the thickness of each shale facies combination in the depth domain shale facies classification result by the deposition rate value V at the corresponding depth to obtain time data of the shale facies combination, and obtaining the time domain shale facies of the a well shown in fig. 6 by referring to the curve X405 through the time data.
S33, dividing the time domain lithology by taking the change period of the eccentricity gyratory curve of the time domain as a reference to obtain a secondary lithology combination;
specifically, as shown in fig. 6, the X405 curve has 10 periods, and based on the 10 periods, the lithofacies are divided into 10 independent units, namely, two-stage lithofacies, so that it can be obviously seen that the lithofacies in each two-stage lithofacies are relatively stable, and generally, the proportion of one lithofacies in one two-stage lithofacies has absolute advantages, and as shown in fig. 6, the main M-3 type two-stage lithofacies mainly develops the M-3 mixed shale, and the proportion exceeds 93%.
S34, the secondary lithofacies combination divided by the eccentric rate gyratory curve of the same time domain is formed into a primary lithofacies combination, the primary lithofacies combination and the secondary lithofacies combination jointly construct shale lithofacies combination of a single vertical well time domain, and the adopted multi-stage lithofacies combination division scheme not only ensures finer division results, but also is more beneficial to judging shale lithofacies change trend in the region.
Specifically, 10 secondary rock phase combinations shown in fig. 6 form a primary rock phase combination unit corresponding to a five-peak group-dragon one section stratum, the primary rock phase combination is analyzed to find that the interior of the primary rock phase combination is sequentially deposited with mixed type, main M-2 type, main M-3 type and main CM-1 type secondary rock phase combination from bottom to top, and the upper-lower superposition relationship of the secondary rock phase combination in the primary rock phase combination can be seen from fig. 5 to be that the mixed type secondary rock phase combination is positioned at the bottommost part, then the main M-2 type is developed, two main M-3 types are developed on the main rock phase combination, and then 1 main CM-1 type, 2 main M-2 types and 3 main CM-1 types are sequentially superposed on the main rock phase combination.
S4, obtaining classification results of the time domain shale lithofacies of the plurality of vertical wells and analyzing the classification results to obtain a lithofacies combination change trend in the region.
Specifically, S4 includes the following steps:
S1-S3 steps are carried out on other B, C and D vertical wells from west to east in the region X, so that time domain shale lithofacies classification results of the A, B, C and D four vertical wells are obtained, as shown in FIG. 7, two-level lithofacies combinations of the four vertical wells respectively have differences in longitudinal stacking relation, and the method is characterized in that the bottommost parts of the four wells are developed with mixed type, and the A, B and D wells in the previous period are developed with main M-2 type combinations, but the C well does not develop the two-level lithofacies combinations; in the cycle above, 2 main M-3 type secondary rock phase combinations are developed in the A, B and C wells, and only 1 main M-3 type secondary rock phase combination is developed in the D well.
Further, the ratio of shale types in the secondary lithofacies combination corresponding to different wells in the A, B, C and D four vertical wells is also different. The transverse contrast can be further refined by analyzing the duty cycle of each facies within the secondary facies combination. For example, after the analysis of the rock phase proportion of the main M-3 type rock phase combination, the proportion of the M-3 mixed shale in the M-3 type rock phase combination from the A well to the C well is continuously reduced, so that the reduction of the number of the main M-3 type combination from the A well to the D well is also reflected in the number of the secondary rock phase combination.
Because the geographic positions from the A well to the D well are always east and west, the A well is located in the west of the X region, and the D well is located in the east of the X region, based on analysis of the classification results of the shale lithofacies in the single well time domain of the four vertical wells, the lithofacies combination change trend among the four vertical wells is obtained, and therefore the lithofacies combination change trend of the X region, which comprises the combination deposition period length, the lithofacies combination type and the thickness change trend, is obtained.
The method disclosed by the invention can be effectively applied to shale, can be widely and effectively applied to sand shale stratum with frequent interbedding, and has great significance for subsequent compact sandstone development and analysis of sea-land shale oil-gas reservoirs.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The shale rock phase combination characterization and analysis method based on the spectrum analysis time domain division is characterized by comprising the following steps of:
s1, performing true vertical depth correction on a natural gamma curve of a vertical well based on logging curve data and electric imaging data to obtain a natural gamma curve after true vertical depth correction;
s2, performing time sequence analysis on the natural gamma curve to obtain an eccentricity gyratory curve of a depth domain, converting the eccentricity gyratory curve of the depth domain into an eccentricity gyratory curve of a time domain, and obtaining deposition rate values at different depths based on the eccentricity gyratory curve of the time domain;
s3, obtaining a classification result of the depth-domain shale lithofacies, converting the depth-domain shale lithofacies combination into a time-domain shale lithofacies combination by combining the deposition rate value, and dividing the time-domain shale lithofacies combination based on an eccentricity gyratory curve of the time domain to obtain a classification result of the time-domain shale lithofacies;
s4, obtaining classification results of the time domain shale lithofacies of the plurality of vertical wells and analyzing the classification results to obtain a lithofacies combination change trend in the region.
2. The shale rock phase combination characterization and analysis method based on spectrum analysis time domain division according to claim 1, wherein the step S1 specifically comprises the following steps:
s11, acquiring logging curve data and electric imaging data from the vertical well, and performing depth correction on the logging curve data and the electric imaging data;
s12, acquiring dynamic and static electric imaging images based on the electric imaging logging data after the depth correction, and picking up and recording stratum layer structural features in the electric imaging images;
s13, collecting logging curve data of the stratum layer structural characteristics, the depth corrected natural gamma data and the borehole inclination and azimuth data into a data set;
s14, performing true vertical depth correction on the natural gamma curve of the vertical well based on the data set, eliminating the influence of well deviation and stratum inclination angle on the form of the natural gamma curve, and obtaining the natural gamma curve after the true vertical depth correction.
3. The method for characterizing and analyzing shale rock phase based on spectral analysis time domain division as recited in claim 2, wherein the method for picking up and recording the stratum layer structure of dynamic electric imaging is as follows: at least one point of the stratigraphic structure is picked up and recorded within each 1 meter of depth of the dynamic electrogram image.
4. The shale rock phase combination characterization and analysis method based on spectrum analysis time domain division according to claim 1, wherein the specific step S2 comprises:
s21, performing trending analysis on the natural gamma curve subjected to true vertical depth correction to obtain a trend line of the natural gamma curve subjected to true vertical depth correction;
s22, carrying out frequency spectrum analysis on the trend line to obtain a spectrogram, obtaining high-reliability frequency based on the spectrogram, and converting the high-reliability frequency into a deposition convolution thickness of a logarithmic scale to obtain the deposition convolution thickness corresponding to the high-reliability frequency;
s23, obtaining a stable eccentricity ratio period of a stratum in the area where the vertical well is located through investigation and analysis, and comparing the ratio of the eccentricity ratio with the deposition convolution thickness corresponding to the trend line power peak value to obtain the deposition convolution thickness corresponding to the eccentricity ratio period;
s24, calculating the length of a Gaussian filter window through the deposition convolution thickness corresponding to the eccentricity ratio period, and carrying out Gaussian filter on the natural gamma curve subjected to true vertical depth correction based on the length of the window to obtain an eccentricity ratio convolution curve of a depth domain;
s25, processing the eccentricity gyratory curve of the depth domain according to a tuning method in gyroglfrom study, obtaining an astronomical chronology scale, converting the eccentricity gyratory curve of the depth domain into the eccentricity gyratory curve of the time domain based on the astronomical chronology scale, and simultaneously calculating and recording deposition rate values at different depths in the conversion process.
5. The method for characterizing and analyzing shale rock phase combination based on spectrum analysis time domain division as recited in claim 4, wherein the calculating of the sedimentary rate value comprises the specific steps of: and calculating the ratio of the top-bottom difference value of the eccentricity gyratory curve of the depth domain of the vertical well to the top-bottom difference value of the eccentricity gyratory curve of the time domain to obtain the ratio, namely the deposition rate value.
6. The shale rock phase combination characterization and analysis method based on spectrum analysis time domain division as claimed in claim 1, wherein the specific step S3 comprises:
s31, acquiring Lithoscanner data from the vertical well, carrying out depth correction on the Lithoscanner data, summarizing and classifying the depth-corrected Lithoscanner data, wherein the Lithoscanner data contains minerals of quartz, feldspar, calcite, dolomite and clay minerals, and classifying the lithofacies of shale according to the content of the minerals and the content difference of the minerals of different shale facies types in a three-terminal element classification scheme to obtain a depth-domain shale lithofacies classification result;
s32, calculating the ratio of the depth data in the depth domain shale lithofacies classification result to the deposition rate under the depth data, obtaining the time data of each shale lithofacies in the depth domain shale lithofacies classification result, and reconstructing the shale lithofacies combination of a time domain by the time data;
s33, dividing the time domain lithology by taking the change period of the eccentricity gyratory curve of the time domain as a reference to obtain a secondary lithology combination;
s34, the secondary lithofacies combination divided by the eccentricity gyratory curve of the same time domain is formed into a primary lithofacies combination, and the primary lithofacies combination and the secondary lithofacies combination jointly construct shale lithofacies combination of a single vertical well time domain.
7. The shale rock phase combination characterization and analysis method based on spectrum analysis time domain division as claimed in claim 6, wherein the specific step S33 comprises: dividing shale rock phase combination of the time domain into a plurality of independent units of the period number, namely a secondary rock phase combination according to the period number of the change period of the eccentricity gyratory curve of the time domain, and naming the secondary rock phase combination according to the rock phase with the largest proportion in the secondary rock phase combination.
8. The shale rock phase combination characterization and analysis method based on spectrum analysis time domain division according to claim 1, wherein step S4 specifically comprises: and S1-S3 steps are carried out on a plurality of vertical wells in different positions in a region to obtain time domain shale lithofacies classification results of the plurality of vertical wells, and rock facies combination change trends among the plurality of vertical wells are judged according to the time domain shale lithofacies classification results of the plurality of vertical wells, and then the rock facies combination change trends in the region are obtained by combining the geographic positions of the plurality of vertical wells.
9. The shale rock phase combination characterization and analysis method based on the spectrum analysis time domain division according to claim 8, wherein the specific step of judging the rock phase combination change trend among the plurality of vertical wells according to the plurality of vertical well time domain shale rock phase combinations comprises the following steps: and obtaining the duty ratio of the same secondary rock phase combination of the plurality of vertical wells according to the time domain shale rock phase combination of the plurality of vertical wells, and judging the change condition of the duty ratio, namely the rock phase combination change trend among the plurality of vertical wells.
10. Shale rock phase combination characterization and analysis device based on spectrum analysis time domain division is characterized by comprising at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the spectral analysis time domain partition based shale rock phase characterization and analysis method of any of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310600329.0A CN116819643A (en) | 2023-05-25 | 2023-05-25 | Shale rock phase combination characterization and analysis method based on spectrum analysis time domain division |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310600329.0A CN116819643A (en) | 2023-05-25 | 2023-05-25 | Shale rock phase combination characterization and analysis method based on spectrum analysis time domain division |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116819643A true CN116819643A (en) | 2023-09-29 |
Family
ID=88124954
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310600329.0A Pending CN116819643A (en) | 2023-05-25 | 2023-05-25 | Shale rock phase combination characterization and analysis method based on spectrum analysis time domain division |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116819643A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119126234A (en) * | 2024-11-06 | 2024-12-13 | 中国石油大学(华东) | A method for identifying and dividing shale sedimentary cycles of different scales |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180031732A1 (en) * | 2015-02-13 | 2018-02-01 | Schlumberger Technology Corporation | Diagenetic and Depositional Rock Analysis |
| CN111650670A (en) * | 2020-06-24 | 2020-09-11 | 中国地质大学（武汉） | A method, device and storage medium for identifying shale deposition rhythm |
| CN115166854A (en) * | 2021-04-07 | 2022-10-11 | 中国石油化工股份有限公司 | Time-domain high-frequency sequence stratigraphic body construction method, device, electronic device and medium |
-
2023
- 2023-05-25 CN CN202310600329.0A patent/CN116819643A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180031732A1 (en) * | 2015-02-13 | 2018-02-01 | Schlumberger Technology Corporation | Diagenetic and Depositional Rock Analysis |
| CN111650670A (en) * | 2020-06-24 | 2020-09-11 | 中国地质大学（武汉） | A method, device and storage medium for identifying shale deposition rhythm |
| CN115166854A (en) * | 2021-04-07 | 2022-10-11 | 中国石油化工股份有限公司 | Time-domain high-frequency sequence stratigraphic body construction method, device, electronic device and medium |
Non-Patent Citations (2)
| Title |
|---|
| 王浡 等: "天文周期驱动下湖相细粒沉积岩有机质富集模式——以东营凹陷LY1 井沙三下—沙四上亚段为例", 沉积学报, vol. 43, no. 2, 28 March 2023 (2023-03-28), pages 750 - 763 * |
| 闫建平 等: "湖相泥页岩天文地层旋回测井识别在沾化凹陷沙三下亚段的应用", 测井技术, vol. 41, no. 6, 31 December 2017 (2017-12-31), pages 701 - 706 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119126234A (en) * | 2024-11-06 | 2024-12-13 | 中国石油大学(华东) | A method for identifying and dividing shale sedimentary cycles of different scales |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114114459A (en) | Deep-ultra-deep carbonate rock thin reservoir prediction method under phase control constraint | |
| CN111856566B (en) | Method and device for predicting thin reservoir in sand body of lake-phase beach dam | |
| CN107065011B (en) | A kind of curve frequencies fusion method applied to continental basins reservoir inversion | |
| CN103527184A (en) | Method and system for predicting dolomite reservoir | |
| CN115113268A (en) | Three-level sequence dividing method based on Mie's cycle | |
| CN113655544A (en) | High-precision stratum dividing method for organic-rich shale | |
| CN108427140B (en) | A method of for small scale fracture and cave reservoir seismic recognition | |
| CN111090117B (en) | Effective reservoir prediction method and system under phase control forward constraint | |
| CN116819643A (en) | Shale rock phase combination characterization and analysis method based on spectrum analysis time domain division | |
| CN113109875A (en) | Inversion method of carbonate rock reservoir under full waveform velocity field constraint | |
| CN115629417B (en) | Multi-scale Fusion and Facies-Controlled Grain Bank Characterization Method Based on Seismic Sedimentology | |
| CN110673211B (en) | A Quality Factor Modeling Method Based on Logging and Seismic Data | |
| CN111352154A (en) | Reservoir prediction method based on wide-azimuth earthquake | |
| CN115685344A (en) | Reservoir determination method and device, storage medium and electronic equipment | |
| CN109143335B (en) | A method, system, medium and device for making synthetic seismic records | |
| CN114578444A (en) | Astronomical stratum convolution dividing method based on wavelet multi-scale analysis | |
| CN118625389A (en) | High-precision 3D seismic slice extraction method and system for thin interbedded reservoirs | |
| WO2025035685A1 (en) | Speed model construction method, control device, and storage medium | |
| CN110389381B (en) | Sand reservoir prediction method and device based on seismic attributes | |
| CN115113267A (en) | Mie's cycle-based seismic interface age determination method | |
| RU2145099C1 (en) | Method for prospecting of oil-gas pool | |
| CN115524743A (en) | High resolution elastic impedance direct inversion method without well constraints in frequency domain | |
| CN113671565A (en) | Seismic multi-scale reservoir prediction method for gas reservoir development of huge thick reservoir | |
| CN113514891B (en) | A method for determining the extent of oil-water transition zone based on seismic inversion | |
| CN119828224B (en) | Lithology compensation-based sediment phase control high-resolution inversion method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |