CN112363212B - Method for establishing relation model between plume gas content and seismic attributes and application - Google Patents
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Abstract
The invention discloses a method for establishing a relation model between plume gas content and seismic attributes and application thereof, wherein the method comprises the following steps of S1: establishing a plume model according to the characteristics of bubble-containing seawater, the appearance characteristics of a plume detected by sonar and the distribution characteristics of bubbles; s2: carrying out pre-stack reverse time migration processing on shot gather records of the plume model to obtain a seismic migration profile; s3: extracting seismic attributes from the seismic migration profile; s4: and establishing a relation between the gas content and the seismic attributes, and obtaining a relation model between the plume gas content and the seismic attributes through fitting. The method comprises the steps of fitting different seismic attribute values and gas contents of different layers in the seismic migration profile to obtain a relation model between the plume gas content and the seismic attributes, verifying that the established relation model has small error, applying the relation model to gas content inversion of an actual plume seismic profile, inverting the gas content profile through the actual plume seismic profile, and reflecting the distribution condition of the gas bubble content visually.
Description
Technical Field
The invention relates to the field of natural gas hydrate exploration and development, in particular to a method for establishing a relation model between plume gas content and seismic attributes and application of the relation model.
Background
The natural gas hydrate has important strategic energy significance, and the identification, exploration and development of the hydrate are hot spots of research in the scientific community at present. In recent years, china has made significant progress in hydrate exploration and mining (Wang et al, 2013, hao et al, 2013, lu et al, 2013, zhang, 2010), successfully drilled natural gas hydrate real samples in north of the south sea on day 5/1 of 2007, supposing that natural gas hydrates 27 are present in other regions of the chinese sea area (Yao et al, 2008); in 2008, a hydrate sample was obtained in a permafrost zone in a qilian mountain wood region in Qinghai-Tibet plateau by 11 months (Zhu et al, 2009); in 2017, 5, 18 months, the ministry of homeland resources, long ginger and daming, the first trial production of natural gas hydrates (i.e. combustible ice) in the sea area of china was successful (Wu and Wang, 2018).
Bubble plumes are often found in overburden water in hydrate-bearing areas, which phenomenon has been detected by sonar or seismic means, such as the western ocean floor of the barren ocean (Sauter et al, 2006), the edge hydrate ridge of the Cascadia continental area (Shipboard Scientific Party, 2002), the jojoba ocean (Luan et al, 2012), the gulf of mexico (Sassen et al, 2001), UT-04 seashore (Gong, 2006) and other sea areas (Freire et al, 2011) in the convergent edge Naoetsu basin of the japanese ocean, mud volcanoes of the mediterranean sea (Charlou et al, 2003), and the like. The plume bubbles carry hydrates to be sprayed out of the sea floor to form a sea floor 'flame' phenomenon, and the sea floor plume bubbles overflowing from a sea floor mud volcanic vent are observed to be as high as 1300m in the black sea (Greinert et al, 2006). As can be seen from fig. 1 (Luan et al, 2010), bubbles in the plume during the ascent appear to be feathery (this is also the origin of the "plume" name), vertical, and also inclined due to the influence of ocean currents, the seabed topography (Heeschen et al, 2003, tryon et al, 2002, tryon and tartown, 2004).
The plume is formed due to the formation of a submarine cold spring, a marine geological phenomenon in which gas from a submarine sedimentary formation (or deeper) is injected into the sea in a gushing or leaking manner (Luan et al, 2010 di et al, 2008), and a plume of bubbles can be formed from a large amount of gas gushing into the sea water. Thus, plumes are a direct manifestation of subsea cold spring activity and also direct evidence of subsea gas leaks, and are increasingly valued by scientists (Liu et al, 2015) because, on the one hand, the precipitation of leaky gas hydrates is closely related to subsea cold spring activity and, on the other hand, the plumes of methane bubbles due to subsea cold spring activity are an important factor in marine environments and even global climate change.
Since a distinct wave impedance interface is formed between seawater and bubbles, the acoustic waves scatter as they travel through the seawater and encounter the bubbles (Wu andAki, 1993), and bubble plumes in the water body can be identified by using scattering imaging theory. Currently, identifying plumes is mostly performed by photography and sonar acoustic techniques (Sauter et al, 2006, shipboard Scientific party,2002, luan et al, 2010, sassen et al, 2001), where images obtained by sonar have a higher resolution and plumes are clearly visible, as shown in fig. 1. This is because sonar has a high detection frequency resulting in high resolution (Luan et al, 2010), and conversely, seismic acoustic frequencies and resolutions are relatively low, resulting in seismic imaging that is less sharp than sonar, as shown in fig. 2 (a) (You et al, 2015). But seismic methods also have their own advantages: the seismic exploration area is large, and the cost is low; the problems related to the hydrate can be further searched by researching the seismic response generated by the plume, for example, the gas content of the plume can be inverted according to the seismic response, so that the hydrate reserve of the seabed stratum can be estimated, the hydrate decomposition and migration rule can be searched, and the like.
A seismic migration section (shown in (b) in the attached figure 2) of a certain region of south China sea (You et al, 2015) shows an air chimney, a fracture structure, a typical BSR and a blank zone; FIG. 2 (a) is a seismic migration profile of bubble plume-containing seawater in the same region in (b), and it can be clearly seen that the seismic response generated by the plume is distributed in a vertical strip shape, and shallow bubbles are sparse in deeper layers; meanwhile, cold springs and hydrates also exist on the seabed of the area. Figure 2 shows that the gas chimney and the fracture provide a migration channel to promote the methane to overflow into the seawater, and the decomposition of the natural gas hydrate is the source of the bubble plume, so the plume plays an indirect indication role for the exploration and identification of the natural gas hydrate. This illustrates that seismic methods can also be used to detect bubble plumes in bodies of water, and that for regional exploration, seismic is one of the primary means of detecting plumes. However, a complete method system is not formed for how to process the seismic data to identify the plume; how the seismic response mechanism of the plume is currently internationally uncertain.
Disclosure of Invention
Aiming at the existing problems, the invention aims to provide a method for establishing a relation model between plume gas content and seismic attributes and application thereof, a specific fitting relation between the plume gas content and the seismic attributes is obtained, then the fitting relation is applied to the inversion of an actual plume seismic profile, and the finally obtained plume gas content profile can reflect the distribution condition of the plume gas bubble content more simply and intuitively, so that the distribution rule of the submarine gas bubbles can be deduced, and a foundation is laid for further estimating the plume gas content and the hydrate reserve.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the method for establishing the relation model between the plume gas content and the seismic attributes is characterized by comprising the following steps of,
s1: establishing a plume model according to the characteristics of bubble-containing seawater, the appearance characteristics of a plume detected by sonar and the distribution characteristics of bubbles;
s2: performing pre-stack reverse time migration processing on shot gather records of the plume model established in the step S1 to obtain a seismic migration profile;
s3: extracting seismic attributes from the seismic migration profile;
s4: and (4) fitting the seismic attribute value extracted in the step (S3) with the corresponding gas content to obtain a relation model between plume gas content and seismic attribute.
Further, the specific operation of establishing the plume model in step S1 includes,
s11: calculating the speed of the plume according to a reus average equivalent medium theory;
s12: obtaining the distribution state of the plume bubbles through a random medium theory;
s13: simulating a plume shape by an elliptic function;
s14: and establishing a plurality of plume models with different background gas contents according to the velocity of the plume, the distribution state of the plume bubbles and the shape of the plume.
Further, the plume model comprises two layers, wherein the background of the first layer is seawater, and the middle of the first layer is a semi-elliptical plume; the second layer is a gas-rich sediment layer on the seabed.
Further, the specific operation of step S2 includes,
s21: solving a sound wave equation corresponding to the plume model by adopting a finite difference method to obtain shot gather records of a plurality of plume models with different background gas contents;
s22: and performing prestack reverse time migration processing on shot gather records of the plume models with different background gas contents to obtain seismic migration profiles with different background gas contents.
Further, the seismic attributes extracted in step S3 include root mean square amplitude, average absolute amplitude, and absolute amplitude integral; the extraction formula of the attribute parameters of the root-mean-square amplitude, the average absolute amplitude and the absolute amplitude integral is as follows:
root mean square amplitude
Mean absolute amplitude
Integral of absolute amplitude
In the formula, attn represents the n-th attribute extracted, n 1 ，n 2 Respectively corresponding sampling points at the top and bottom of the time window, Δ t is sampling interval, A (n.Δ t) is instantaneous amplitude, n 2 -n 1 For all the sampling points in the time window, | a (n · Δ t) | is the absolute value of the instantaneous amplitude.
Further, the concrete operation of extracting the seismic attributes in the step S3 includes,
s31: selecting data of three horizons in each seismic migration section as a representative for extracting seismic attributes;
s32: and extracting a small rectangular two-dimensional data volume from each layer, substituting the small rectangular two-dimensional data volume into three seismic attribute parameter extraction formulas to calculate, and obtaining corresponding seismic attribute values.
Further, the specific operation of step S4 includes,
s41: for each horizon, respectively taking the background gas content as an abscissa and the three amplitude attribute values as an ordinate to obtain a fitting relation between the gas content and the three seismic attributes;
s42: verifying a fitting relation between the gas content and the seismic attribute;
s43: and (5) obtaining the overall fitting relation between the gas content and the three seismic attributes, namely a relation model between the plume gas content and the seismic attributes according to the fitting relation between the gas content of the three horizons and the seismic attributes in the step (S41).
Further, the specific operation of step S42 includes,
s421: substituting the three seismic attribute values obtained by the operation in the step S32 into the corresponding fitting relational expression, and inversely calculating the value of the gas content;
s422: comparing the gas content inversely calculated in the step S421 with the background gas content value of the horizon in the model, and calculating the error between the gas content and the background gas content value, wherein the calculation formula of the error is
In the formula, b is the background gas content corresponding to a certain layer, and a is the gas content obtained by using a fitting relational expression.
Further, the application of a relation model between plume gas content and seismic attributes in the inversion of the gas content of an actual plume seismic profile.
Further, the specific operation steps of the application are,
s5: converting the actual plume seismic profile into a seismic attribute profile;
s6: substituting the seismic attribute values of the seismic attribute profiles in the step S5 into a relation model between plume gas content and seismic attributes, and inverting three corresponding plume gas content profiles;
s7: and combining the three plume gas content profiles to obtain the distribution of the gas bubble content.
The beneficial effects of the invention are:
1. the method for establishing the relation model between the plume gas content and the seismic attributes constructs the plume model by utilizing an equivalent medium theory and a random medium theory on the basis of analyzing the characteristics of bubble-containing seawater and the distribution characteristics of actual plume bubbles; in order to analyze the change of the plume gas content on the change of the seismic response, 5 plume models with gradually increasing gas content are established; the method comprises the steps of processing seismic data through pre-stack reverse time migration to obtain a better seismic migration profile, extracting seismic attributes from the seismic migration profile, fitting a relational model fitting relational expression between gas content and the seismic attributes, quantitatively researching the relation between the gas content and the seismic amplitude attributes, and verifying that the error between the gas content obtained through fitting and the actual background gas content of the model is small.
2. The relation model between the gas content and the seismic attributes is applied to the inversion of the gas content of the actual plume seismic profile, the actual plume seismic profile can be inverted into the gas content profile, the distribution condition of the gas bubble content can be reflected visually, and a foundation is laid for further estimating the plume gas content and the hydrate reserve.
Drawings
FIG. 1 is an Orthomson sea cold spring bubble plume image obtained by sonar acoustic technology in the background art of the present invention;
FIG. 2 is a section of bubble plume and seismic migration in a certain survey area of south China sea in the background art of the present invention;
FIG. 3 is a plume model with background gas content of 1% -5% -1% in accordance with the present invention;
FIG. 4 is a plume reverse time seismic migration profile with a background gas content of 10% -15% -10% in accordance with the present invention;
FIG. 5 is a graph of the relationship between plume gas content at the first horizon and seismic attributes in accordance with the present invention;
FIG. 6 is a second horizon plume gas content versus seismic attributes for the present invention;
FIG. 7 is a graph showing the relationship between plume gas content at the third level and seismic attributes in accordance with the present invention;
FIG. 8 is a general fit relationship of plume gas content to seismic attributes in accordance with the present invention;
FIG. 9 is a plume profile of a survey area of the south China sea in the application of the model of the relationship between plume gas content and seismic attributes in accordance with the present invention;
FIG. 10 is a root mean square amplitude seismic attribute profile obtained from conversion of the plume profile of FIG. 9 in accordance with the present invention;
FIG. 11 is a converted average absolute amplitude seismic attribute profile of the plume profile of FIG. 9 of the present invention;
FIG. 12 is a transformed absolute amplitude integrated seismic attribute profile of the plume profile of FIG. 9 in accordance with the present invention;
FIG. 13 is a gas content profile inverted from the root mean square amplitude attribute of FIG. 10 using a model of the relationship between plume gas content and seismic attributes in accordance with the present invention;
FIG. 14 is a gas content profile inverted from the mean absolute amplitude attribute of FIG. 11 using the model of the relationship between plume gas content and seismic attributes in the present invention;
FIG. 15 is a gas content profile inverted from the absolute amplitude integral property of FIG. 12 using the model of the relationship between plume gas content and seismic properties of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the following further describes the technical solution of the present invention with reference to the drawings and the embodiments.
Example (b):
the method for establishing the relation model between the plume gas content and the seismic attributes comprises the following steps,
s1: establishing a plume model according to the characteristics of bubble-containing seawater, the appearance characteristics of a plume detected by sonar and the distribution characteristics of bubbles;
specifically, S11: microscopically, the plume bubbles and the seawater around the plume bubbles belong to a typical gas-liquid two-phase medium, and based on the characteristic, the velocity of the plume is calculated according to a Reuss average equivalent medium theory;
s12: the plume bubbles in the seawater are in a random distribution state in the rising process from the actual plume profile, so that the seawater medium containing bubbles also belongs to a random medium; based on the method, the distribution state of the plume bubbles is obtained through a random medium theory;
s13: the shape of the actual plume is a semi-ellipse, and based on the semi-ellipse, the shape of the plume is simulated by using an ellipse function;
s14: establishing a plurality of plume models with different background gas contents according to the velocity of the plume, the distribution state of the bubbles of the plume and the shape of the plume (the detailed process of establishing a specific plume model adopts the methods in the prior art, such as young, j.c., li, c.p., cheng, l.f., et al, 2015, numerical simulation of conduit stacks based on effective medium thereof; in order to better simulate plume seismic response, a two-layer model is established, wherein the background of the first layer is seawater, and the middle of the first layer is a semi-elliptical plume; the second layer is a gas-rich sediment layer on the seabed;
in the embodiment, 5 plume models with gradually increasing background gas content are established, and the background gas content of the 5 models is respectively 1% -5% -1%, 5% -10% -5%, 10% -15% -10%, 15% -20% -15% and 20% -25% -20%. A model with a background gas content of 1% -5% -1% is extracted as an example, as shown in fig. 3.
Further, step S2: performing pre-stack reverse time migration processing on shot gather records of the plume model established in the step S1 to obtain a seismic migration profile;
specifically, S21: solving a sound wave equation corresponding to the plume model by adopting a finite difference method to obtain shot gather records of a plurality of plume models with different background gas contents;
acquisition parameters for acoustic detection were as follows: the transverse length of the measuring line is 1000m; mesh division is performed by 1 multiplied by 1m; the seismic wavelet dominant frequency is 140Hz; the receiving system is as follows: receiving seismic waves in a full-array manner; fixing a detector, moving shot points from left to right, and setting shot spacing to be 10m, wherein the total number of the shots is 101; the seismic source depth is 0m, the track spacing is 1m, the total number of the tracks is 1000, and the minimum offset distance is 0; the recording length is 1.4s and the sampling rate is 0.2ms. The propagation rule of the sound wave follows a sound wave equation, and the sound wave equation corresponding to the plume model is solved by adopting a finite difference method in the application to obtain shot gather records of a plurality of plume models with different background gas contents.
S22: and (3) performing prestack reverse time migration processing on the shot gather records of the 5 plume models with different background gas contents to obtain 5 seismic migration profiles with the background gas contents.
The plume reverse-time seismic migration profile with the background gas content of 10% -15% -10% obtained by adopting the pre-stack reverse-time migration processing is shown in the attached drawing 4, and compared with the plume profile (shown in the attached drawing 1) and the plume seismic migration profile (shown in the attached drawing 2) (a)) detected by sonar, the plume reverse-time seismic migration processing in the invention has the advantages of better imaging result, higher precision and better boundary convergence effect, and keeps the shape characteristics of the plume, thereby proving that the plume can generate seismic response, and different gas contents can cause corresponding different seismic response changes. Therefore, the bubble plume can be identified through a seismic means, and a foundation is laid for researching and analyzing the corresponding relation between the gas content and the seismic property.
Further, step S3: extracting seismic attributes from the seismic migration profile; .
Specifically, as the plume gas content is sensitive to the amplitude attribute, typical 3 amplitude attributes are selected for extraction in the method, and the 3 seismic amplitude attributes comprise root mean square amplitude, average absolute amplitude and absolute amplitude integral; the extraction formula of the attribute parameters of the root-mean-square amplitude, the average absolute amplitude and the absolute amplitude integral is as follows:
root mean square amplitude
Mean absolute amplitude
Integral of absolute amplitude
In the formula, attn represents the n-th attribute extracted, n 1 ，n 2 Respectively corresponding sampling points at the top and bottom of the time window, Δ t is sampling interval, A (n.Δ t) is instantaneous amplitude, n 2 -n 1 For all sampling points in the time window, | a (n · Δ t) | is the absolute value of the instantaneous amplitude.
When seismic attribute extraction is performed, seismic data need to be read from 5 plume migration profiles shown in fig. 4, each reverse time migration profile in fig. 4 is a two-dimensional data volume of 420 × 1000, the data range belonging to the plume is a color spot area in the diagram, and the remaining green area is seawater.
The specific operations for extracting the seismic attributes include,
s31: because the plume model is distributed in layers, data of three layers in the seismic migration profile are selected as representatives for extracting seismic attributes, and the depths of the three layers are respectively as follows: the distance between 121 and 130m from the sea level is a first layer; the distance 201-210m from the sea level is a second layer; a third layer is arranged at a distance of 291-300m from the sea level, and the transverse ranges of the three layers are 451-550 times;
s32: each layer extracts a small rectangular two-dimensional data volume of 10 multiplied by 100, and the small rectangular two-dimensional data volume is substituted into three seismic attribute parameter extraction formulas to be calculated, so that corresponding seismic attribute values are obtained.
The background gas content for the three horizons of the 5 models is shown in table 1 below.
TABLE 1 background gas content of three horizons of 5 models
Further, step S4: and (4) fitting the seismic attribute value extracted in the step (S3) with the corresponding gas content to obtain a relation model between the plume gas content and the seismic attribute.
Specifically, S41: for the three layers, respectively taking the background gas content as an abscissa and the three amplitude attribute values as an ordinate to obtain a fitting relation between the gas content and the three seismic attributes;
in the three layers, the background gas content of the 5 models is used as an abscissa, and the three amplitude attribute values are used as ordinates, so that a scatter diagram and a trend line between the gas content of the three layers and the seismic attribute are respectively shown in the attached drawings 5, 6 and 7.
As can be seen from fig. 5, 6 and 7, at each horizon, as the gas content increases, the 3 amplitude attribute values increase correspondingly, and show a clear increasing trend, and the linear correlation is better. The reason is that the gas content of the plume is volume content, and the increase of the gas content is equivalent to the increase of the radius of the bubble, so that the scattering generated by the seismic wave is more obvious, and the energy of the scattered wave is increased along with the increase of the radius of the bubble; another reason is that large bubbles break into small bubbles, resulting in increased gas content, and the randomness of the gas-liquid random medium will be more apparent, and thus the scattering by seismic waves will be more apparent, and the energy of the scattered waves will increase.
It can also be seen from fig. 5, 6 and 7 that the linear relationship between the plume gas content and the amplitude attribute of the three horizons is more obvious after fitting. The relationship between the gas content of the three horizons and the 3 amplitude attributes is further obtained from the fitting function, as shown in table 2.
TABLE 2 fitting relationship between gas content and amplitude attribute of three layers
In Table 2, y nm Represents the nth, mth seismic attribute, x nm The gas contents corresponding to the nth and mth seismic attributes are represented, and as can be seen from table 2, fitting relations between the plume gas contents of the three levels and the 3 amplitude attributes are linear relations and are simple linear function relations. The independent variable coefficients of the root-mean-square amplitude and the average absolute amplitude are similar, the intercepts of the first layer and the third layer are similar, and the independent variable coefficients of the absolute amplitude integrals of the three layers are similar.
S42: verifying a fitting relation between the gas content and the seismic attribute;
specifically, S421: substituting the three seismic attribute values obtained by the operation in the step S32 into the corresponding fitting relational expression, and inversely calculating the value of the gas content;
s422: comparing the gas content inversely calculated in the step S421 with the background gas content value of the horizon in the model, and calculating the error between the gas content and the background gas content value, wherein the calculation formula of the error is
In the formula, b is the background gas content corresponding to a certain layer, and a is the gas content obtained by utilizing a fitting relational expression; the resulting error is the result of averaging 5 model-identical horizons, as shown in table 3 below.
TABLE 3 error of gas content from its true value calculated using fitting relation
As can be understood from the error results in table 3, the error values between the background gas contents of the three layers and the gas contents calculated by the fitting formula are smaller, especially for the second layer, so the fitting relationship in table 2 is basically true.
S43: and according to the fitting relational expression between the gas content of the three layers and the seismic attributes in the step S41, obtaining the overall fitting relation between the gas content and the three seismic attributes, namely a relational model between the plume gas content and the seismic attributes.
Specifically, as can be seen from table 2, in the fitting relation between the plume gas content at three layers and 3 amplitude attributes, the independent variable coefficients are very similar, and in order to obtain a general relational formula between the gas content and the amplitude attributes, the gas content at three layers and the 3 amplitude attributes corresponding to the gas content at three layers in table 2 are integrated to obtain an overall fitting relation between the gas content and the 3 amplitude attributes, as shown in fig. 8.
As can be seen from FIG. 8, the model of the overall relationship between plume gas content and 3 seismic attributes is linear. All 3 amplitude attributes showed a clear increasing trend with increasing gas content. Through linear fitting, a comprehensive fitting relational expression of the plume gas content and 3 seismic attributes is obtained, namely a relational model between the plume gas content and the seismic attributes is specifically as follows:
root mean square amplitude versus gas content: y is 1 ＝0.048x 1 +7.99×10 -4 ；
Average absolute amplitude versus gas content: y is 2 ＝0.023x 2 +9.68×10 -4 ；
Absolute amplitude integral versus gas content: y is 3 ＝22.51x 3 +0.968；
In the above expression, y n Representing the nth seismic attribute, x n Representing the gas content corresponding to the nth seismic attribute.
Furthermore, the relation model between the plume gas content and the seismic attributes can be applied to inversion of the gas content of the actual plume seismic section.
Specifically, the specific operation steps of the application are,
s5: converting the actual plume seismic profile into a seismic attribute profile;
FIG. 9 is a section of plume seismic migration in certain measurement area of south China sea, and because the data volume is large, only data of a deep-colored part in the section is selected, which represents that the plume bubbles are dense and have large content. 3868 channels of original data are provided, the sampling interval is 2ms, 1000 channels are provided in the transverse direction, and channels are 2101-3100; taking 600 samples in time, and taking out the data as a two-dimensional array of 600 multiplied by 1000 from 600 to 1800 ms. Then, a 50 × 5 hour window two-dimensional array is selected from the 600 × 1000 two-dimensional arrays from the 1 st row to the 5 th row and from the 1 st row to the 50 th row, and is substituted into the seismic attribute parameter extraction formula in the step S3 to obtain 3 seismic attribute values; then, moving a time window longitudinally, moving one sampling point every time, and obtaining 3 seismic attribute values every time of moving; after the hour window is longitudinally moved to the 600 th row, the hour window is transversely moved to the 2 nd column to the 6 th column, the longitudinal movement is repeated, one sampling point is moved each time, 3 seismic attribute values are still obtained each time, and the process is circulated, and finally, 3 551X 996 two-dimensional seismic attribute sections are obtained, as shown in the attached figures 10-12.
By extracting the amplitude attribute in the range of 600-1800ms in the longitudinal direction and 1801-2800 in the transverse direction in the actual plume profile (fig. 9), 3 seismic attribute profiles (fig. 10-12) are obtained. The distribution of the plume attribute section and the corresponding position of the seismic section is the same as the distribution of the plume attribute section and the seismic section as can be seen from the shades of the colors in the figure: the lower right corner of the section is darker in color, which indicates that the amplitude value is large, the scattering energy is strong, and the plume bubbles are densely distributed.
S6: substituting the seismic attribute values of the seismic attribute profiles in the step S5 into a relation model between the plume gas content and the seismic attributes, and performing inversion to obtain three corresponding plume gas content profiles;
specifically, by using the three seismic attribute profiles (fig. 10 to 12) obtained in step S5, the 3 plume gas content profiles are inverted by substituting the seismic attribute values in the 3 two-dimensional seismic attribute profiles into the model of the relationship between the plume gas content and the seismic attribute, as shown in fig. 13 to 15.
The gas content value range of the 3 profiles obtained from the 3 plume gas content profiles in the attached figures 13-15 is approximately the same and is approximately below 0.4, namely 40%, and the gas content value inverted by the root mean square amplitude attribute is slightly larger and reaches 40%; the gas content, inverted from the mean absolute amplitude and the integral of the absolute amplitude, is close to 0.35 or less, i.e. 35%.
S7: and combining the three plume gas content profiles to obtain the distribution of the gas bubble content.
Comparing the actual plume seismic profile (fig. 9), seismic attribute profile (fig. 10-12) and gas content profile (fig. 13-15), the 3 types of profiles all have the common distribution characteristics, i.e., the lower right corner of the profile is darker in color, larger in amplitude value corresponding to larger attribute value, and correspondingly larger in inverted gas content value (because the gas content and the amplitude attribute are linearly related), which indicates that the plume bubbles are more densely distributed. Therefore, the actual plume seismic profile is inverted into a plume gas content profile, and the distribution condition of the gas content can be reflected visually.
In conclusion, the method for establishing the relation model between the plume gas content and the seismic attributes utilizes the equivalent medium theory and the random medium theory to construct the plume model on the basis of analyzing the characteristics of the bubble-containing seawater and the distribution characteristics of the actual plume bubbles; in order to analyze the change of the plume gas content on the change of the seismic response, 5 plume models with gradually increasing gas content are established; the method comprises the steps of processing seismic data through pre-stack reverse-time migration to obtain a better seismic migration section, extracting seismic attributes from the seismic migration section, fitting a relational model fitting relational expression between gas content and the seismic attributes, quantitatively researching the relation between the gas content and the seismic amplitude attributes, and verifying that the error between the gas content obtained through fitting and the actual background gas content of the model is small.
The relation model between the gas content and the seismic attributes is applied to the inversion of the gas content of the actual plume seismic profile, the actual plume seismic profile can be inverted into the gas content profile, the distribution condition of the gas bubble content can be reflected visually, and a foundation is laid for further estimating the plume gas content and the hydrate reserve.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. The method for establishing the relation model between the plume gas content and the seismic attributes is characterized by comprising the following steps of,
s1: establishing a plume model according to the characteristics of bubble-containing seawater, the appearance characteristics of a plume detected by sonar and the distribution characteristics of bubbles;
s2: performing pre-stack reverse time migration processing on shot gather records of the plume model established in the step S1 to obtain a seismic migration profile;
s3: extracting seismic attributes from the seismic migration profile;
s4: fitting the seismic attribute value extracted in the step S3 with the corresponding gas content to obtain a relation model between the plume gas content and the seismic attribute;
the specific operation of establishing the plume model in step S1 includes,
s11: calculating the velocity of the plume according to a Reuss average equivalent medium theory;
s12: obtaining the distribution state of the plume bubbles through a random medium theory;
s13: simulating a plume shape by an elliptic function;
s14: establishing a plurality of plume models with different background gas contents according to the velocity of the plume, the distribution state of the plume bubbles and the shape of the plume;
the plume model comprises two layers, wherein the background of the first layer is seawater, and the middle part is a semi-elliptical plume; the second layer is a gas-rich deposit on the seabed;
the specific operation of step S2 includes,
s21: solving a sound wave equation corresponding to the plume model by adopting a finite difference method to obtain shot gather records of a plurality of plume models with different background gas contents;
s22: adopting pre-stack reverse time migration processing to shot gather records of a plurality of plume models with different background gas contents to obtain seismic migration profiles with different background gas contents;
the seismic attributes extracted in the step S3 comprise root mean square amplitude, average absolute amplitude and absolute amplitude integral; the extraction formula of the attribute parameters of the root-mean-square amplitude, the average absolute amplitude and the absolute amplitude integral is as follows:
root mean square amplitude
Mean absolute amplitude
Integral of absolute amplitude
In the formula, attn represents the n-th attribute extracted, n 1 ，n 2 Respectively corresponding sampling points at the top and bottom of the time window, Δ t is sampling interval, A (n.Δ t) is instantaneous amplitude, n 2 -n 1 For all sampling points in the time window, | a (n · Δ t) | is the absolute value of the instantaneous amplitude;
the specific operation of seismic attribute extraction in step S3 includes,
s31: selecting data of three horizons in each seismic migration section as a representative for extracting seismic attributes;
s32: extracting a small rectangular two-dimensional data volume from each layer, substituting the data volume into three seismic attribute parameter extraction formulas for calculation, and obtaining corresponding seismic attribute values;
the specific operation of step S4 includes,
s41: for each horizon, respectively taking the background gas content as an abscissa and the three amplitude attribute values as an ordinate to obtain a fitting relation between the gas content and the three seismic attributes;
s42: verifying a fitting relation between the gas content and the seismic attribute;
s43: obtaining a total fitting relation between the gas content and the three seismic attributes according to the fitting relation between the gas content and the seismic attributes of the three horizons in the step S41, namely a relation model between the plume gas content and the seismic attributes; in particular to
Root mean square amplitude versus gas content: y is 1 ＝0.048x 1 +7.99×10 -4 ；
Average absolute amplitude versus gas content: y is 2 ＝0.023x 2 +9.68×10 -4 ；
Absolute amplitude integral versus gas content: y is 3 ＝22.51x 3 +0.968；
In the above expression, y n Representing the nth seismic attribute, x n Representing the gas content corresponding to the nth seismic attribute;
the specific operation of step S42 includes,
s421: substituting the three seismic attribute values obtained by the operation in the step S32 into the corresponding fitting relational expression, and inversely calculating the value of the gas content;
s422: comparing the gas content inversely calculated in the step S421 with the background gas content value of the horizon in the model, and calculating the error between the gas content and the background gas content value, wherein the calculation formula of the error is
In the formula, b is the background gas content corresponding to a certain layer, and a is the gas content obtained by using a fitting relation.
2. Use of a model of the relationship between plume gas content and seismic attributes as claimed in claim 1 in the inversion of actual plume seismic profile gas content.
3. The use of the model of the relationship between plume gas content and seismic attributes in the inversion of actual plume seismic profile gas content as claimed in claim 2, wherein said use is carried out by the steps of,
s5: converting the actual plume seismic profile into a seismic attribute profile;
s6: substituting the seismic attribute values of the seismic attribute profiles in the step S5 into a relation model between plume gas content and seismic attributes, and inverting three corresponding plume gas content profiles;
s7: and combining the three plume gas content profiles to obtain the distribution of the gas bubble content.
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