CN110609322A - Seismic interpretation and reservoir description method based on music attributes - Google Patents
Seismic interpretation and reservoir description method based on music attributes Download PDFInfo
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- CN110609322A CN110609322A CN201910902950.6A CN201910902950A CN110609322A CN 110609322 A CN110609322 A CN 110609322A CN 201910902950 A CN201910902950 A CN 201910902950A CN 110609322 A CN110609322 A CN 110609322A
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- 238000001228 spectrum Methods 0.000 claims description 23
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- 230000009466 transformation Effects 0.000 claims description 7
- 238000003909 pattern recognition Methods 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 238000012417 linear regression Methods 0.000 claims description 3
- 238000012567 pattern recognition method Methods 0.000 claims description 3
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- 238000004364 calculation method Methods 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/282—Application of seismic models, synthetic seismograms
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/32—Transforming one recording into another or one representation into another
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Abstract
The invention discloses a seismic interpretation and reservoir description method based on music attributes, which belongs to the field of seismic data interpretation, and is characterized in that a frequency division method is used for carrying out target processing on seismic data, converting the seismic data into note data, and writing the note data into an audio file, so that the reservoir can be distinguished by hearing, the reservoir can be more easily found by combining the traditional seismic profile and horizontal slice interpretation, extraction and interpretation of other music attributes can be carried out on the basis, and a new method and a new way are provided for seismic data interpretation; the method is mainly used for seismic data target processing and reservoir identification, and forms a new seismic data interpretation method.
Description
Technical Field
The invention relates to the field of seismic data interpretation, in particular to a seismic interpretation and reservoir description method based on music attributes.
Background
The time-frequency analysis method is a very common method for seismic interpretation. The frequency of the frequency domain is varied at regular intervals, ranging from several hertz to several tens of hertz, in the conventional method. Because the real frequency is very low, we can only see, such as looking at the frequency spectrum, making frequency division slices, etc., but cannot listen. Various wonderful music utilizes audio frequency, whether the seismic signal can be converted into the audio frequency through certain mapping processing or not can be used for listening to oil and gas reservoirs, and the music is interesting and meaningful work. A standard piano consists of 88 keys (52 white keys, 36 black keys), ranging in register from a0(27.5Hz) to C8(4186Hz), for a total of 38 notes (notes), and can play a wonderful and exciting tune. As mentioned above, the seismic signal is a band-limited signal with an effective frequency band from several hertz to several tens of hertz in terms of frequency domain, and can be converted into a note through some transformation, and the note can be played and identified through a certain music player, and music attributes such as Pitch (Pitch), dynamics (Velocity), and single-tone time domain features (Duration) can be obtained through related calculation, and on the basis, target processing such as other music attribute extraction, clustering, pattern recognition and the like and reservoir fine description work can also be performed. If the related concept and the representation method of the musics can be applied to the field of seismic interpretation, the method is an innovation, and no related report of the technology exists in China at present.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provides a seismic interpretation and reservoir description method based on music attributes.
In order to achieve the purpose, the invention is implemented according to the following technical scheme:
a seismic interpretation and reservoir description method based on music attributes comprises the following steps:
s1, inputting the line number NL, the track number NTR and the point number NP of the seismic three-dimensional data body;
s2, inputting a starting time t1 and a terminating time t 2;
s3, given an effective low cutoff frequency f 0;
s4, inputting a phonetic symbol p to be analyzed;
s5, judging whether the line number IL of the seismic three-dimensional data body is larger than the line number NL:
if the determination result is negative, go to step S6;
or if yes, go to step S12;
s6, judging whether the track number ITR of the seismic three-dimensional data volume is greater than the track number NTR;
if the determination result is negative, go to step S7;
or if yes, returning to step S5;
s7, inputting the IL line and the ITR track data;
s8, establishing a P-f transformation relation according to the formula (1);
wherein f is frequency, f0For low cut-off frequency, p is a tone symbol;
s9, judging whether the relation t1 is more than or equal to tau is less than or equal to t 2:
if the judgment result is no, returning to the step S6;
or if yes, go to step S10;
s10, calculating a time frequency spectrum according to the formula (2);
wherein t1 and t2 are analysis time window ranges; tau and f respectively represent time and frequency coordinates of time-frequency analysis, and t is a time integral variable; ST represents S transformation;
s11, converting to a voice score, and then performing steps S9 and S12;
s12, respectively carrying out listening identification, monophonic note body interpretation, music attribute extraction, clustering and pattern recognition, and then executing the step S13;
and S13, reservoir comprehensive analysis.
Further, in step S12, the music spectrum data obtained during the listening authentication is written into a MIDI or WAV file.
Further, in step S12, the monophonic character interpretation includes a single-pass analysis, a slice analysis, and a profile analysis, which are used to extract basic musical properties including tone, force, and monophonic time-domain features.
Further, in step S12, the attributes extracted in the music attribute extraction process include a mean, a standard deviation, and an information entropy.
Further, in step S12, the clustering and pattern recognition methods include a principal factor method, K-center clustering, and a multiple linear regression method.
Further, in step S13, the reservoir comprehensive analysis specifically includes: the stratum characteristic change is distinguished by listening to sound to obtain reservoir information, and the reservoir is found by combining the traditional seismic section and horizontal slice interpretation.
Compared with the prior art, the method uses a frequency division method to perform target processing on the seismic data, converts the seismic data into the note data, and writes the note data into the audio file, so that the reservoir can be distinguished by hearing, the reservoir can be more easily found by combining the traditional seismic profile and horizontal slice interpretation, extraction and interpretation of other music attributes can be performed on the basis, and a new method and a new way are provided for seismic data interpretation; the method is mainly used for seismic data target processing and reservoir identification, and forms a new seismic data interpretation method.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a single-pass theoretical model and its music spectrogram of the present invention: (a) is a single seismic channel theoretical model diagram; (b) is a calculated score (Piano Roll) graph.
FIG. 3 is a single-section test chart of the wedge: (a) is a wedge-shaped body model diagram; (b) is a cross-sectional view of the wedge.
FIG. 4 is a comparison S (x) of the spectra (Piano roll) of different tracksfixedτ, p) diagram: (a) comparing the 101 st channel signal with the music spectrum; (b) comparing the 51 st channel signal with the music spectrum; (c) the 11 th channel signal is compared with the music spectrum.
FIG. 5 is a comparison S (x, τ, p) of different pitch (pitch) profilesfixed) The following drawings: (a) is a bass section view; (b) a high pitch profile.
FIG. 6 is a p-x spectrum S (x, τ) at fixed timefixedP) diagram: (a) is a section view of a wedge-shaped model; (b) the music spectrogram is T130 ms.
FIG. 7 is a diagram of practical data application in a certain work area: (a) the original section of the WG9 cross-well log is obtained; (b) is analyzed by WG9 crosslog note spectrum.
Fig. 8 shows horizontal slices T0 ═ 2180 for different phonetic symbols: (a) is an original slice image; (b) a horizontal slice when P is 5; (c) a horizontal slice when P is 10; (d) the horizontal section is shown as P20.
Fig. 9 is a cross-royal 9 well edge music property slice and an enlarged view of a target area: (a) the slice p is 10 for 9-well boundary music attribute of the royal jelly; (b) the target area was sliced up in an enlarged view for the 9-well border music property of the royal jelly.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. The specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
As shown in fig. 1, the present embodiment provides a seismic interpretation and reservoir description method based on music attributes, which includes the following steps:
s1, inputting the line number NL, the track number NTR and the point number NP of the seismic three-dimensional data body;
s2, inputting a starting time t1 and a terminating time t 2;
s3, given an effective low cutoff frequency f 0;
s4, inputting a phonetic symbol p to be analyzed;
s5, judging whether the line number IL of the seismic three-dimensional data body is larger than the line number NL:
if the determination result is negative, go to step S6;
or if yes, go to step S12;
s6, judging whether the track number ITR of the seismic three-dimensional data volume is greater than the track number NTR;
if the determination result is negative, go to step S7;
or if yes, returning to step S5;
s7, inputting the IL line and the ITR track data;
s8, establishing a P-f transformation relation according to the formula (1);
wherein f is frequency, f0For low cut-off frequency, p is a tone symbol;
s9, judging whether the relation t1 is more than or equal to tau is less than or equal to t 2:
if the judgment result is no, returning to the step S6;
or if yes, go to step S10;
s10, calculating a time frequency spectrum according to the formula (2);
wherein t1 and t2 are analysis time window ranges; tau and f respectively represent time and frequency coordinates of time-frequency analysis, and t is a time integral variable; ST represents S transformation;
s11, converting to a voice score, and then performing steps S9 and S12;
s12, respectively carrying out listening identification, monophonic note body interpretation, music attribute extraction, clustering and pattern recognition, and then executing the step S13; in actual operation, the music spectrum data obtained during listening and identification is written into a MIDI or WAV file; the monophonic note body interpretation comprises single-channel analysis, slice analysis and section analysis and is used for extracting basic music attributes including tone, dynamics and monophonic time domain characteristics; the attributes extracted in the music attribute extraction process comprise a mean value, a standard deviation and an information entropy; the clustering and pattern recognition method comprises a main factor method, a K central point clustering and a multiple linear regression method.
S13, reservoir comprehensive analysis, specifically: the stratum characteristic change is distinguished by listening to sound to obtain reservoir information, and the reservoir is found by combining the traditional seismic section and horizontal slice interpretation.
To further verify the feasibility of the invention, the invention was applied to a thin interbed oil region in the victory exploration area. Theoretical research is firstly carried out, and corresponding software modules are developed. FIG. 2 is a single pass theoretical model of the present invention and its music spectrogram; wherein, the graph (a) is a single seismic channel theoretical model graph, the 100ms is the single interface reflection of a Rake wavelet with the wavelet of 35Hz, the middle is the thin layer reflection with the thickness of 35Hz and 10ms, and the lower part is the thin layer reflection with the same thickness and the wavelet dominant frequency of 25 Hz; the plot (b) is a calculated score of the music (Piano Roll) and it can be seen that there are three energy blobs on the music spectrum, the first of which is higher dominant but less energetic, the second is similar but more energetic, and the third is lower dominant and matches the time domain features. FIG. 3(a) is a wedge geomodel designed specifically; the wavelet convolution calculation is performed on FIG. 3(a) to obtain a corresponding synthetic seismic section (FIG. 3 (b)). Wherein, the upper layer has a strong reflection interface with a dominant frequency of 35Hz, the thin layer has a reflection time of 10ms, a wedge is arranged below the strong reflection interface, and the dominant frequency is 25 Hz. Fig. 4(a) (b) (c) are the extracted seismic traces and the corresponding calculated acoustic scores, where: the graph (a) is a comparison graph of the signal of the 101 th channel and the music spectrum, the channel is positioned at the thicker part of the wedge body, and the music spectrum of two effective signals can be seen on the calculated music score without overlapping and can be directly distinguished. The graph (b) is a comparison of the 51 st trace signal with the music score, the trace being located in the middle of the wedge, with some overlap in the score, but some discrimination of the energy mass can be seen. The diagram (c) is a comparison diagram of the 11 th signal and the music spectrum, the signal is located in the front of the wedge, the strong reflection is very close to the reflection of the wedge, and the spectral energy masses of the two effective signals can be seen to be coupled together and cannot be distinguished. And converting the extracted seismic channels into time frequency spectrums by using S conversion, and converting the time frequency spectrums into note frequency spectrums. Fig. 5(a) is a cross section obtained by extracting p-19 in the score, which is a bass cross section, and it can be seen that bass energy is strong. Fig. 5(b) is a cross section obtained by extracting a portion p-26 of the music book, which is a high-pitched cross section, and it can be seen that high-pitched energy is strong. FIG. 6(a) is a seismic section calculated by a wedge model, and the section is transformed into a time spectrum by S transform and then transformed into a note spectrum, to obtain FIG. 6 (b). It can be seen that the energy of the wedge model is concentrated between p 15-45 and there are three distinct band features. FIG. 7(a) shows the actual work area and the well location and log curves are plotted, and FIG. 7(b) shows the note spectrum analysis of the WG9 well traverse line. The music spectrum at the favorable reservoir can be seen to be distributed in a blob. Fig. 8(a) shows an original slice with T0 2180 extracted from the work area, and fig. 8(b) shows a music attribute seismic slice with p 5 calculated using fig. 8 (a). It can be seen that the reservoir characteristics are not apparent on the music attribute seismic slice with p-5. Fig. 8(c) is a music attribute seismic slice calculated using p-10 in fig. 8(a), and it can be seen that the reservoir features are apparent on the music attribute seismic slice with p-10. Fig. 8(d) shows a music attribute seismic section with p equal to 20 calculated using fig. 8(a), and it can be seen that the fan feature is obvious on the music attribute seismic section with p equal to 20. Fig. 9(a) is a music attribute slice of the 9-well boundary layer of the queen-bee extracted from the work area, and fig. 9(b) is a partial enlarged view of the target area in fig. 9(a), and it can be seen from the figure that the favorable block characteristics of the reservoir are obvious and the music attribute effect is good.
In summary, the invention uses the frequency division method to perform target processing on the seismic data, converts the seismic data into the note data, and writes the note data into the audio file, so that the reservoir can be distinguished by hearing, the reservoir can be found more easily by combining the traditional seismic profile and horizontal slice interpretation, and the extraction and interpretation of other music attributes can be carried out on the basis, thereby providing a new method and approach for seismic data interpretation.
The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.
Claims (6)
1. A seismic interpretation and reservoir description method based on music attributes is characterized in that: the method comprises the following steps:
s1, inputting the line number NL, the track number NTR and the point number NP of the seismic three-dimensional data body;
s2, inputting a starting time t1 and a terminating time t 2;
s3, given an effective low cutoff frequency f 0;
s4, inputting a phonetic symbol p to be analyzed;
s5, judging whether the line number IL of the seismic three-dimensional data body is larger than the line number NL:
if the determination result is negative, go to step S6;
or if yes, go to step S12;
s6, judging whether the track number ITR of the seismic three-dimensional data volume is greater than the track number NTR;
if the determination result is negative, go to step S7;
or if yes, returning to step S5;
s7, inputting the IL line and the ITR track data;
s8, establishing a P-f transformation relation according to the formula (1);
wherein f is frequency, f0For low cut-off frequency, p is a tone symbol;
s9, judging whether the relation t1 is more than or equal to tau is less than or equal to t 2:
if the judgment result is no, returning to the step S6;
or if yes, go to step S10;
s10, calculating a time frequency spectrum according to the formula (2);
wherein t1 and t2 are analysis time window ranges; tau and f respectively represent time and frequency coordinates of time-frequency analysis, and t is a time integral variable; ST represents S transformation;
s11, converting to a voice score, and then performing steps S9 and S12;
s12, respectively carrying out listening identification, monophonic note body interpretation, music attribute extraction, clustering and pattern recognition, and then executing the step S13;
and S13, reservoir comprehensive analysis.
2. The method of claim 1 for seismic interpretation and reservoir description based on musical attributes, wherein: in step S12, the music spectrum data obtained during the listening authentication is written into a MIDI or WAV file.
3. The method of claim 1 for seismic interpretation and reservoir description based on musical attributes, wherein: in step S12, the single-tone symbol interpretation includes single-channel analysis, slice analysis, and profile analysis, and is used to extract basic music attributes including tone, force, and time-domain characteristics of single tone.
4. The method of claim 1 for seismic interpretation and reservoir description based on musical attributes, wherein: in step S12, the attributes extracted in the music attribute extraction process include a mean, a standard deviation, and an information entropy.
5. The method of claim 1 for seismic interpretation and reservoir description based on musical attributes, wherein: in step S12, the clustering and pattern recognition methods include a principal factor method, K-center clustering, and a multiple linear regression method.
6. The method of claim 2 for seismic interpretation and reservoir description based on musical attributes, wherein: in the step S13, the reservoir comprehensive analysis specifically includes: the stratum characteristic change is distinguished by listening to sound to obtain reservoir information, and the reservoir is found by combining the traditional seismic section and horizontal slice interpretation.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102809762A (en) * | 2012-08-13 | 2012-12-05 | 成都理工大学 | Reservoir imaging technique based on full-frequency-band seismic information mining |
| US20180136350A1 (en) * | 2015-04-07 | 2018-05-17 | Eni S.P.A. | Method and system for the multimodal and multiscale analysis of geophysical data by transformation into musical attributes |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102809762A (en) * | 2012-08-13 | 2012-12-05 | 成都理工大学 | Reservoir imaging technique based on full-frequency-band seismic information mining |
| US20180136350A1 (en) * | 2015-04-07 | 2018-05-17 | Eni S.P.A. | Method and system for the multimodal and multiscale analysis of geophysical data by transformation into musical attributes |
Non-Patent Citations (1)
| Title |
|---|
| P. DELL’AVERSANA ET AL.: "Application of Machine Learning and Digital Music Technology to distinguish high from low gas-saturated reservoirs" * |
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