CN111856584B - Thin reservoir hydrocarbon detection method and device based on super-gather - Google Patents
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Abstract
The invention provides a thin reservoir hydrocarbon detection method and a device based on a super-gather, wherein the method comprises the steps of obtaining a post-stack seismic data volume of a research work area; acquiring interpretation data of top and bottom layers of a thin reservoir in a research work area; constructing a super-channel set seismic data volume according to the acquired post-stack seismic data and the thin reservoir top and bottom horizon interpretation data; carrying out spectrum analysis on the super-channel set seismic data volume to obtain a super-channel set seismic data amplitude spectrum; calculating to obtain a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set; and detecting the thin reservoir hydrocarbons according to the low-frequency energy ratio attribute. Compared with a conventional thin reservoir prediction method, the method and the device provided by the invention overcome the problems of incomplete thin reservoir seismic response and insufficient data to be analyzed, improve the stability of reservoir prediction, reduce the ambiguity of attribute analysis and improve the accuracy of thin reservoir hydrocarbon detection.
Description
Technical Field
The invention relates to a thin reservoir hydrocarbon detection method and device based on a super gather, and belongs to the technical field of geophysical exploration of petroleum.
Background
With the continuous deepening of oil exploration and development, the main targets of oil and gas geophysical exploration and development are continuously complicated and concealed, the aim of reservoir prediction is changed from tectonics to lithology and tectonics-lithology, so that the difficulty of reservoir prediction is more and more high, and the requirement on prediction precision is more and more high. In order to meet the requirement of refined reservoir description, a new geophysical prospecting technology is continuously updated, a prestack inversion technology, an anisotropy characterization technology, an attribute fusion technology and the like are developed, the reservoir prediction precision is greatly improved by the application of the new technology, and meanwhile, the risk of exploration and development is greatly reduced. With the deep exploration degree, the thin reservoir prediction of an old oil field development area gradually becomes a main research direction and reserve growth point, and compared with the conventional thick reservoir prediction, the thin reservoir is small in thickness and usually smaller than 10 meters, so that the seismic resolution cannot meet the requirement of stably identifying the oil-gas seismic response abnormity, and the ambiguity of the thin reservoir prediction is increased.
At present, for thin reservoir prediction, a conventional method is to perform frequency extension processing on original seismic data, then perform sequence stratigraphic division based on a frequency extension seismic data body to obtain corresponding seismic response of the thin reservoir, perform conventional attribute analysis based on a sequence stratigraphic framework, extract various attributes of a sequence interface corresponding to the thin reservoir, and finally perform reservoir prediction and hydrocarbon detection according to well calibration and preferred favorable seismic attributes. The conventional method calibrates the seismic response of the thin reservoir through sequence grid division, then carries out attribute analysis in a targeted manner, and solves the problem of thin reservoir hydrocarbon detection to a certain extent, however, the method has serious defects, wherein the most important defect is that the requirement of attribute analysis on the completeness of a time window or a sampling point is not met, the response of the thin reservoir on seismic data usually corresponds to one seismic axis or a half seismic axis, and for the stability of attribute analysis, the time window for extracting attributes usually requires more than two complete waveforms, so that the requirement of an algorithm can be met in the process of attribute analysis, and a desired attribute extraction result is obtained.
Therefore, it has become an urgent technical problem in the art to provide a method and apparatus for detecting hydrocarbons in thin reservoirs based on a super gather.
Disclosure of Invention
To address the above-described shortcomings and drawbacks, it is an object of the present invention to provide a method for thin reservoir hydrocarbon detection based on a super gather.
It is still another object of the present invention to provide a thin reservoir hydrocarbon detection device based on a gather of super-traces.
Yet another object of the present invention is to provide a computer apparatus.
It is yet another object of the present invention to provide a computer-readable storage medium.
In order to achieve the above object, in one aspect, the present invention provides a thin reservoir hydrocarbon detection method based on a super-gather, wherein the thin reservoir hydrocarbon detection method based on the super-gather comprises:
acquiring a post-stack seismic data volume of a research work area;
acquiring interpretation data of top and bottom layers of a thin reservoir in a research work area;
constructing a super-gather seismic data volume according to the obtained post-stack seismic data and the thin reservoir top-bottom horizon interpretation data;
carrying out spectrum analysis on the super-channel set seismic data volume to obtain a super-channel set seismic data amplitude spectrum;
calculating to obtain a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set;
and detecting the thin reservoir hydrocarbons according to the low-frequency energy ratio attribute.
In the method, preferably, the obtained range of the line number of the post-stack seismic data volume plane of the research work area needs to include a research target area, and the time range of the data volume in the vertical direction needs to include a target reservoir.
In the method described above, preferably, the acquiring of the top-bottom horizon interpretation data of the thin reservoir of the research work area includes: and carrying out seismic horizon interpretation work aiming at the target layer of the target research work area to obtain the top and bottom horizon interpretation data of the thin reservoir within the range of the target research work area.
In the method described above, preferably, the constructing a super gather seismic data volume according to the acquired post-stack seismic data and the thin reservoir top-bottom horizon interpretation data includes:
1) Intercepting seismic data of a thin reservoir section by utilizing a top and bottom interpretation horizon of the thin reservoir;
2) Sequentially splicing each intercepted seismic data and four adjacent closest seismic data in series according to the sequence of south, east, west and north;
3) Replacing the seismic data of the original center channel by the seismic data obtained by serial splicing;
4) Splicing all data in the research work area according to the processing procedures shown in the steps 1) to 3) to obtain a thin reservoir interval super gather seismic data volume.
In the thin reservoir section gather seismic data volume obtained by the invention, the data sampling point of each channel is about 5 times of that of the original gather, the stability of an attribute analysis algorithm is ensured, and because the splicing is carried out on the adjacent and nearest four channels of seismic data, and the plane spread range of an underground target body is far larger than the seismic data range participating in splicing, the seismic data participating in splicing reflects the seismic response of the same reservoir target, so that the finally obtained thin reservoir section gather seismic data not only meets the data quantity requirement of attribute analysis, but also keeps the target consistency of the seismic response.
In the above method, preferably, the tandem splicing is performed according to the following formula 1):
Sms＝{Sm,Se,Ss,Sw,Snformula 1);
in formula 1), SmsSeismic data volume of a gather of super-channels for a central channel in a thin reservoir interval, SmThe original seismic data volume, S, being a central trace in a thin reservoir intervale,Ss,Sw,SnOriginal seismic data bodies in the east direction, the south direction, the west direction and the north direction of a certain central channel of the thin reservoir section are respectively represented by { } representing that each channel set is sequentially connected in series for splicing processing operation.
In the method described above, preferably, performing spectrum analysis on the gather seismic data volume to obtain a gather seismic data amplitude spectrum includes:
performing spectrum analysis on the super-gather seismic data volume by utilizing Fourier transform to obtain a super-gather seismic data amplitude spectrum shown in the following formula 2):
in formula 2), A (f) is the amplitude spectrum of the seismic data of the super channel set, SmsThe seismic data volume is a super channel set seismic data volume of a certain central channel of the thin reservoir section, j is an imaginary number unit, f is frequency and is Hz, t is time and is s.
In the method described above, preferably, the calculating the low frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the gather of super channels includes:
respectively calculating a low-frequency band energy attribute, a full-frequency band energy attribute and a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set, wherein the calculation formulas are respectively shown in the following formula 3) to formula 5):
equation 3) -equation 5), A (f) is the amplitude spectrum of the seismic data of the super channel set, f is frequency and has the unit of Hz, flow1Is the starting frequency of the low frequency band in Hz, flow2Is a low band stop frequency in Hz, fhighIs the high band end frequency in Hz, ElowFor low band energy properties, EfullFor full band energy properties, RlowA low frequency energy ratio property.
In the method described above, according to the petrophysical theory, when seismic waves propagate in a subsurface hydrocarbon-bearing thin reservoir, the energy of the low frequency band of the seismic waves is enhanced due to the fluid dissipation effect, the energy of the high frequency band is weakened, namely, the effect of low frequency resonance and high frequency attenuation is realized, and the total energy is basically kept unchanged. The low-frequency energy ratio attribute represents the proportion of the low-frequency band energy of the seismic waves in the total energy and reflects the hydrocarbon-bearing property of the reservoir, namely, when the reservoir contains hydrocarbons, a larger low-frequency band energy ratio attribute value can be obtained due to the low-frequency resonance effect, and when the reservoir does not contain hydrocarbons, the lower low-frequency band energy attribute value corresponds to the smaller low-frequency band energy attribute value. Based on the low-frequency energy attribute distribution rule and the drilled well calibration, the distribution range of the hydrocarbon-bearing reservoir favorable for the research work area can be obtained, and the purpose of detecting the thin reservoir hydrocarbon is achieved.
In another aspect, the present invention further provides a thin reservoir hydrocarbon detection device based on a super gather, where the thin reservoir hydrocarbon detection device based on a super gather includes:
the seismic data acquisition module is used for acquiring a post-stack seismic data volume of a research work area;
the horizon interpretation data acquisition module is used for acquiring top and bottom horizon interpretation data of a thin reservoir in a research work area;
the super-channel set seismic data volume construction module is used for constructing a super-channel set seismic data volume according to the acquired post-stack seismic data and the thin reservoir top and bottom layer interpretation data;
the frequency spectrum analysis module is used for carrying out frequency spectrum analysis on the super-channel set seismic data body to obtain a super-channel set seismic data amplitude spectrum;
the low-frequency energy ratio attribute calculation module is used for calculating to obtain a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set;
and the thin reservoir hydrocarbon detection module is used for detecting the thin reservoir hydrocarbons according to the low-frequency energy ratio attribute.
In the above apparatus, preferably, the range of the trace number of the post-stack seismic data volume plane in the research work area acquired by the seismic data acquisition module needs to include a research target area, and the time range of the vertical direction of the data volume needs to include a target reservoir.
In the apparatus described above, preferably, the horizon interpretation data acquisition module is specifically configured to: and carrying out seismic horizon interpretation work aiming at the target layer of the target research work area to obtain the top and bottom horizon interpretation data of the thin reservoir within the range of the target research work area.
In the apparatus described above, preferably, the super gather seismic data volume construction module is specifically configured to:
1) Intercepting seismic data of a thin reservoir section by using a thin reservoir top and bottom interpretation horizon;
2) Sequentially splicing each intercepted seismic data and four adjacent closest seismic data in series according to the sequence of south, east, west and north;
3) Replacing the seismic data of the original center channel by the seismic data obtained by serial splicing;
4) Splicing all data in the research work area according to the processing process shown in the step 1) to the step 3) to obtain a thin reservoir interval super gather seismic data volume.
In the apparatus described above, preferably, the super gather seismic data volume construction module further includes a tandem splicing unit, where the tandem splicing unit is specifically configured to: according to the following formula 1):
Sms＝{Sm,Se,Ss,Sw,Snequation 1);
in formula 1), SmsSeismic data volume of a gather of super-channels for a central channel in a thin reservoir interval, SmThe original seismic data volume, S, being a central trace in a thin reservoir intervale,Ss,Sw,SnOriginal seismic data bodies in the east direction, the south direction, the west direction and the north direction of a certain central channel of the thin reservoir section are respectively represented by { } representing that each channel set is sequentially connected in series for splicing processing operation.
In the above apparatus, preferably, the spectrum analysis module is specifically configured to:
performing spectrum analysis on the gather seismic data volume by using Fourier transform to obtain a gather seismic data amplitude spectrum shown in the following formula 2):
in formula 2), A (f) is the amplitude spectrum of the seismic data of the gather of the super channels, SmsThe seismic data volume is a super gather seismic data volume of a certain central channel of the thin reservoir section, j is an imaginary number unit, f is frequency, and is Hz, t is time, and is s.
In the above apparatus, preferably, the low frequency energy ratio attribute calculation module is specifically configured to:
respectively calculating a low-frequency band energy attribute, a full-frequency band energy attribute and a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set, wherein the calculation formulas are respectively shown in the following formula 3) to formula 5):
equation 3) -equation 5), where A (f) is the amplitude spectrum of the seismic data of the super channel gather, f is the frequency in Hz, and f islow1Is the starting frequency of the low frequency band in Hz, flow2Is a low band stop frequency in Hz, fhighIs the high band end frequency in Hz, ElowFor low band energy properties, EfullFor full band energy properties, RlowA low frequency energy ratio property.
In yet another aspect, the present invention also provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program realizes the steps of the thin reservoir hydrocarbon detection method based on a super gather.
In yet another aspect, the present invention further provides a computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the thin reservoir hydrocarbon detection method based on a super-gather described above.
The thin reservoir hydrocarbon detection method and device based on the super-gather firstly carry out splicing processing on seismic data in a thin reservoir time window range and adjacent nearest four-channel seismic data by using a super-gather series splicing technology to obtain a super-gather seismic data body for thin reservoir hydrocarbon detection, the data body has data sampling analysis points which are about 5 times of original seismic data, the stability of attribute analysis is guaranteed, the adjacent nearest four-channel seismic data are selected for splicing processing, the obtained super-gather well keeps the response characteristics of the original seismic data, the target consistency of attribute analysis is guaranteed, then the amplitude energy spectrum of the super-gather seismic data is calculated by using a spectrum analysis technology on the basis of the super-gather seismic data, the low-frequency energy ratio attribute is calculated on the basis of the amplitude energy spectrum of the super-gather data, and finally the thin reservoir hydrocarbon detection is carried out on the basis of the low-frequency energy ratio attribute to obtain favorable reservoir distribution rules. Compared with the conventional thin reservoir prediction technology, the method and the device provided by the invention overcome the problems of incomplete thin reservoir seismic response and insufficient data to be analyzed, improve the stability of reservoir prediction, reduce the ambiguity of attribute analysis and improve the accuracy of thin reservoir hydrocarbon detection.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a thin reservoir hydrocarbon detection method based on a super-gather according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a method for constructing seismic data of a gather in an embodiment of the invention.
Fig. 3 is a schematic diagram illustrating calculation of low frequency energy ratio attribute according to an embodiment of the present invention.
FIG. 4 is a diagram of a thin reservoir hydrocarbon detection prediction result based on a super gather in an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a thin reservoir hydrocarbon detection device based on a super-gather according to an embodiment of the present invention.
Detailed Description
In order to clearly understand the technical features, objects and advantages of the present invention, the following detailed description of the technical solutions of the present invention will be made with reference to the following specific examples, which should not be construed as limiting the implementable scope of the present invention.
Fig. 1 is a flowchart of a method for detecting hydrocarbons in a thin reservoir based on a super gather according to an embodiment of the present invention, and as can be seen from fig. 1, the method specifically includes the following steps:
s101: acquiring a post-stack seismic data volume of a research work area;
s102: acquiring interpretation data of top and bottom layers of a thin reservoir in a research work area;
s103: constructing a super-channel set seismic data volume according to the acquired post-stack seismic data and the thin reservoir top and bottom horizon interpretation data;
s104: carrying out spectrum analysis on the super-channel set seismic data volume to obtain a super-channel set seismic data amplitude spectrum;
s105: calculating to obtain a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set;
s106: and detecting the thin reservoir hydrocarbons according to the low-frequency energy ratio attribute.
In one embodiment, the range of trace numbers of the plane of the post-stack seismic data volume obtained in S101 includes a research target area, and the time range of the vertical direction of the data volume includes a target reservoir.
In one embodiment, S102: acquiring interpretation data of top and bottom layers of a thin reservoir in a research work area, wherein the interpretation data comprises the following steps: and carrying out seismic horizon interpretation work aiming at the target layer of the target area to obtain the top and bottom horizon interpretation data of the thin reservoir within the range of the research target area.
In one embodiment, S103: constructing a super gather seismic data volume according to the acquired post-stack seismic data and thin reservoir top-bottom horizon interpretation data, which comprises the following steps:
FIG. 2 is a schematic diagram of a super gather seismic data volume constructed based on post-stack seismic data and thin reservoir top-bottom horizons, showing the planar and vertical distribution characteristics of 5 seismic traces, where S ismIs the seismic data of the central channel and has S in the east, west, south and north directions respectivelye、Sw、SsAnd SnFirstly, intercepting thin reservoir section seismic data by using thin reservoir top and bottom explaining horizon, and then intercepting central channel seismic data SmAdjacent to it mostNear four traces of seismic data Se、Sw、SsAnd SnSequentially connecting in series according to the sequence of south, east and north to obtain the center track SmCorresponding gather-over-the-road seismic data SmsThen using the seismic data S of the super gathermsReplacement of original center-track seismic data SmAnd thus, a super gather seismic data construction work is completed, and similarly, all data in the research work area are sequentially spliced in series according to the process to obtain a thin reservoir section super gather seismic data volume in the whole research work area as shown in c in fig. 3. The data volume of each channel of the obtained super-gather seismic data is about 5 times of that of the original gather, so that the stability of an attribute analysis algorithm is ensured, and because the adjacent and nearest four channels of seismic data are spliced, and the plane spread range of an underground target body is far larger than the seismic data range of splicing, the seismic data of the same reservoir target is reflected by the seismic data of splicing, so that the finally obtained thin reservoir section super-gather seismic data not only meets the data volume requirement of attribute analysis, but also keeps the target consistency of seismic response.
The specific operation process is shown in the following formula 1):
Sms＝{Sm,Se,Ss,Sw,Snequation 1);
in formula 1), SmsSeismic data volume of a gather of super-channels for a central channel in a thin reservoir interval, SmIs the original seismic data volume, S, of a certain central channel of a thin reservoir intervale,Ss,Sw,SnOriginal seismic data bodies in the east direction, the south direction, the west direction and the north direction of a certain central channel of the thin reservoir section are respectively represented by { } representing that each channel set is sequentially connected in series for splicing processing operation.
In one embodiment, S104: carrying out spectrum analysis on the super-gather seismic data volume to obtain a super-gather seismic data amplitude spectrum, wherein the spectrum analysis comprises the following steps: performing frequency spectrum analysis on the seismic data volume of the super-gather by using conventional Fourier transform to obtain an amplitude spectrum of the seismic data of the super-gather, as shown in FIG. 3, the distribution condition of the amplitude spectrum of the seismic data of the super-gather is shown in FIG. 3, wherein the X axis is frequency, the Y axis is amplitude, and a black solid line is an amplitude energy envelope line;
the amplitude spectrum calculation formula of the seismic data of the super gather is shown in the following formula 2):
in formula 2), A (f) is the amplitude spectrum of the seismic data of the super channel set, SmsThe seismic data volume is a super channel set seismic data volume of a thin reservoir section, j is an imaginary number unit, f is frequency, the unit is Hz, t is time, and the unit is s.
In one embodiment, S105: calculating to obtain a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set, wherein the low-frequency energy ratio attribute comprises the following steps: respectively calculating low-frequency band energy attribute E according to amplitude spectrum of seismic data of super channel setlowFull band energy attribute EfullAnd a low frequency energy ratio property RlowThe calculation formulas are respectively shown in the following formula 3) to formula 5):
equation 3) -equation 5), where A (f) is the amplitude spectrum of the seismic data of the super channel gather, f is the frequency in Hz, and f islow1Is the starting frequency of the low frequency band in Hz, flow2Is a low band stop frequency in Hz, fhighThe high band end frequency is in Hz.
FIG. 3 shows the distribution of parameters during the calculation of the low band energy ratio property, f in FIG. 3low1Is the starting frequency of the low frequency band, i.e. the minimum frequency value corresponding to the amplitude energy of 0, flow2For a low frequency bandThe termination frequency, typically at the first inflection point of the amplitude energy envelope as the frequency increases, as shown by the position of the circle in FIG. 3, fhighThe high-band end frequency is the maximum frequency value corresponding to the amplitude energy of 0. In this embodiment, the values of the parameters are as follows:
flow1＝1.5Hz，flow2＝10Hz，fhigh＝95Hz。
Elowfor low band energy property values, the frequency is characterized from flow1To flow2The area of the region surrounded by the amplitude envelope and the frequency axis, i.e. the hatched region in FIG. 3 at flow2Area of left side, similarly, EfullIs the full-band energy property, i.e., the area of the entire shaded portion in fig. 3. R islowThe low-frequency energy ratio attribute is characterized in that the proportion of low-frequency band energy to full-frequency band energy is represented, when hydrocarbons are contained in a reservoir, the low-frequency energy attribute is increased due to the low-frequency resonance effect, the full-frequency band energy attribute is basically kept unchanged, and finally the low-frequency energy ratio attribute is increased.
In one embodiment, in S106, the low-frequency band energy of the seismic waves is enhanced and the high-frequency band energy is attenuated due to fluid dissipation effects while the overall energy remains substantially unchanged, i.e., the "low-frequency resonance, high-frequency attenuation" effect. The low-frequency energy ratio attribute represents the proportion of the low-frequency band energy of the seismic waves in the total energy and reflects the hydrocarbon-bearing property of the reservoir, namely when the reservoir contains hydrocarbons, a larger low-frequency band energy ratio attribute value can be obtained due to the low-frequency resonance effect, and when the reservoir does not contain hydrocarbons, the lower low-frequency band energy attribute value corresponds to the smaller low-frequency band energy attribute value. The distribution range of the reservoir which is beneficial to hydrocarbon bearing in the research work area can be obtained based on the low-frequency energy attribute distribution rule and the drilled well calibration, and the purpose of detecting the thin reservoir hydrocarbons is achieved.
Fig. 4 is a diagram showing a thin reservoir hydrocarbon detection prediction result based on a super-gather in this embodiment, the color depth in fig. 4 represents the size of the low-frequency energy ratio attribute, wherein a dark color region is a high value of the low-frequency energy ratio attribute, a light color region is a low value of the low-frequency energy ratio attribute, three drilled wells are in the range of a research work area, W-1 and W-3 are high-yield wells in a thin reservoir section and W-2 is a water well in the thin reservoir section according to drilling and test oil data, and it can be seen through comparative analysis that the actual drilling situation is well matched with the low-frequency energy ratio attribute. The distribution rule of the low-frequency energy ratio attribute is analyzed and compared with the drilled well calibration, and the result shows that the low-frequency energy ratio attribute extracted based on the seismic data of the super channel set can accurately depict the hydrocarbon-containing distribution range of the thin reservoir, and the effectiveness of the method provided by the invention is verified.
Based on the same inventive concept, the embodiment of the invention also provides a thin reservoir hydrocarbon detection device based on the super-channel set, and as the principle of solving the problems of the device is similar to the thin reservoir hydrocarbon detection method based on the super-channel set, the implementation of the device can refer to the implementation of the method, and repeated parts are not repeated. As used hereinafter, the term "module" or "unit" may implement a combination of software and/or hardware of predetermined functions. The means described in the embodiments below are preferably implemented in hardware, but implementations in software or a combination of software and hardware are also possible and contemplated.
Fig. 5 is a schematic structural diagram of a thin reservoir hydrocarbon detection device based on a super-gather according to an embodiment of the present invention, and as shown in fig. 5, the thin reservoir hydrocarbon detection device based on the super-gather includes:
the seismic data acquisition module 101 is used for acquiring a post-stack seismic data volume of a research work area;
the horizon interpretation data acquisition module 102 is used for acquiring top and bottom horizon interpretation data of a thin reservoir in a research work area;
the super-channel set seismic data volume construction module 103 is used for constructing a super-channel set seismic data volume according to the acquired post-stack seismic data and the thin reservoir top-bottom layer interpretation data;
the spectrum analysis module 104 is used for performing spectrum analysis on the super-gather seismic data volume to obtain a super-gather seismic data amplitude spectrum;
the low-frequency energy ratio attribute calculation module 105 is used for calculating the low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set;
and the thin reservoir hydrocarbon detection module 106 is used for detecting the thin reservoir hydrocarbons according to the low frequency energy ratio attribute.
In an embodiment, the range of the line number of the post-stack seismic data volume plane of the research work area acquired by the seismic data acquisition module 101 needs to include a research target area, and the time range of the vertical direction of the data volume needs to include a target reservoir.
In an embodiment, the horizon interpretation data obtaining module 102 is specifically configured to: and carrying out seismic horizon interpretation work aiming at the target layer of the target research work area to obtain thin reservoir top and bottom horizon interpretation data in the target research work area range.
In an embodiment, the super gather seismic data volume construction module 103 is specifically configured to:
1) Intercepting seismic data of a thin reservoir section by using a thin reservoir top and bottom interpretation horizon;
2) Sequentially splicing each intercepted seismic data and four adjacent closest seismic data in series according to the sequence of south, east, west and north;
3) Replacing the seismic data of the original center channel by the seismic data obtained by serial splicing;
4) Splicing all data in the research work area according to the processing procedures shown in the steps 1) to 3) to obtain a thin reservoir interval super gather seismic data volume.
In an embodiment, the super gather seismic data volume construction module 103 further includes a series splicing unit, where the series splicing unit is specifically configured to: according to the following formula 1):
Sms＝{Sm,Se,Ss,Sw,Snequation 1);
in formula 1), SmsSeismic data volume of a gather of super-channels for a central channel in a thin reservoir interval, SmThe original seismic data volume, S, being a central trace in a thin reservoir intervale,Ss,Sw,SnOriginal seismic data bodies in the east direction, the south direction, the west direction and the north direction of a certain central channel of the thin reservoir section are respectively represented by { } representing that each channel set is sequentially connected in series for splicing processing operation.
In an embodiment, the spectrum analysis module 104 is specifically configured to:
performing spectrum analysis on the super-gather seismic data volume by utilizing Fourier transform to obtain a super-gather seismic data amplitude spectrum shown in the following formula 2):
in formula 2), A (f) is the amplitude spectrum of the seismic data of the super channel set, SmsThe seismic data volume is a super channel set seismic data volume of a certain central channel of the thin reservoir section, j is an imaginary number unit, f is frequency and is Hz, t is time and is s.
In the above apparatus, preferably, the low frequency energy ratio attribute calculation module 105 is specifically configured to:
respectively calculating low-frequency band energy attribute, full-frequency band energy attribute and low-frequency energy ratio attribute according to the seismic data amplitude spectrum of the super channel set, wherein the calculation formulas are respectively shown in the following formulas 3) to 5):
equation 3) -equation 5), A (f) is the amplitude spectrum of the seismic data of the super channel set, f is frequency and has the unit of Hz, flow1Is the starting frequency of the low frequency band in Hz, flow2Is a low band stop frequency in Hz, fhighIs the high band end frequency in Hz, ElowFor low-band energy properties, EfullFor full band energy properties, RlowIs a low frequency energy ratio property.
The thin reservoir hydrocarbon detection method and device based on the super-gather provided by the embodiment of the invention firstly carry out splicing processing on seismic data in a thin reservoir time window range and four adjacent nearest seismic data by using a super-gather series splicing technology to obtain a super-gather seismic data body for thin reservoir hydrocarbon detection, wherein the data body has data sampling analysis points which are about 5 times of original seismic data, the stability of attribute analysis is ensured, the obtained super-gather well keeps the response characteristics of the original seismic data due to the fact that the four adjacent nearest seismic data are selected for splicing processing, the target consistency of attribute analysis is ensured, then the amplitude energy spectrum of the super-gather seismic data is calculated on the basis of the super-gather seismic data by using a spectrum analysis technology, the low-frequency energy ratio attribute is calculated on the basis of the amplitude energy spectrum of the super-gather data, and finally the thin reservoir hydrocarbon detection is carried out on the basis of the low-frequency energy ratio attribute to obtain the favorable reservoir distribution rule. Compared with the conventional thin reservoir prediction technology, the method and the device provided by the embodiment of the invention overcome the problems of incomplete thin reservoir seismic response and insufficient data to be analyzed, improve the stability of reservoir prediction, reduce the ambiguity of attribute analysis and improve the accuracy of thin reservoir hydrocarbon detection.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only exemplary of the invention and should not be taken as limiting the scope of the invention, so that the invention is intended to cover all modifications and equivalents of the embodiments described herein. In addition, the technical features and the technical inventions of the present invention, the technical features and the technical inventions, and the technical inventions can be freely combined and used.
Claims (14)
1. A thin reservoir hydrocarbon detection method based on a super-gather is characterized by comprising the following steps:
acquiring a post-stack seismic data volume of a research work area;
acquiring interpretation data of top and bottom layers of a thin reservoir in a research work area;
constructing a super-gather seismic data volume according to the acquired post-stack seismic data and the thin reservoir top and bottom horizon interpretation data, wherein the super-gather seismic data volume comprises the following steps:
1) Intercepting seismic data of a thin reservoir section by using a thin reservoir top and bottom interpretation horizon;
2) Sequentially splicing each intercepted seismic data and four adjacent nearest seismic data in series according to the sequence of the south, the west and the north;
3) Replacing the seismic data of the original center channel by the seismic data obtained by serial splicing;
4) Splicing all data in the research work area according to the processing processes shown in the steps 1) to 3) to obtain a thin reservoir section super gather seismic data volume;
carrying out spectrum analysis on the super-channel set seismic data volume to obtain a super-channel set seismic data amplitude spectrum;
calculating to obtain a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set;
and detecting the thin reservoir hydrocarbons according to the low-frequency energy ratio attribute.
2. The method of claim 1, wherein the obtained range of trace numbers for the plane of the post-stack seismic data volume at the research work area is required to contain a research target area, and the time range of the vertical direction of the data volume is required to contain a target reservoir.
3. The method of claim 1, wherein obtaining top-bottom horizon interpretation data for thin reservoir layers of a research work area comprises: and carrying out seismic horizon interpretation work aiming at the target layer of the target research work area to obtain the top and bottom horizon interpretation data of the thin reservoir within the range of the target research work area.
4. The method according to claim 1, wherein the tandem splicing is performed according to the following equation 1):
Sms＝{Sm,Se,Ss,Sw,Snformula 1);
in formula 1), SmsSeismic data volume of a gather of super-channels for a central channel in a thin reservoir interval, SmIs the original seismic data volume, S, of a certain central channel of a thin reservoir intervale,Ss,Sw,SnAnd (4) respectively representing original seismic data bodies of a central channel of the thin reservoir section in the east, south, west and north directions, wherein the { } represents that each channel set is sequentially spliced in series in sequence for processing and operation.
5. The method of claim 1, wherein performing spectral analysis on the volume of the gather seismic data to obtain a gather seismic data amplitude spectrum comprises:
performing spectrum analysis on the super-gather seismic data volume by utilizing Fourier transform to obtain a super-gather seismic data amplitude spectrum shown in the following formula 2):
in formula 2), A (f) is the amplitude spectrum of the seismic data of the super channel set, SmsThe seismic data volume is a super channel set seismic data volume of a certain central channel of the thin reservoir section, j is an imaginary number unit, f is frequency and is Hz, t is time and is s.
6. The method of claim 1, wherein the calculating a low frequency energy ratio attribute from the amplitude spectra of the gather seismic data comprises:
respectively calculating a low-frequency band energy attribute, a full-frequency band energy attribute and a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set, wherein the calculation formulas are respectively shown in the following formula 3) to formula 5):
equation 3) -equation 5), where A (f) is the amplitude spectrum of the seismic data of the super channel gather, f is the frequency in Hz, and f islow1Is the starting frequency of the low frequency band in Hz, flow2Is a low band stop frequency in Hz, fhighIs the high band end frequency in Hz, ElowFor low-band energy properties, EfullFor full band energy properties, RlowIs a low frequency energy ratio property.
7. A thin reservoir hydrocarbon testing device based on a super-gather, comprising:
the seismic data acquisition module is used for acquiring a post-stack seismic data volume of a research work area;
the horizon interpretation data acquisition module is used for acquiring the top and bottom horizon interpretation data of the thin reservoir in the research work area;
the super-channel set seismic data volume construction module is used for constructing a super-channel set seismic data volume according to the acquired post-stack seismic data and the thin reservoir top and bottom layer interpretation data; the super gather seismic data volume construction module is specifically used for:
1) Intercepting seismic data of a thin reservoir section by using a thin reservoir top and bottom interpretation horizon;
2) Sequentially splicing each intercepted seismic data and four adjacent closest seismic data in series according to the sequence of south, east, west and north;
3) Replacing the seismic data of the original center channel by the seismic data obtained by serial splicing;
4) Splicing all data in the research work area according to the processing processes shown in the steps 1) to 3) to obtain a thin reservoir section super gather seismic data volume;
the frequency spectrum analysis module is used for carrying out frequency spectrum analysis on the super-channel set seismic data body to obtain a super-channel set seismic data amplitude spectrum;
the low-frequency energy ratio attribute calculation module is used for calculating to obtain a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set;
and the thin reservoir hydrocarbon detection module is used for detecting the thin reservoir hydrocarbons according to the low-frequency energy ratio attribute.
8. The apparatus of claim 7, wherein the range of trace numbers of the plane of the post-stack seismic data volume of the research work area acquired by the seismic data acquisition module is required to include a research target area, and the time range of the vertical direction of the data volume is required to include a target reservoir.
9. The apparatus of claim 7, wherein the horizon interpretation data acquisition module is specifically configured to: and carrying out seismic horizon interpretation work aiming at the target layer of the target research work area to obtain the top and bottom horizon interpretation data of the thin reservoir within the range of the target research work area.
10. The apparatus of claim 7, wherein the gather-superchannel seismic data volume construction module further comprises a tandem splicing unit, the tandem splicing unit being specifically configured to: according to the following formula 1):
Sms＝{Sm,Se,Ss,Sw,Snformula 1);
in formula 1), SmsSeismic data volume of a gather of super-channels for a central channel of a thin reservoir section, SmIs the original seismic data volume, S, of a certain central channel of a thin reservoir intervale,Ss,Sw,SnAnd (4) respectively representing original seismic data bodies of a central channel of the thin reservoir section in the east, south, west and north directions, wherein the { } represents that each channel set is sequentially spliced in series in sequence for processing and operation.
11. The apparatus of claim 7, wherein the spectrum analysis module is specifically configured to:
performing spectrum analysis on the super-gather seismic data volume by utilizing Fourier transform to obtain a super-gather seismic data amplitude spectrum shown in the following formula 2):
in formula 2), A (f) is the amplitude spectrum of the seismic data of the gather of the super channels, SmsThe seismic data volume is a super channel set seismic data volume of a certain central channel of the thin reservoir section, j is an imaginary number unit, f is frequency and is Hz, t is time and is s.
12. The apparatus of claim 7, wherein the low frequency energy ratio attribute calculation module is specifically configured to:
respectively calculating a low-frequency band energy attribute, a full-frequency band energy attribute and a low-frequency energy ratio attribute according to the amplitude spectrum of the seismic data of the super channel set, wherein the calculation formulas are respectively shown in the following formula 3) to formula 5):
equation 3) -equation 5), A (f) is the amplitude spectrum of the seismic data of the super channel set, f is frequency and has the unit of Hz, flow1Is the starting frequency of the low frequency band in Hz, flow2Is a low band stop frequency in Hz, fhighIs the high band end frequency in Hz, ElowFor low band energy properties, EfullFor full band energy properties, RlowIs a low frequency energy ratio property.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of the thin superset-based reservoir hydrocarbon detection method of any one of claims 1-6.
14. A computer readable storage medium having stored thereon a computer program for implementing the steps of the thin reservoir hydrocarbon detection method based on a gather of super-traces according to any one of claims 1-6 when executed by a processor.
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