CN111399040A - Stacked sand body identification model and method based on seismic attribute negative difference characteristics - Google Patents
Stacked sand body identification model and method based on seismic attribute negative difference characteristics Download PDFInfo
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- CN111399040A CN111399040A CN202010149957.8A CN202010149957A CN111399040A CN 111399040 A CN111399040 A CN 111399040A CN 202010149957 A CN202010149957 A CN 202010149957A CN 111399040 A CN111399040 A CN 111399040A
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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. analysis, for interpretation, for correction
- 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. analysis, for interpretation, for correction
- G01V1/30—Analysis
Abstract
The invention discloses a stacked sand body identification model and method based on seismic attribute negative difference characteristics, wherein the method comprises the following steps: extracting seismic attributes according to the seismic data and the target layer position data, wherein the seismic attributes comprise root mean square amplitude attributes and waveform variation coefficient attributes; searching the maximum value and the minimum value of the seismic attribute, and carrying out 0-1 interval normalization processing on the maximum value and the minimum value; solving a first derivative of the seismic attribute after the normalization processing; establishing a superposed sand body identification model based on the seismic attribute negative difference characteristics; and identifying the type of the sand body of the target stratum according to the superposed sand body identification model by combining the seismic attribute after the first-order derivative is solved and the root-mean-square amplitude attribute after the normalization processing. The method can effectively identify the superposed sand bodies, solve the problem of calculating the effective thickness of the sandstone reservoir and improve the accuracy of seismic interpretation.
Description
Technical Field
The invention relates to the technical field of seismic exploration sand body prediction, in particular to a stacked sand body identification model and method based on seismic attribute negative difference characteristics.
Background
In recent years, with the continuous deepening of seismic exploration, more and more lithologic oil and gas reservoirs are provided, new requirements are provided for the precision of reservoir prediction, and a more accurate sand body prediction method is needed. However, how to accurately identify the stacked sand zones has been an important factor in calculating effective reservoir thickness. At present, no accurate and effective method for identifying superposed sand bodies exists, the method depends on the experience and comprehensive understanding of interpreters to a great extent, simple qualitative analysis is mainly used, human factors are large, a quantitative analysis standard is lacked, the thickness of the sand bodies determines the thickness of a reservoir layer, but the thickness of the reservoir layer in a superposed sand body area is influenced by the thickness of a non-reservoir layer in the longitudinal direction, and meanwhile, the superposed sand bodies are not obvious in characteristics on a seismic section and difficult to identify, so that how to accurately identify the superposed sand body area is very important.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a stacked sand body identification model and a method based on seismic attribute negative difference characteristics.
The technical scheme of the invention is as follows:
on the one hand, the utility model provides a superpose sand body identification model based on seismic attribute negative difference characteristic, superpose sand body identification model specifically is:
in the formula:
u is a superposed sand body identification result;
R′inormalizing the interval of the root mean square amplitude attribute between 0 and 1, and then calculating a first derivative value of a first derivative;
B′inormalizing the waveform variation coefficient attribute in the interval of 0-1, and then calculating a first derivative value of a first derivative;
Rithe root mean square amplitude value is the normalized root mean square amplitude value in the interval 0-1 of the root mean square amplitude attribute.
Preferably, the specific method of the normalization process is as follows: firstly, finding the maximum value and the minimum value of the root-mean-square amplitude attribute and the waveform variation coefficient attribute, and then carrying out 0-1 interval normalization processing on the root-mean-square amplitude attribute and the waveform variation coefficient attribute by adopting the following formula:
in the formula: a. theiFor normalized seismic attributes, amaxAnd aminRespectively maximum and minimum values in the seismic attribute, aiAnd (4) seismic attributes.
On the other hand, the invention also provides a stacked sand body identification method based on the seismic attribute negative difference characteristic, which comprises the following steps:
extracting seismic attributes according to the seismic data and the target layer position data, wherein the seismic attributes comprise root mean square amplitude attributes and waveform variation coefficient attributes;
searching the maximum value and the minimum value of the seismic attribute, and carrying out 0-1 interval normalization processing on the maximum value and the minimum value;
solving a first derivative of the seismic attribute after the normalization processing;
establishing any one of the superposed sand body identification models;
and identifying the type of the sand body of the target stratum according to the superposed sand body identification model by combining the seismic attribute after the first-order derivative is solved and the root-mean-square amplitude attribute after the normalization processing.
Compared with the prior art, the invention has the following advantages:
according to the invention, from the seismic attribute data, the seismic attributes sensitive to the stacked sand body characteristics are mined, a mathematical identification model of the stacked sand body seismic attribute negative difference change characteristics is established, and the stacked sand bodies can be accurately and efficiently identified in a semi-quantitative manner. The current situation that the prior superposed sand bodies depend on the experience and comprehensive understanding of interpreters and lack a quantitative analysis standard is changed. The method is more favorable for quickly identifying the superposed sand bodies of the sandstone reservoir, provides more accurate data for calculating the thickness of the effective reservoir, and can improve the prediction precision of the sandstone reservoir.
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, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic view of stacked sand body identification according to the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples. It should be noted that, in the present application, the embodiments and the technical features of the embodiments may be combined with each other without conflict.
The invention provides a stacked sand body identification method based on seismic attribute negative difference characteristics, which comprises the following steps of:
firstly, extracting seismic attributes according to seismic data and target layer hierarchy data, wherein the seismic attributes comprise root mean square amplitude attributes and waveform variation coefficient attributes.
Secondly, searching the maximum value and the minimum value of the seismic attribute, and carrying out 0-1 interval normalization processing on the seismic attribute, wherein the specific method of the normalization processing is as follows:
in the formula: a. theiFor normalized seismic attributes, amaxAnd aminRespectively maximum and minimum values in the seismic attribute, aiAnd (4) seismic attributes.
Thirdly, solving a first derivative of the seismic attribute after the normalization processing, wherein the formula for solving the first derivative is as follows:
A'i＝(Ai)' (3)
then, establishing a stacked sand body identification model based on the seismic attribute negative difference characteristics, wherein the stacked sand body identification model specifically comprises the following steps:
in the formula:
u is a superposed sand body identification result;
R′inormalizing the interval of the root mean square amplitude attribute between 0 and 1, and then calculating a first derivative value of a first derivative;
B′inormalizing the waveform variation coefficient attribute in the interval of 0-1, and then calculating a first derivative value of a first derivative;
ri is the root mean square amplitude value after normalization processing in the interval 0-1 of the root mean square amplitude attribute.
Finally, according to the superposed sand body identification model in the formula (1), the type of the target layer sand body is identified by combining the seismic attribute after the first derivative is solved and the root-mean-square amplitude attribute after the normalization processing:
when the root mean square amplitude value becomes small (the first derivative is a negative number), the waveform coefficient of variation value becomes large (the first derivative is a positive number), and the root mean square amplitude value is larger than 0.1, the target layer is the superposed sand body;
when the root mean square amplitude value becomes small (the first derivative is a negative number), the waveform coefficient of variation value becomes large (the first derivative is a positive number), and the root mean square amplitude value is less than 0.1, the target layer is separated from the superposed sand body;
when the root mean square amplitude value becomes larger (the first derivative is positive), the waveform coefficient of variation value becomes larger (the first derivative is positive), and the root mean square amplitude value is larger than 0.1, the target layer is a single sand body.
In a specific embodiment, using the present invention to identify stacked sand bodies, exemplified by sand bodies in a zone 1750m to 1890m, the results are shown in FIG. 1. As can be seen from fig. 1, according to the stacked sand body identification method based on the seismic attribute negative difference characteristic, the stacked sand body can be accurately and efficiently identified in a semi-quantitative manner from the seismic attribute data, more accurate data are provided for calculating the thickness of an effective reservoir, and the prediction accuracy of a sandstone reservoir can be improved.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. The utility model provides a superpose sand body identification model based on seismic attribute negative difference characteristic which characterized in that, superpose sand body identification model specifically is:
in the formula:
u is a superposed sand body identification result;
R′inormalizing the interval of the root mean square amplitude attribute between 0 and 1, and then calculating a first derivative value of a first derivative;
B′inormalizing the waveform variation coefficient attribute in the interval of 0-1, and then calculating a first derivative value of a first derivative;
Rithe root mean square amplitude value is the normalized root mean square amplitude value in the interval 0-1 of the root mean square amplitude attribute.
2. The stacked sand body identification model based on seismic attribute negative difference features of claim 1, wherein the specific method of the normalization process is as follows: firstly, finding the maximum value and the minimum value of the root-mean-square amplitude attribute and the waveform variation coefficient attribute, and then carrying out 0-1 interval normalization processing on the root-mean-square amplitude attribute and the waveform variation coefficient attribute by adopting the following formula:
in the formula: a. theiFor normalized seismic attributes, amaxAnd aminRespectively maximum and minimum values in the seismic attribute, aiAnd (4) seismic attributes.
3. A stacked sand body identification method based on seismic attribute negative difference features is characterized by comprising the following steps:
extracting seismic attributes according to the seismic data and the target layer position data, wherein the seismic attributes comprise root mean square amplitude attributes and waveform variation coefficient attributes;
searching the maximum value and the minimum value of the seismic attribute, and carrying out 0-1 interval normalization processing on the maximum value and the minimum value;
solving a first derivative of the seismic attribute after the normalization processing;
establishing the stacked sand body identification model of any one of claims 1 or 2;
and identifying the type of the sand body of the target stratum according to the superposed sand body identification model by combining the seismic attribute after the first-order derivative is solved and the root-mean-square amplitude attribute after the normalization processing.
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