CN115857015B - Method for quantitatively predicting distribution of tuff in volcanic stratum - Google Patents
Method for quantitatively predicting distribution of tuff in volcanic stratum Download PDFInfo
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Abstract
The invention discloses a method for quantitatively predicting the distribution of tuff in volcanic strata, which relates to the technical field of quantitative prediction of the distribution of tuff and comprises the following steps: s1, analyzing logging response characteristics of different lithologic volcanic rocks in a drilled volcanic stratum, and searching or constructing a characterization curve of the tuff; s2, performing seismic attribute calculation such as 90-degree phase shift, relative wave impedance and the like by utilizing three-dimensional seismic data; s3, extracting correlation between the well side channel seismic attribute and the tuff characterization curve; s4, performing error test according to the attribute ranked at the front to obtain first n seismic attributes with the minimum error; and S5, performing deep learning according to the first n seismic attributes with the minimum error to obtain the tuff characterization three-dimensional data volume. By means of the method, the method for quantitatively predicting the space distribution of the tuff by utilizing the three-dimensional seismic data can accurately predict the space distribution range of the tuff in the volcanic stratum.
Description
Technical Field
The invention relates to the technical field of quantitative prediction of the distribution of tuff, in particular to a method for quantitatively predicting the distribution of tuff in volcanic strata.
Background
The tuff is one of the tuff, is the transition lithology between volcanic and sedimentary, and is formed under the dual effects of volcanic activity and transformation, and research has shown that the tuff has poor storage effect, and the elastic parameter characteristics such as speed, density and the like are very similar to those of the volcanic limestone reservoir with good storage conditions, so that the understanding of the volcanic reservoir is greatly disturbed. Because the tuff is widely distributed in the volcanic basin, the spatial distribution of the tuff in the volcanic stratum is predicted, and the method is important for researching the volcanic reservoir and reducing the polycrystallinity of the earthquake prediction reservoir.
The current research situation at home and abroad is studied, and related experiments and research progress are carried out on the pore structure characteristics and the fluidity characteristics of the tuff. In the aspect of geophysical logging, the lithology and electrical logging curve characteristic of the tuff are also deeply researched, and more related identification methods based on logging curve intersection plates, machine learning, neural networks and the like exist. However, the current research on the tuff is limited to lithology identification and log identification, and the research on quantitative spatial prediction of the tuff in volcanic formations is lacking.
A method for quantitatively predicting the distribution of tuff in a volcanic formation is therefore proposed to solve the above-mentioned problems.
Disclosure of Invention
The invention aims to provide a method for quantitatively predicting the distribution of tuff in volcanic strata, which aims to solve the technical problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for quantitatively predicting the distribution of tuff in a volcanic formation, comprising the steps of:
s1, analyzing logging response characteristics of different lithologic volcanic rocks in a drilled volcanic stratum, and searching or constructing a characterization curve of the tuff;
s2, performing seismic attribute calculation such as 90-degree phase shift, relative wave impedance and the like by utilizing three-dimensional seismic data to obtain various seismic attribute data volumes;
s3, extracting a correlation between the well side channel seismic attribute and the tuff characterization curve, and sorting according to the correlation;
s4, performing error test according to the attribute ranked at the front to obtain first n seismic attributes with the minimum error;
and S5, performing deep learning according to the first n seismic attributes with the minimum error to obtain the tuff characterization three-dimensional data volume.
And S6, analyzing the space position of the tuff on the tuff three-dimensional data body according to the value range of the tuff characterization curve, and extracting the plane distribution map of the tuff according to the top and bottom or the secondary top and bottom of the volcanic mechanism.
Furthermore, the tuff is characterized by low resistivity on the logging curve, and gamma values are distributed in a certain interval but lower than the volcanic lava, so that a lithology-distinguishing curve is constructed, and a lithology indicator is constructed according to the following formula:
wherein GRJ is a curve after GR of a research area is subjected to area consistency correction, and LLD is a resistivity logging curve; b is the threshold value of gamma curve division tuff and lava in the region, and a is the threshold value of resistivity curve division tuff and tuff in the region.
Further, in step S5, a nonlinear relationship is established between the characterization curve of the tuff and the first 3 seismic attributes with the smallest error, and deep learning is performed by using the nonlinear relationship, so as to obtain a three-dimensional data volume representing the tuff.
Advantageous effects
According to the method, the correlation between the characteristics of the well-drilled tuff and the well side channel seismic attribute is established, and the deep learning is applied to the whole seismic attribute body calculated by three-dimensional seismic data, so that the three-dimensional data body representing the tuff is obtained.
The invention provides a method for quantitatively predicting the spatial distribution of tuff by utilizing three-dimensional seismic data, which can accurately predict the spatial distribution range of tuff in volcanic strata.
The method has the characteristics of seismic attribute determination, quick realization and accurate identification, is applied to prediction of the space distribution of the tuff in the Songnan area, and has proved to be effective.
According to the method, the relation between the seismic attribute and the lithology characterization curve of the tuff is established for deep learning, the lithology on the well is extrapolated and predicted under the transverse variation trend of the earthquake, the extrapolated and predicted method accords with the transverse variation rule of lithology combination on the seismic reflection and the attribute thereof, and meanwhile, the transverse variation rule of the volcanic can be reflected relatively accurately.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow chart of a method of quantitatively predicting a distribution of tuff in a volcanic formation.
FIG. 2 is a gamma resistivity intersection.
FIG. 3 is a graph showing characterization of the resulting tuff.
Fig. 4 is a plan view distribution diagram of the tuff.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Example 1
The embodiment provides a method for quantitatively predicting the distribution of tuff in volcanic formations, referring to fig. 1, comprising the following steps:
s1, analyzing logging response characteristics of different lithologic volcanic rocks in a drilled volcanic stratum, and searching or constructing a characterization curve of the tuff;
the tuff is characterized by low resistivity on the logging curve, and gamma values are distributed in a certain interval but lower than volcanic lava, so that a lithology distinguishing curve is constructed, and a lithology indicating factor is constructed according to the following formula:
wherein GRJ is a curve after GR of a research area is subjected to area consistency correction, and LLD is a resistivity logging curve; b is the threshold value of gamma curve dividing the tuff and lava in the area, a is the threshold value of resistivity curve dividing the tuff and lava in the area;
FIG. 2 is a graph of gamma resistivity intersection, which shows that the gamma value is tuff when the resistivity is large; when the resistivity is small and the gamma is small, the volcanic lava is formed; when the resistivity is large and the gamma value is small, the volcanic lava is mainly used; when the resistivity is small and the gamma value is large, the material is mainly tuff;
FIG. 3 is a graph of characterization of the as-constructed tuff, with low values (black parts) that distinguish the tuff;
s2, performing seismic attribute calculation such as 90-degree phase shift, relative wave impedance and the like by utilizing three-dimensional seismic data to obtain various seismic attribute data volumes;
s3, extracting a correlation between the well side channel seismic attribute and the tuff characterization curve, and sorting according to the correlation;
| seismic attributes | Correlation of | Ordering of |
| 90 DEG phase shift | 0.64 | 1 |
| Relative wave impedance | 0.62 | 2 |
| Instantaneous amplitude | 0.58 | 3 |
| … | … | … |
S4, performing error test according to the attribute ranked at the front to obtain first n seismic attributes with the minimum error;
| number of seismic attributes | Error of | Ordering of |
| 3 | 0.34 | 1 |
| 2 | 0.37 | 2 |
| 5 | 0.41 | 3 |
| … | … | … |
S5, deep learning is carried out according to the first n seismic attributes with the smallest error, and a tuff representation three-dimensional data volume is obtained; analyzing the space position of the tuff on the tuff three-dimensional data body according to the value range of the tuff characterization curve, and extracting the plane distribution map of the tuff according to the top and bottom or the secondary top and bottom of the volcanic mechanism;
preferably, a nonlinear relation is established by using the characterization curve of the tuff and the first 3 seismic attributes with the minimum error, and deep learning is performed by using the nonlinear relation to obtain a three-dimensional data volume for characterizing the tuff;
the principle of the invention is as follows: because the lithology of different volcanic rocks has different elastic parameter characteristics, the different lithology and lithology combination changes are represented by different seismic waveform characteristics, the relationship between the lithology characterization curve and the seismic attribute is established by constructing the lithology characterization curve of the tuff, the deep learning is carried out by optimizing various seismic attributes with high relativity with the lithology characterization curve, and the spatial distribution of the tuff in the volcanic stratum is finally predicted;
according to the method, the correlation between the characteristics of the well-drilled tuff and the well side channel seismic attribute is established, and the deep learning is applied to the whole seismic attribute body calculated by three-dimensional seismic data, so that the three-dimensional data body representing the tuff is obtained.
The invention provides a method for quantitatively predicting the spatial distribution of tuff by utilizing three-dimensional seismic data, which can accurately predict the spatial distribution range of tuff in volcanic strata.
The method has the characteristics of seismic attribute determination, quick realization and accurate identification, is applied to prediction of the space distribution of the tuff in the Songnan area, and has proved to be effective.
According to the method, the relation between the seismic attribute and the lithology characterization curve of the tuff is established for deep learning, the lithology on the well is extrapolated and predicted under the transverse variation trend of the earthquake, the extrapolated and predicted method accords with the transverse variation rule of lithology combination on the seismic reflection and the attribute thereof, and meanwhile, the transverse variation rule of the volcanic can be reflected relatively accurately.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (2)
1. A method for quantitatively predicting the distribution of tuff in a volcanic formation, characterized by: the method comprises the following steps:
s1, analyzing logging response characteristics of different lithologic volcanic rocks in a drilled volcanic stratum, and searching or constructing a characterization curve of the tuff;
s2, performing seismic attribute calculation by utilizing three-dimensional seismic data to obtain various seismic attribute data volumes;
s3, extracting a correlation between the well side channel seismic attribute and the tuff characterization curve, and sorting according to the correlation;
s4, performing error test according to the attribute ranked at the front to obtain first n seismic attributes with the minimum error;
s5, deep learning is carried out according to the first n seismic attributes with the smallest error, and a tuff representation three-dimensional data volume is obtained;
s6, analyzing the space position of the tuff on the tuff characterization three-dimensional data body according to the value range of the tuff characterization curve, and extracting the plane distribution map of the tuff according to the top and bottom or the secondary top and bottom of the volcanic mechanism;
the tuff is characterized by low resistivity on the logging curve, and gamma values are distributed in a certain interval but lower than volcanic lava, so that a lithology distinguishing curve is constructed, and a lithology indicating factor is constructed according to the following formula:
wherein GRJ is a curve of a research area after GR is subjected to area consistency correction, GR is a natural gamma logging curve, and LLD is a resistivity logging curve; b is the threshold value of gamma curve division tuff and lava in the region, and a is the threshold value of resistivity curve division tuff and tuff in the region.
2. A method of quantitatively predicting the distribution of tuff in a volcanic formation according to claim 1 wherein: in step S5, a nonlinear relation is established by using the characterization curve of the tuff and the first 3 seismic attributes with the minimum error, and deep learning is performed by using the nonlinear relation, so that a three-dimensional data body for characterizing the tuff is obtained.
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